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Oleksandr Bezdieniezhnykh 8b0dcde893 Merge branch 'stage' into main 2026-04-22 01:40:33 +03:00
Oleksandr Bezdieniezhnykh 4e3b814229 Merge branch 'dev' into stage 2026-04-22 01:37:33 +03:00
1398 changed files with 15387 additions and 253323 deletions
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modernize-*,
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.cmake-format.yaml
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format:
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.cursor/rules/coderule.mdc
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- When you think you are done with changes, run the full test suite. Every failure in tests that cover code you modified or that depend on code you modified is a **blocking gate**. For pre-existing failures in unrelated areas, report them to the user but do not block on them. Never silently ignore or skip a failure without reporting it. On any blocking failure, stop and ask the user to choose one of:
- **Investigate and fix** the failing test or source code
- **Remove the test** if it is obsolete or no longer relevant
- **Iterative-skill exception**: when an iterative loop skill is active (e.g. autodev / `implement/SKILL.md` batch loop, `refactor/SKILL.md` batch loop), the skill governs full-suite cadence — typically focused tests per task/batch and a single full-suite gate at the very end of the implementation phase, NOT after each batch. "Done with changes" means done with the entire implementation phase the skill is running, not done with one batch. Do not run the full suite per batch unless the skill explicitly says to.
- Do not rename any databases or tables or table columns without confirmation. Avoid such renaming if possible.
- Make sure we don't commit binaries, create and keep .gitignore up to date and delete binaries after you are done with the task
.cursor/rules/large-file-writes.mdc
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---
description: "Use chunked writes (Write + StrReplace marker pattern) for large generated files, especially after a monolithic Write fails"
alwaysApply: true
---
# Large File Writes — Chunk on Failure
When a `Write` call to a single file fails (timeout, payload limit, "Invalid arguments", or any tool error) and the intended content is large (>~500 lines or >~50 KB), do NOT retry the same monolithic Write. Switch to chunked writes:
1. **First Write** — create the file with header + table of contents (if applicable) + an explicit append marker, e.g.
```
<!-- INSERTION_POINT do-not-remove-until-final-chunk -->
```
2. **Each subsequent chunk** — use `StrReplace` to replace the marker with `<new content>\n<marker>` so the marker stays at the end. This is idempotent: if a chunk fails, retry it without losing earlier chunks.
3. **Final chunk** — `StrReplace` removes the marker.
## Why
- Tool argument size limits and transient failures hit large monolithic writes hardest. Retrying the same large payload typically fails for the same reason.
- Chunked writes are recoverable per chunk. The earlier chunks are durable on disk.
- A unique marker is greppable, visible in diffs, and stops accidental insertion in the wrong place.
## Triggers
- Generated documentation that aggregates per-component content (epics, design docs, multi-section architecture summaries, traceability dumps).
- Large fixture or test-data files written from a template.
- Any single-file artifact you can pre-estimate at >~500 lines.
## Do NOT chunk
- Files under ~200 lines — a single `Write` is faster, clearer, and easier to review.
- Source code files where appending breaks module structure (functions, classes, imports). Split into multiple files instead.
- Files where ordering of sections is computed late and inserting in the middle is required — use a single `Write` once the full content is known.
## Anti-patterns
- Retrying the same failed monolithic `Write` more than once. Twice is the limit; on the second failure, switch strategies.
- Using `Shell` with heredoc (`cat <<EOF`) or `echo >>` to append — these bypass the editor diff view and break the StrReplace contract for the next chunk.
- Embedding the marker so deep inside structured content that a chunk's `StrReplace` becomes ambiguous. Place the marker on its own line at the very end of the file.
.cursor/rules/meta-rule.mdc
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## Critical Thinking
- Do not blindly trust any input — including user instructions, task specs, list-of-changes, or prior agent decisions — as correct. Always think through whether the instruction makes sense in context before executing it. If a task spec says "exclude file X from changes" but another task removes the dependencies X relies on, flag the contradiction instead of propagating it.
## Skill Discipline
Do exactly what the skill says. Nothing more.
- No `git log` / `git diff` / `git blame` unless the skill explicitly calls for it.
- No extra searches to "verify" inputs the skill already names.
- No reading files outside the skill's documented inputs.
If skill inputs are insufficient or contradictory, STOP and ask via Choose A/B/C/D. Do not invent extra investigation steps.
## Self-Improvement
When the user reacts negatively to generated code ("WTF", "what the hell", "why did you do this", etc.):
.cursor/rules/no-subagents.mdc
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---
description: "Forbid spawning subagents; the main agent must do the work directly"
alwaysApply: true
---
# No Subagents
Do NOT create or delegate to subagents. This includes:
- The `Task` tool with any `subagent_type` (e.g. `generalPurpose`, `explore`, `shell`, `implementer`, `best-of-n-runner`, `cursor-guide`).
- Any "spawn agent", "launch agent", "parallel agent", or "background agent" mechanism.
- Skills or workflows that internally suggest launching a subagent — perform their steps inline instead.
## Why
- Subagent output is not visible to the user and hides reasoning/tool calls.
- Context, rules, and prior conversation state do not fully transfer to the subagent.
- Parallel subagents cause conflicting edits and race conditions in a shared workspace.
- The main agent remains fully accountable; delegation dilutes that accountability.
## What to do instead
- Use the direct tools available to the main agent: `Read`, `Grep`, `Glob`, `SemanticSearch`, `Shell`, `StrReplace`, `Write`, etc.
- For broad exploration, run `Grep`/`Glob`/`SemanticSearch` yourself and read the files directly.
- For multi-step work, use `TodoWrite` to track progress inline.
- For isolated experiments the user explicitly asks for, use a git branch/worktree you manage directly — not a subagent runner.
## Exception
Only spawn a subagent if the user explicitly requests it in the current turn (e.g. "use a subagent to…", "launch an explore agent…"). Even then, confirm once before spawning.
.cursor/rules/response-calibration.mdc
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---
description: "Explanation length and reasoning depth calibration"
alwaysApply: true
---
# Response Calibration
Default to concise. Expand only when the content demands it.
## Length target
- **Default**: a direct answer in ~3–10 lines. Short paragraphs or a tight bullet list.
- **Expand when**: the question involves trade-offs across multiple options, a migration/architectural decision, a security/data-loss risk, or the user explicitly asks for depth ("explain in detail", "walk me through", "why").
- **Shrink when**: the user asks for "shorter", "simpler", "TL;DR", "one line", or similar. Do not re-inflate in later turns unless they ask a new deeper question.
## Completeness floor
Short ≠ incomplete. Every response must still:
- Answer the actual question asked (not a reframed version).
- State the key constraint or reason *once*, not repeatedly.
- Flag a real caveat if one exists (data loss, breaking change, wrong-OS, security). One sentence is enough.
- Not drop a step from an action sequence. If there are 5 steps, list 5 — but without narration between them.
If the honest answer truly needs more space (e.g. trade-off matrix, multi-option decision), write more — but lead with the recommendation or direct answer, then the detail.
## Structure
- One direct sentence first. Then supporting detail.
- Prefer bullets over prose for enumerations, comparisons, or step lists.
- Drop section headers for anything under ~15 lines.
- No "Summary" / "Conclusion" sections unless the response is genuinely long.
## Reasoning depth (internal)
- Match thinking to the problem, not the length of the answer.
- Factual / "where is X used" / single-file edit → minimal thinking, go straight to tools.
- Trade-off / refactor / debugging 3+ hypotheses deep → full thinking budget.
- Do not pad thinking to look thorough. Do not skip thinking on genuinely ambiguous problems to look fast.
## Anti-patterns to avoid
- Restating the question back to the user.
- Multi-paragraph preambles before the answer.
- Exhaustive "alternatives considered" sections when the user didn't ask for alternatives.
- Recapping what was just done at the end of every tool-using turn ("Done. I have edited the file…") — a one-line confirmation is enough.
- Speculative "you might also want to…" paragraphs. Offer follow-ups as a single short sentence, or not at all.
.cursor/rules/skill-building.mdc
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---
description: "Standards for creating and maintaining Cursor skills"
globs: [".cursor/skills/**"]
---
# Skill Building
## When To Create A Skill
- Create a skill for repeatable, bounded workflows that benefit from a reusable process.
- Do not create a skill for a one-off task, vague goal, or workflow that still needs product decisions.
- Start small; evolve the skill when repeated use reveals clearer steps, constraints, or checks.
## Skill Contract
- `SKILL.md` must define a clear `name` and a proactive `description` that explains when the skill should be used.
- State expected inputs, constraints, workflow steps, and final output shape.
- Make trigger conditions explicit enough that the agent can recognize intent without an exact command.
- Base instructions on observable project evidence; do not invite fabrication or unsupported assumptions.
## Keep The Core Lean
- Keep `SKILL.md` concise and under the repo's `.cursor/` size guidance.
- Move detailed standards, examples, and background knowledge into `references/`.
- Put reusable output shapes in `templates/` or other skill-local assets instead of embedding them in the main instructions.
- Keep one primary responsibility per skill; use an orchestrator skill only when multiple existing skills must run in a defined order.
## Deterministic Work
- Use scripts for mechanical steps that are repeatable, parameterized, and safer outside the model's reasoning.
- Scripts must expose explicit inputs, avoid hidden side effects, and fail loudly on errors.
- Do not use scripts to bypass review, hide destructive behavior, or hardcode secrets.
## Quality Proof
- Include realistic examples, checklists, or eval-style scenarios that define what good output looks like.
- Cover common failure cases such as missing sections, leftover placeholders, hallucinated facts, unsafe actions, or malformed output.
- Review skill changes against those checks before treating the skill as ready.
## Security Review
- Treat third-party skills like untrusted code until reviewed.
- Inspect scripts, dependencies, references, secret handling, network calls, and destructive commands before use.
- Prefer local, project-scoped assets and dependencies; document any external dependency the skill requires.
.cursor/rules/tracker.mdc
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@@ -14,14 +14,11 @@ alwaysApply: true
- Issue types: Epic, Story, Task, Bug, Subtask
## Tracker Availability Gate
- If Jira MCP returns **Unauthorized**, **errored**, **connection refused**, **timeout**, a non-2xx status code, an empty body, or any response shape that does not clearly confirm the requested change: **STOP IMMEDIATELY** — no automatic retry, no silent continuation. Surface the full raw error/response to the user verbatim and notify via the Choose A/B/C/D format documented in `.cursor/skills/autodev/protocols.md`.
- A minimal `{"success": true}` body with no echoed issue state is NOT a confirmed transition. When a transition's success matters (status moves, ticket creation, blocking link), follow it with a read-back call (`getJiraIssue` or equivalent) and confirm the new state matches what you asked for. If the read-back disagrees → STOP and ASK.
- Do NOT loop "retry up to N times before asking". One call, one verification. On failure, the user decides whether to retry.
- If Jira MCP returns **Unauthorized**, **errored**, **connection refused**, or any non-success response: **STOP** tracker operations and notify the user via the Choose A/B/C/D format documented in `.cursor/skills/autodev/protocols.md`.
- The user may choose to:
- **Retry the same operation** — once, after the user authorizes it. If it fails again, surface both responses.
- **Retry authentication** — preferred when the failure looks like an auth/credentials problem; the tracker remains the source of truth.
- **Retry authentication** — preferred; the tracker remains the source of truth.
- **Continue in `tracker: local` mode** — only when the user explicitly accepts this option. In that mode all tasks keep numeric prefixes and a `Tracker: pending` marker is written into each task header. The state file records `tracker: local`. The mode is NOT silent — the user has been asked and has acknowledged the trade-off.
- Do NOT auto-fall-back to `tracker: local` without a user decision. Do not pretend a write succeeded. Do not paper over an opaque response by moving on. If the user is unreachable (e.g., non-interactive run), stop and wait.
- Do NOT auto-fall-back to `tracker: local` without a user decision. Do not pretend a write succeeded. If the user is unreachable (e.g., non-interactive run), stop and wait.
- When the tracker becomes available again, any `Tracker: pending` tasks should be synced — this is done at the start of the next `/autodev` invocation via the Leftovers Mechanism below.
## Leftovers Mechanism (non-user-input blockers only)
.cursor/skills/autodev/SKILL.md
+4 -14
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@@ -3,7 +3,7 @@ name: autodev
description: |
Auto-chaining orchestrator that drives the full BUILD-SHIP workflow from problem gathering through deployment.
Detects current project state from _docs/ folder, resumes from where it left off, and flows through
problem → research → plan → test specs → decompose → implement → tests → docs sync → deploy without manual skill invocation.
problem → research → plan → decompose → implement → deploy without manual skill invocation.
Maximizes work per conversation by auto-transitioning between skills.
Trigger phrases:
- "autodev", "auto", "start", "continue"
@@ -52,7 +52,7 @@ Determine which flow to use (check in order — first match wins):
After selecting the flow, apply its detection rules (first match wins) to determine the current step.
**Note**: the meta-repo flow uses a different artifact layout — its source of truth is `_docs/_repo-config.yaml`, not `_docs/NN_*/` folders. After Step 2.5 it also produces `_docs/glossary.md` and a `## Architecture Vision` section in the cross-cutting architecture doc identified by `docs.cross_cutting`. Other detection rules assume the BUILD-SHIP artifact layout; they don't apply to meta-repos.
**Note**: the meta-repo flow uses a different artifact layout — its source of truth is `_docs/_repo-config.yaml`, not `_docs/NN_*/` folders. Other detection rules assume the BUILD-SHIP artifact layout; they don't apply to meta-repos.
## Execution Loop
@@ -67,9 +67,8 @@ B3. Read state — `_docs/_autodev_state.md` (if it exists).
B4. Read File Index — `state.md`, `protocols.md`, and the active flow file.
### Resolve (once per invocation, after Bootstrap)
R1. Reconcile state — verify state file against `_docs/` contents; probe `<workspace-root>/../docs`
(parent suite `docs/` — see `state.md` → "State File Rules" #4); on disagreement,
trust the folders and update the state file (rules: `state.md` → "State File Rules" #4).
R1. Reconcile state — verify state file against `_docs/` contents; on disagreement, trust the folders
and update the state file (rules: `state.md` → "State File Rules" #4).
After this step, `state.step` / `state.status` are authoritative.
R2. Resolve flow — see §Flow Resolution above.
R3. Resolve current step — when a state file exists, `state.step` drives detection.
@@ -113,15 +112,6 @@ Do NOT modify, skip, or abbreviate any part of the sub-skill's workflow. The aut
The state file (`_docs/_autodev_state.md`) is a minimal pointer — only the current step. See `state.md` for the authoritative template, field semantics, update rules, and worked examples. Do not restate the schema here — `state.md` is the single source of truth.
**Conciseness rule (authoritative).** The state file MUST stay short. Acceptable content per field:
- `name` — the step title from the active flow's Step Reference Table. That's it.
- `sub_step.name` — kebab-case identifier from the active sub-skill. That's it.
- `sub_step.detail` — **leave empty (`""`) by default.** Add a one-line note ONLY when the next-session resumer cannot infer where to pick up from `phase` + `name` + on-disk artifacts alone (e.g. `"batch 2 of 4"`, `"blocked on D-PROJ-2 reply"`, `"variant 1b"`). NEVER use `detail` as a changelog, recap, or summary of completed work — those facts belong in the relevant `_docs/` artifact (glossary, traceability matrix, leftovers folder, retro report, etc.) and in git history.
- **Total file size target: <30 lines.** If you're tempted to write more, you're using the wrong artifact — write in `_docs/` instead.
Multi-line `detail` blobs that recap what was just completed are a smell. The state file is a *pointer*, not a logbook.
## Trigger Conditions
This skill activates when the user wants to:
.cursor/skills/autodev/flows/existing-code.md
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@@ -13,7 +13,7 @@ A first-time run executes Phase A then Phase B; every subsequent invocation re-e
| Step | Name | Sub-Skill | Internal SubSteps |
|------|------|-----------|-------------------|
| 1 | Document | document/SKILL.md | Steps 0–7 incl. inline 2.5 (module-layout) and 4.5 (glossary + arch vision) |
| 1 | Document | document/SKILL.md | Steps 1–8 |
| 2 | Architecture Baseline Scan | code-review/SKILL.md (baseline mode) | Phase 1 + Phase 7 |
| 3 | Test Spec | test-spec/SKILL.md | Phases 1–4 |
| 4 | Code Testability Revision | refactor/SKILL.md (guided mode) | Phases 0–7 (conditional) |
@@ -53,8 +53,6 @@ Action: An existing codebase without documentation was detected. Read and execut
The document skill's Step 2.5 produces `_docs/02_document/module-layout.md`, which is required by every downstream step that assigns file ownership (`/implement` Step 4, `/code-review` Phase 7, `/refactor` discovery). If this file is missing after Step 1 completes (e.g., a pre-existing `_docs/` dir predates the 2.5 addition), re-invoke `/document` in resume mode — it will pick up at Step 2.5.
The document skill's Step 4.5 produces `_docs/02_document/glossary.md` and prepends a confirmed `## Architecture Vision` section to `architecture.md`. Both are user-confirmed artifacts; downstream skills (refactor, decompose, new-task) treat them as authoritative for terminology and structural intent. If `glossary.md` is missing after Step 1 (pre-existing `_docs/` dir from before the 4.5 addition), re-invoke `/document` in resume mode — it will pick up at Step 4.5 without redoing module/component analysis.
---
**Step 2 — Architecture Baseline Scan**
@@ -152,17 +150,15 @@ If `_docs/02_tasks/` subfolders have some task files already (e.g., refactoring
---
**Step 6 — Implement Tests**
Condition (folder fallback): `_docs/02_tasks/todo/` contains test task files AND `_dependencies_table.md` exists AND `_docs/03_implementation/implementation_report_tests.md` does not exist.
Condition (folder fallback): `_docs/02_tasks/todo/` contains task files AND `_dependencies_table.md` exists AND `_docs/03_implementation/implementation_report_tests.md` does not exist.
State-driven: reached by auto-chain from Step 5.
Action: Invoke `.cursor/skills/implement/SKILL.md` with task selection context **Test implementation**.
Action: Read and execute `.cursor/skills/implement/SKILL.md`
The implement skill reads only test tasks from `_docs/02_tasks/todo/` and implements them.
The implement skill reads test tasks from `_docs/02_tasks/todo/` and implements them.
If `_docs/03_implementation/` has batch reports, the implement skill detects completed tasks and continues.
For folder fallback, **test task files** means `*_test_infrastructure.md` plus task specs whose `**Component**` or `**Epic**` identifies `Blackbox Tests`.
---
**Step 7 — Run Tests**
.cursor/skills/autodev/flows/greenfield.md
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@@ -1,6 +1,6 @@
# Greenfield Workflow
Workflow for new projects built from scratch. Flows linearly: Problem → Research → Plan → UI Design (if applicable) → Test Spec → Decompose → Implement + Product Completeness Gate → Code Testability Revision → Decompose Tests → Implement Tests → Run Tests → Test-Spec Sync → Update Docs → Security Audit (optional) → Performance Test (optional) → Deploy → Retrospective.
Workflow for new projects built from scratch. Flows linearly: Problem → Research → Plan → UI Design (if applicable) → Decompose → Implement → Run Tests → Security Audit (optional) → Performance Test (optional) → Deploy → Retrospective.
## Step Reference Table
@@ -10,19 +10,13 @@ Workflow for new projects built from scratch. Flows linearly: Problem → Resear
| 2 | Research | research/SKILL.md | Mode A: Phase 1–4 · Mode B: Step 0–8 |
| 3 | Plan | plan/SKILL.md | Step 1–6 + Final |
| 4 | UI Design | ui-design/SKILL.md | Phase 0–8 (conditional — UI projects only) |
| 5 | Test Spec | test-spec/SKILL.md | Phases 1–4 |
| 6 | Decompose | decompose/SKILL.md (implementation task decomposition) | Step 1 + Step 1.5 + Step 2 + Step 4 |
| 7 | Implement | implement/SKILL.md | Batch loop + Product Implementation Completeness Gate |
| 8 | Code Testability Revision | refactor/SKILL.md (guided mode) | Phases 0–7 (conditional) |
| 9 | Decompose Tests | decompose/SKILL.md (tests-only) | Step 1t + Step 3 + Step 4 |
| 10 | Implement Tests | implement/SKILL.md | (batch-driven, no fixed sub-steps) |
| 11 | Run Tests | test-run/SKILL.md | Steps 1–4 |
| 12 | Test-Spec Sync | test-spec/SKILL.md (cycle-update mode) | Phase 2 + Phase 3 (scoped) |
| 13 | Update Docs | document/SKILL.md (task mode) | Task Steps 0–5 |
| 14 | Security Audit | security/SKILL.md | Phase 1–5 (optional) |
| 15 | Performance Test | test-run/SKILL.md (perf mode) | Steps 1–5 (optional) |
| 16 | Deploy | deploy/SKILL.md | Step 1–7 |
| 17 | Retrospective | retrospective/SKILL.md (cycle-end mode) | Steps 1–4 |
| 5 | Decompose | decompose/SKILL.md | Step 1–4 |
| 6 | Implement | implement/SKILL.md | (batch-driven, no fixed sub-steps) |
| 7 | Run Tests | test-run/SKILL.md | Steps 1–4 |
| 8 | Security Audit | security/SKILL.md | Phase 1–5 (optional) |
| 9 | Performance Test | test-run/SKILL.md (perf mode) | Steps 1–5 (optional) |
| 10 | Deploy | deploy/SKILL.md | Step 1–7 |
| 11 | Retrospective | retrospective/SKILL.md (cycle-end mode) | Steps 1–4 |
## Detection Rules
@@ -86,12 +80,12 @@ If `_docs/02_document/` exists but is incomplete (has some artifacts but no `FIN
---
**Step 4 — UI Design (conditional)**
Condition (folder fallback): `_docs/02_document/architecture.md` exists AND `_docs/02_document/tests/traceability-matrix.md` does not exist.
Condition (folder fallback): `_docs/02_document/architecture.md` exists AND `_docs/02_tasks/todo/` does not exist or has no task files.
State-driven: reached by auto-chain from Step 3.
Action: Read and execute `.cursor/skills/ui-design/SKILL.md`. The skill runs its own **Applicability Check**, which handles UI project detection and the user's A/B choice. It returns one of:
- `outcome: completed` → mark Step 4 as `completed`, auto-chain to Step 5 (Test Spec).
- `outcome: completed` → mark Step 4 as `completed`, auto-chain to Step 5 (Decompose).
- `outcome: skipped, reason: not-a-ui-project` → mark Step 4 as `skipped`, auto-chain to Step 5.
- `outcome: skipped, reason: user-declined` → mark Step 4 as `skipped`, auto-chain to Step 5.
@@ -99,162 +93,34 @@ The autodev no longer inlines UI detection heuristics — they live in `ui-desig
---
**Step 5 — Test Spec**
Condition (folder fallback): `_docs/02_document/FINAL_report.md` exists AND `_docs/02_document/architecture.md` exists AND `_docs/02_document/tests/traceability-matrix.md` does not exist.
State-driven: reached by auto-chain from Step 4 (completed or skipped).
**Step 5 — Decompose**
Condition: `_docs/02_document/` contains `architecture.md` AND `_docs/02_document/components/` has at least one component AND `_docs/02_tasks/todo/` does not exist or has no task files
Action: Read and execute `.cursor/skills/test-spec/SKILL.md`.
This step converts the greenfield problem statement, acceptance criteria, solution, architecture, component docs, and UI design artifacts (if any) into test specifications before implementation begins. The test spec should cover unit, integration, blackbox, and e2e scenarios where those levels are applicable to the project.
---
**Step 6 — Decompose**
Condition: `_docs/02_document/` contains `architecture.md` AND `_docs/02_document/components/` has at least one component AND `_docs/02_document/tests/traceability-matrix.md` exists AND `_docs/02_tasks/todo/` does not exist or has no implementation task files.
Action: Invoke `.cursor/skills/decompose/SKILL.md` for **implementation task decomposition**. The greenfield flow selects the implementation entrypoint before handing off: Bootstrap Structure, Module Layout, Component Task Decomposition, and Cross-Task Verification.
Do not invoke Blackbox Test Task Decomposition from Step 6. Test tasks are intentionally deferred to Step 9 (Decompose Tests) so the first implementation batch stays focused on product functionality and Step 8 can revise testability before test task files exist.
Action: Read and execute `.cursor/skills/decompose/SKILL.md`
If `_docs/02_tasks/` subfolders have some task files already, the decompose skill's resumability handles it.
---
**Step 7 — Implement**
Condition: `_docs/02_tasks/todo/` contains implementation task files AND `_dependencies_table.md` exists AND `_docs/03_implementation/` does not contain a valid product implementation report.
**Step 6 — Implement**
Condition: `_docs/02_tasks/todo/` contains task files AND `_dependencies_table.md` exists AND `_docs/03_implementation/` does not contain any `implementation_report_*.md` file
Action: Invoke `.cursor/skills/implement/SKILL.md` with task selection context **Product implementation**.
The implement skill must run its **Product Implementation Completeness Gate** before it writes any final product implementation report. This gate compares completed product task specs, architecture/component promises, and actual source code so scaffold-only implementations cannot advance to Step 8. A final product implementation report without `_docs/03_implementation/implementation_completeness_cycle[N]_report.md` is incomplete and must not be treated as Step 7 completion.
Action: Read and execute `.cursor/skills/implement/SKILL.md`
If `_docs/03_implementation/` has batch reports, the implement skill detects completed tasks and continues. The FINAL report filename is context-dependent — see implement skill documentation for naming convention.
For folder fallback, **implementation task files** means task specs that are not test-only specs: exclude `*_test_infrastructure.md` and task specs whose `**Component**` or `**Epic**` identifies `Blackbox Tests`.
For folder fallback, a **product implementation report** is any `_docs/03_implementation/implementation_report_*.md` file except `_docs/03_implementation/implementation_report_tests.md` and refactor reports. It is valid for greenfield progression only when:
- the matching `_docs/03_implementation/implementation_completeness_cycle[N]_report.md` exists,
- that completeness report does not contain unresolved `FAIL` classifications, and
- `_docs/02_tasks/todo/` contains no pending implementation task files.
If a product report exists but any of those validity checks fail, treat product implementation as incomplete and stay in Step 7.
---
**Step 8 — Code Testability Revision**
Condition (folder fallback): `_docs/03_implementation/` contains a valid product implementation report, `_docs/03_implementation/implementation_completeness_cycle[N]_report.md` exists without unresolved `FAIL` classifications, `_docs/04_refactoring/01-testability-refactoring/testability_assessment.md` does not exist, `_docs/04_refactoring/01-testability-refactoring/testability_changes_summary.md` does not exist, `_docs/03_implementation/implementation_report_tests.md` does not exist, and `_docs/02_tasks/todo/` does not contain test task files.
State-driven: reached by auto-chain from Step 7.
**Purpose**: verify the newly built code can be exercised by the planned tests before writing the test suite. Greenfield code should be testable by design; this step catches accidental hardcoded paths, singletons, direct external service construction, or other implementation choices that would make meaningful tests impossible.
**Scope — MINIMAL, SURGICAL fixes**: this is not a general refactor. It is the smallest set of changes required to make the implemented code runnable under tests.
**Allowed changes** in this phase:
- Replace hardcoded URLs / file paths / credentials / magic numbers with env vars or constructor arguments.
- Extract narrow interfaces for components that need stubbing in tests.
- Add optional constructor parameters for dependency injection; default to the existing behavior so callers do not break.
- Wrap global singletons in thin accessors that tests can override.
- Split a function ONLY when necessary to stub one of its collaborators — do not split for clarity alone.
**NOT allowed** in this phase (defer to a later refactor task):
- Renaming public APIs.
- Moving code between files unless strictly required for isolation.
- Changing algorithms or business logic.
- Restructuring module boundaries or rewriting layers.
Action: Analyze the codebase against the test specs to determine whether the code can be tested as-is.
1. Read `_docs/02_document/tests/traceability-matrix.md` and all test scenario files in `_docs/02_document/tests/`.
2. For each test scenario, check whether the code under test can be exercised in isolation. Look for:
- Hardcoded file paths or directory references
- Hardcoded configuration values (URLs, credentials, magic numbers)
- Global mutable state that cannot be overridden
- Tight coupling to external services without abstraction
- Missing dependency injection or non-configurable parameters
- Direct file system operations without path configurability
- Inline construction of heavy dependencies (models, clients)
3. If ALL scenarios are testable as-is:
- Create `_docs/04_refactoring/01-testability-refactoring/`
- Write `_docs/04_refactoring/01-testability-refactoring/testability_assessment.md` with the scenarios reviewed and outcome "Code is testable — no changes needed"
- Mark Step 8 as `completed` with outcome "Code is testable — no changes needed"
- Auto-chain to Step 9 (Decompose Tests)
4. If testability issues are found:
- Create `_docs/04_refactoring/01-testability-refactoring/`
- Write `list-of-changes.md` in that directory using the refactor skill template (`.cursor/skills/refactor/templates/list-of-changes.md`), with:
- **Mode**: `guided`
- **Source**: `autodev-greenfield-testability-analysis`
- One change entry per testability issue found (change ID, file paths, problem, proposed change, risk, dependencies). Each entry must fit the allowed-changes list above; reject entries that drift into full refactor territory and log them under "Deferred refactor candidates" instead.
- Invoke the refactor skill in **guided mode**: read and execute `.cursor/skills/refactor/SKILL.md` with the `list-of-changes.md` as input
- Phase 3 (Safety Net) is skipped for this testability run because the test suite has not been implemented yet
- After execution, surface `RUN_DIR/testability_changes_summary.md` to the user via the Choose format (accept / request follow-up) before auto-chaining
- Copy or save the accepted summary as `_docs/04_refactoring/01-testability-refactoring/testability_changes_summary.md` so folder fallback can detect Step 8 completion
- Mark Step 8 as `completed`
- Auto-chain to Step 9 (Decompose Tests)
---
**Step 9 — Decompose Tests**
Condition (folder fallback): `_docs/02_document/tests/traceability-matrix.md` exists AND workspace contains source code files AND `_docs/03_implementation/` contains a valid product implementation report AND `_docs/03_implementation/implementation_completeness_cycle[N]_report.md` exists without unresolved `FAIL` classifications AND (`_docs/04_refactoring/01-testability-refactoring/testability_assessment.md` exists OR `_docs/04_refactoring/01-testability-refactoring/testability_changes_summary.md` exists) AND (`_docs/02_tasks/todo/` does not exist or has no test task files) AND `_docs/03_implementation/implementation_report_tests.md` does not exist.
State-driven: reached by auto-chain from Step 8.
Action: Read and execute `.cursor/skills/decompose/SKILL.md` in **tests-only mode** (pass `_docs/02_document/tests/` as input). The decompose skill will:
1. Run Step 1t (test infrastructure bootstrap)
2. Run Step 3 (blackbox/e2e-capable test task decomposition)
3. Run Step 4 (cross-verification against test coverage)
If `_docs/02_tasks/` subfolders have some task files already, the decompose skill's resumability handles it — it appends test tasks alongside existing completed implementation tasks.
---
**Step 10 — Implement Tests**
Condition (folder fallback): `_docs/02_tasks/todo/` contains test task files AND `_dependencies_table.md` exists AND `_docs/03_implementation/implementation_report_tests.md` does not exist.
State-driven: reached by auto-chain from Step 9.
Action: Invoke `.cursor/skills/implement/SKILL.md` with task selection context **Test implementation**.
The implement skill reads only test tasks from `_docs/02_tasks/todo/` and implements them.
If `_docs/03_implementation/` has batch reports, the implement skill detects completed test tasks and continues.
For folder fallback, **test task files** means `*_test_infrastructure.md` plus task specs whose `**Component**` or `**Epic**` identifies `Blackbox Tests`.
---
**Step 11 — Run Tests**
Condition (folder fallback): `_docs/03_implementation/implementation_report_tests.md` exists.
State-driven: reached by auto-chain from Step 10.
**Step 7 — Run Tests**
Condition (folder fallback): `_docs/03_implementation/` contains an `implementation_report_*.md` file.
State-driven: reached by auto-chain from Step 6.
Action: Read and execute `.cursor/skills/test-run/SKILL.md`
Verifies the implemented unit, integration, blackbox, and e2e tests pass before proceeding to spec and documentation sync. This is a hard product gate, not a harness-smoke gate: e2e/blackbox tests must exercise the actual implemented system through public runtime boundaries and compare actual outputs against `_docs/00_problem/input_data/expected_results/results_report.md` or referenced machine-readable expected-result files. Stubs are allowed only for external systems outside the product boundary; missing internal product implementation must fail or block the gate and send the flow back to Implement.
---
**Step 12 — Test-Spec Sync**
State-driven: reached by auto-chain from Step 11. Requires `_docs/02_document/tests/traceability-matrix.md` to exist — if missing, mark Step 12 `skipped` (see Action below).
Action: Read and execute `.cursor/skills/test-spec/SKILL.md` in **cycle-update mode**. Pass the completed implementation task specs, completed test task specs, and implementation reports as inputs.
The skill appends implementation-learned acceptance criteria, scenarios, and NFR updates to the existing test-spec files without rewriting unaffected sections. If `traceability-matrix.md` is missing, mark Step 12 as `skipped` — the next `/test-spec` full run will regenerate it.
After completion, auto-chain to Step 13 (Update Docs).
---
**Step 13 — Update Docs**
State-driven: reached by auto-chain from Step 12 (completed or skipped). Requires `_docs/02_document/` to contain existing documentation — if missing, mark Step 13 `skipped` (see Action below).
Action: Read and execute `.cursor/skills/document/SKILL.md` in **Task mode**. Pass all completed implementation and test task spec files plus the implementation reports.
The document skill in Task mode updates affected module docs, component docs, system-level docs, and test documentation without redoing full discovery, verification, or problem extraction.
If `_docs/02_document/` does not contain existing docs, mark Step 13 as `skipped`.
After completion, auto-chain to Step 14 (Security Audit).
---
**Step 14 — Security Audit (optional)**
State-driven: reached by auto-chain from Step 13 (completed or skipped).
**Step 8 — Security Audit (optional)**
State-driven: reached by auto-chain from Step 7.
Action: Apply the **Optional Skill Gate** (`protocols.md` → "Optional Skill Gate") with:
- question: `Run security audit before deploy?`
@@ -262,12 +128,12 @@ Action: Apply the **Optional Skill Gate** (`protocols.md` → "Optional Skill Ga
- option-b-label: `Skip — proceed directly to deploy`
- recommendation: `A — catches vulnerabilities before production`
- target-skill: `.cursor/skills/security/SKILL.md`
- next-step: Step 15 (Performance Test)
- next-step: Step 9 (Performance Test)
---
**Step 15 — Performance Test (optional)**
State-driven: reached by auto-chain from Step 14 (completed or skipped).
**Step 9 — Performance Test (optional)**
State-driven: reached by auto-chain from Step 8.
Action: Apply the **Optional Skill Gate** (`protocols.md` → "Optional Skill Gate") with:
- question: `Run performance/load tests before deploy?`
@@ -275,30 +141,30 @@ Action: Apply the **Optional Skill Gate** (`protocols.md` → "Optional Skill Ga
- option-b-label: `Skip — proceed directly to deploy`
- recommendation: `A or B — base on whether acceptance criteria include latency, throughput, or load requirements`
- target-skill: `.cursor/skills/test-run/SKILL.md` in **perf mode** (the skill handles runner detection, threshold comparison, and its own A/B/C gate on threshold failures)
- next-step: Step 16 (Deploy)
- next-step: Step 10 (Deploy)
---
**Step 16 — Deploy**
State-driven: reached by auto-chain from Step 15 (after Step 15 is completed or skipped).
**Step 10 — Deploy**
State-driven: reached by auto-chain from Step 9 (after Step 9 is completed or skipped).
Action: Read and execute `.cursor/skills/deploy/SKILL.md`.
After the deploy skill completes successfully, mark Step 16 as `completed` and auto-chain to Step 17 (Retrospective).
After the deploy skill completes successfully, mark Step 10 as `completed` and auto-chain to Step 11 (Retrospective).
---
**Step 17 — Retrospective**
State-driven: reached by auto-chain from Step 16.
**Step 11 — Retrospective**
State-driven: reached by auto-chain from Step 10.
Action: Read and execute `.cursor/skills/retrospective/SKILL.md` in **cycle-end mode**. This closes the cycle's feedback loop by folding metrics into `_docs/06_metrics/retro_<date>.md` and appending the top-3 lessons to `_docs/LESSONS.md`.
After retrospective completes, mark Step 17 as `completed` and enter "Done" evaluation.
After retrospective completes, mark Step 11 as `completed` and enter "Done" evaluation.
---
**Done**
State-driven: reached by auto-chain from Step 17. (Sanity check: `_docs/04_deploy/` should contain all expected artifacts — containerization.md, ci_cd_pipeline.md, environment_strategy.md, observability.md, deployment_procedures.md, deploy_scripts.md.)
State-driven: reached by auto-chain from Step 11. (Sanity check: `_docs/04_deploy/` should contain all expected artifacts — containerization.md, ci_cd_pipeline.md, environment_strategy.md, observability.md, deployment_procedures.md, deploy_scripts.md.)
Action: Report project completion with summary. Then **rewrite the state file** so the next `/autodev` invocation enters the feature-cycle loop in the existing-code flow:
@@ -325,65 +191,47 @@ On the next invocation, Flow Resolution rule 1 reads `flow: existing-code` and r
| Research (2) | Auto-chain → Research Decision (ask user: another round or proceed?) |
| Research Decision → proceed | Auto-chain → Plan (3) |
| Plan (3) | Auto-chain → UI Design detection (4) |
| UI Design (4, done or skipped) | Auto-chain → Test Spec (5) |
| Test Spec (5) | Auto-chain → Decompose (6) |
| Decompose (6) | **Session boundary** — suggest new conversation before Implement |
| Implement (7) | Auto-chain only after Product Implementation Completeness Gate passes → Code Testability Revision (8) |
| Code Testability Revision (8) | Auto-chain → Decompose Tests (9) |
| Decompose Tests (9) | **Session boundary** — suggest new conversation before Implement Tests |
| Implement Tests (10) | Auto-chain → Run Tests (11) |
| Run Tests (11, all pass) | Auto-chain → Test-Spec Sync (12) |
| Test-Spec Sync (12, done or skipped) | Auto-chain → Update Docs (13) |
| Update Docs (13, done or skipped) | Auto-chain → Security Audit choice (14) |
| Security Audit (14, done or skipped) | Auto-chain → Performance Test choice (15) |
| Performance Test (15, done or skipped) | Auto-chain → Deploy (16) |
| Deploy (16) | Auto-chain → Retrospective (17) |
| Retrospective (17) | Report completion; rewrite state to existing-code flow, step 9 |
| UI Design (4, done or skipped) | Auto-chain → Decompose (5) |
| Decompose (5) | **Session boundary** — suggest new conversation before Implement |
| Implement (6) | Auto-chain → Run Tests (7) |
| Run Tests (7, all pass) | Auto-chain → Security Audit choice (8) |
| Security Audit (8, done or skipped) | Auto-chain → Performance Test choice (9) |
| Performance Test (9, done or skipped) | Auto-chain → Deploy (10) |
| Deploy (10) | Auto-chain → Retrospective (11) |
| Retrospective (11) | Report completion; rewrite state to existing-code flow, step 9 |
## Status Summary — Step List
Flow name: `greenfield`. Render using the banner template in `protocols.md` → "Banner Template (authoritative)". No header-suffix, current-suffix, or footer-extras — all empty for this flow.
| # | Step Name | Extra state tokens (beyond the shared set) |
|---|-----------------------------|--------------------------------------------|
| 1 | Problem | — |
| 2 | Research | `DONE (N drafts)` |
| 3 | Plan | — |
| 4 | UI Design | — |
| 5 | Test Spec | — |
| 6 | Decompose | `DONE (N tasks)` |
| 7 | Implement | `IN PROGRESS (batch M of ~N)` |
| 8 | Code Testability Revision | — |
| 9 | Decompose Tests | `DONE (N tasks)` |
| 10 | Implement Tests | `IN PROGRESS (batch M)` |
| 11 | Run Tests | `DONE (N passed, M failed)` |
| 12 | Test-Spec Sync | — |
| 13 | Update Docs | — |
| 14 | Security Audit | — |
| 15 | Performance Test | — |
| 16 | Deploy | — |
| 17 | Retrospective | — |
| # | Step Name | Extra state tokens (beyond the shared set) |
|---|--------------------|--------------------------------------------|
| 1 | Problem | — |
| 2 | Research | `DONE (N drafts)` |
| 3 | Plan | — |
| 4 | UI Design | — |
| 5 | Decompose | `DONE (N tasks)` |
| 6 | Implement | `IN PROGRESS (batch M of ~N)` |
| 7 | Run Tests | `DONE (N passed, M failed)` |
| 8 | Security Audit | — |
| 9 | Performance Test | — |
| 10 | Deploy | — |
| 11 | Retrospective | — |
All rows also accept the shared state tokens (`DONE`, `IN PROGRESS`, `NOT STARTED`, `FAILED (retry N/3)`); rows 4, 12, 13, 14, 15 additionally accept `SKIPPED`.
All rows also accept the shared state tokens (`DONE`, `IN PROGRESS`, `NOT STARTED`, `FAILED (retry N/3)`); rows 4, 8, 9 additionally accept `SKIPPED`.
Row rendering format (step-number column is right-padded to 2 characters for alignment):
```
Step 1 Problem [<state token>]
Step 2 Research [<state token>]
Step 3 Plan [<state token>]
Step 4 UI Design [<state token>]
Step 5 Test Spec [<state token>]
Step 6 Decompose [<state token>]
Step 7 Implement [<state token>]
Step 8 Code Testability Rev. [<state token>]
Step 9 Decompose Tests [<state token>]
Step 10 Implement Tests [<state token>]
Step 11 Run Tests [<state token>]
Step 12 Test-Spec Sync [<state token>]
Step 13 Update Docs [<state token>]
Step 14 Security Audit [<state token>]
Step 15 Performance Test [<state token>]
Step 16 Deploy [<state token>]
Step 17 Retrospective [<state token>]
Step 1 Problem [<state token>]
Step 2 Research [<state token>]
Step 3 Plan [<state token>]
Step 4 UI Design [<state token>]
Step 5 Decompose [<state token>]
Step 6 Implement [<state token>]
Step 7 Run Tests [<state token>]
Step 8 Security Audit [<state token>]
Step 9 Performance Test [<state token>]
Step 10 Deploy [<state token>]
Step 11 Retrospective [<state token>]
```
.cursor/skills/autodev/flows/meta-repo.md
+32 -314
View File
@@ -5,8 +5,7 @@ Workflow for **meta-repositories** — repos that aggregate multiple components
This flow differs fundamentally from `greenfield` and `existing-code`:
- **No problem/research/plan phases** — meta-repos don't build features, they coordinate existing ones
- **No test spec / run tests** — the meta-repo has no code to test
- **`implement` is scoped to suite-level work only** — cross-repo concerns, repo/folder renames, suite-root infra additions (e.g., `.gitmodules`, `_infra/`, suite `e2e/`). Per-component implementation lives in each component's own workspace `/autodev` cycle. The meta-repo's implement step (Step 3.5) executes only when `_docs/tasks/todo/` is non-empty AND the user explicitly opts in; placement is **before** the sync skills so subsequent Doc/E2E/CICD sync propagates the post-implementation state.
- **No test spec / implement / run tests** — the meta-repo has no code to test
- **No `_docs/00_problem/` artifacts** — documentation target is `_docs/*.md` unified docs, not per-feature `_docs/NN_feature/` folders
- **Primary artifact is `_docs/_repo-config.yaml`** — generated by `monorepo-discover`, read by every other step
@@ -16,11 +15,8 @@ This flow differs fundamentally from `greenfield` and `existing-code`:
|------|------|-----------|-------------------|
| 1 | Discover | monorepo-discover/SKILL.md | Phase 1–10 |
| 2 | Config Review | (human checkpoint, no sub-skill) | — |
| 2.5 | Glossary & Architecture Vision | (inline, no sub-skill) | Steps 1–5 |
| 3 | Status | monorepo-status/SKILL.md | Sections 1–5 |
| 3.5 | Suite Implement | implement/SKILL.md (suite-level invocation context) | Steps 1–14 + 16 (Step 14.5 + Step 15 skipped); conditional on `_docs/tasks/todo/` non-empty AND user opt-in |
| 4 | Document Sync | monorepo-document/SKILL.md | Phase 1–7 (conditional on doc drift) |
| 4.5 | Integration Test Sync | monorepo-e2e/SKILL.md | Phase 1–6 (conditional on suite-e2e drift; skipped if `suite_e2e:` block absent in config) |
| 5 | CICD Sync | monorepo-cicd/SKILL.md | Phase 1–7 (conditional on CI drift) |
| 6 | Loop | (auto-return to Step 3 on next invocation) | — |
@@ -62,121 +58,17 @@ Action: This is a **hard session boundary**. The skill cannot proceed until a hu
══════════════════════════════════════
```
- If user picks A → verify `confirmed_by_user: true` is now set in the config. If still `false`, re-ask. If true, auto-chain to **Step 2.5 (Glossary & Architecture Vision)**.
- If user picks A → verify `confirmed_by_user: true` is now set in the config. If still `false`, re-ask. If true, auto-chain to **Step 3 (Status)**.
- If user picks B → mark Step 2 as `in_progress`, update state file, end the session. Tell the user to invoke `/autodev` again after reviewing.
**Do NOT auto-flip `confirmed_by_user`.** Only the human does that.
---
**Step 2.5 — Glossary & Architecture Vision** (one-shot)
Condition (folder fallback): `_docs/_repo-config.yaml` exists AND `confirmed_by_user: true` AND (`_docs/glossary.md` does NOT exist OR the cross-cutting architecture doc identified in `docs.cross_cutting` does NOT contain a `## Architecture Vision` section).
State-driven: reached by auto-chain from Step 2 (user picked A).
**Goal**: Capture meta-repo-wide terminology and the user's architecture vision **once**, after the config is confirmed but before any sync skill runs. Without this, `monorepo-document` will faithfully propagate per-component changes but never surface a unified mental model of the meta-repo to the user, and the AI will keep re-inferring the same project terminology on every invocation.
**Why inline (no sub-skill)**: `monorepo-discover` is hard-guarded to write only `_repo-config.yaml`; `monorepo-document` only edits *existing* docs. Glossary and architecture-vision creation is a first-time, user-confirmed write that crosses both guarantees, so it lives directly in the flow.
**Inputs**:
- `_docs/_repo-config.yaml` (component list, doc map, conventions, assumptions log)
- Cross-cutting docs listed under `docs.cross_cutting` (existing architecture doc, if any)
- Each component's `primary_doc` (read-only, for terminology + responsibility extraction)
- Root `README.md` if `repo.root_readme` is referenced
**Outputs**:
- `_docs/glossary.md` (or `<docs.root>/glossary.md` if `docs.root` ≠ `_docs/`) — NEW
- The cross-cutting architecture doc updated in place: a `## Architecture Vision` section is prepended (or merged into an existing "Vision" / "Overview" heading)
- One new entry appended to `_docs/_repo-config.yaml` under `assumptions_log:` recording the run
- A new top-level config entry: `glossary_doc: <path>` so future `monorepo-status` and `monorepo-document` runs treat the glossary as a known cross-cutting doc
**Procedure**:
1. **Draft glossary** from `_repo-config.yaml` + each component's primary doc. Include:
- Component codenames as they appear in the config (`name` field) and any rename pairs the user noted in `unresolved:` resolutions
- Domain terms that recur across ≥2 component docs
- Acronyms / abbreviations
- Convention names from `conventions:` (e.g., commit prefix, deployment tier names)
- Stakeholder personas if cross-cutting docs reference them
Each entry: one-line definition + source (`source: components.<name>.primary_doc` or `source: _repo-config.yaml conventions`). Skip generic terms.
2. **Draft architecture vision** from the meta-repo perspective:
- **One paragraph**: what the system as a whole is, what each component contributes, the runtime topology (one binary / N services / N clients + 1 server / hybrid), how components communicate (REST / gRPC / queue / DB-shared / file-shared)
- **Components & responsibilities** (one-line each), pulled directly from `_repo-config.yaml` `components:` list
- **Cross-cutting concerns ownership**: which doc owns which concern (auth, schema, deployment, etc.) — pulled from `docs.cross_cutting[].owns`
- **Architectural principles / non-negotiables** the user has implied across components (e.g., "all components share a single Postgres", "submodules own their own CI", "deployment is per-tier, not per-component")
- **Open questions / structural drift signals**: components missing from `docs.cross_cutting`, components in registry but not in config (registry mismatch), or contradictions between component primary docs
3. **Present condensed view** to the user (NOT the full draft files):
```
══════════════════════════════════════
REVIEW: Meta-Repo Glossary + Architecture Vision
══════════════════════════════════════
Glossary (N terms drafted from config + component docs):
- <Term>: <one-line definition>
- ...
Architecture Vision — meta-repo level:
<one-paragraph synopsis>
Components / responsibilities:
- <component>: <one-line>
- ...
Cross-cutting ownership:
- <concern> → <doc>
- ...
Principles / non-negotiables:
- <principle>
- ...
Open questions / drift signals:
- <q1>
- <q2>
══════════════════════════════════════
A) Looks correct — write the files
B) Add / correct entries (provide diffs)
C) Resolve open questions / drift signals first
══════════════════════════════════════
Recommendation: pick C if drift signals exist;
otherwise B if components or principles
don't match your intent; A only when
the inferred vision is exactly right.
══════════════════════════════════════
```
4. **Iterate**:
- On B → integrate the user's diffs/additions, re-present, loop until A.
- On C → ask the listed open questions in one batch, integrate answers, re-present.
- **Do NOT proceed to step 5 until the user picks A.**
5. **Save**:
- Write `_docs/glossary.md` (alphabetical) with `**Status**: confirmed-by-user` + date.
- Update the cross-cutting architecture doc identified in `docs.cross_cutting` (or create one at `_docs/00_architecture.md` if none exists and the user's option-B input named one): prepend `## Architecture Vision` with the confirmed paragraph + components + ownership + principles. Preserve every existing H2 below verbatim.
- Append to `_docs/_repo-config.yaml`:
- Top-level `glossary_doc: <path-relative-to-repo-root>` (sibling of `docs.root`)
- New `assumptions_log:` entry: `{ date: <today>, skill: autodev-meta-repo Step 2.5, run_notes: "Captured glossary + architecture vision", assumptions: [...] }`
- Do NOT flip any `confirmed: false` → `confirmed: true` in the config; this step writes its own confirmed artifact, it does not retroactively confirm config inferences.
**Self-verification**:
- [ ] Every glossary entry traces to either the config or a component primary doc
- [ ] Every component listed in the vision matches a `components:` entry in the config
- [ ] All open questions are answered or explicitly deferred (with the user's acknowledgement)
- [ ] The cross-cutting architecture doc still contains every H2 it had before this step
- [ ] User picked option A on the latest condensed view
**Idempotency**: if both `_docs/glossary.md` exists AND the architecture doc already has a `## Architecture Vision` section, this step is **skipped on re-invocation**. To refresh, the user invokes `/autodev` after deleting `glossary.md` (or running `monorepo-discover` with structural changes that justify a re-confirmation).
After completion, auto-chain to **Step 3 (Status)**.
---
**Step 3 — Status**
Condition (folder fallback): `_docs/_repo-config.yaml` exists AND `confirmed_by_user: true` AND (`_docs/glossary.md` exists OR `glossary_doc:` is recorded in the config).
State-driven: reached by auto-chain from Step 2.5, or entered on any re-invocation after a completed cycle.
Condition (folder fallback): `_docs/_repo-config.yaml` exists AND `confirmed_by_user: true`.
State-driven: reached by auto-chain from Step 2 (user picked A), or entered on any re-invocation after a completed cycle.
Action: Read and execute `.cursor/skills/monorepo-status/SKILL.md`.
@@ -186,16 +78,11 @@ The status report identifies:
- Registry/config mismatches
- Unresolved questions
Based on the report, auto-chain branches in this evaluation order (first match wins):
Based on the report, auto-chain branches:
1. **Registry mismatch** (new components not in config, or config component not in registry) → present the Choose format below FIRST. After the user resolves it (A: refresh discover, B: onboard, C: continue with mismatch acknowledged), proceed to the next rule. This rule has priority because a stale config would mislead Step 3.5's ownership-envelope synthesis and any sync skill's component scope.
2. **Pre-routing gate (Step 3.5 detection)** — check `_docs/tasks/todo/` for suite-level task files (`*.md` excluding files starting with `_`). If ≥1 task is present, auto-chain to **Step 3.5 (Suite Implement)**. After Step 3.5 returns (regardless of A/B outcome), the post-implement re-status applies rules 3–6 below to the post-implementation state.
3. If **doc drift** found → auto-chain to **Step 4 (Document Sync)**
4. Else if **CI drift** (only) found → auto-chain to **Step 5 (CICD Sync)**
5. Else if **suite-e2e drift** (only) found → auto-chain to **Step 4.5 (Integration Test Sync)** (only when `suite_e2e:` block exists in config)
6. Else → **workflow done for this cycle**.
**Registry mismatch Choose format** (rule 1):
- If **doc drift** found → auto-chain to **Step 4 (Document Sync)**
- Else if **CI drift** (only) found → auto-chain to **Step 5 (CICD Sync)**
- Else if **registry mismatch** found (new components not in config) → present Choose format:
```
══════════════════════════════════════
@@ -212,134 +99,7 @@ Based on the report, auto-chain branches in this evaluation order (first match w
══════════════════════════════════════
```
When rule 6 fires (no drift, no todo tasks), report "No drift. Meta-repo is in sync." and end the cycle. Loop waits for next invocation.
---
**Step 3.5 — Suite Implement**
Condition (folder fallback): `_docs/tasks/todo/` exists AND contains ≥1 file matching `*.md` excluding files starting with `_` (e.g., `_dependencies_table.md` is excluded by convention).
State-driven: reached by auto-chain from Step 3 when the pre-routing gate detected todo tasks. Inserted **before** the sync skills (Step 4 / 4.5 / 5) by deliberate design: implementing renames + cross-repo edits first means the subsequent sync skills propagate the actual landed state rather than the pre-change state, avoiding a second cycle to fix downstream drift.
**Skip condition**: `_docs/tasks/todo/` is empty, missing, or contains only `_*` files. In that case Step 3.5 is skipped entirely and the cycle proceeds with Step 3's existing drift-based routing.
**Goal**: Execute suite-level implementation tasks — cross-repo concerns (e.g., `autopilot` + `ui` + suite `e2e/` cutover in a coordinated change-set), folder renames (e.g., `git mv flights missions` + `.gitmodules` edit + `_infra/` path refs), and suite-root infrastructure additions (e.g., `_infra/dev/docker-compose.dev.yml`). Per-component implementation work stays in each component's own workspace `/autodev` cycle.
**Why this exists**: the meta-repo's existing sync skills (`monorepo-document`, `monorepo-cicd`, `monorepo-e2e`) only **propagate** changes that already landed. They cannot **execute** a task spec. Without Step 3.5, suite-level tickets like AZ-543 (B4 repo rename) or AZ-506 (new dev compose) have no flow path forward — they require operator action outside autodev.
**Inputs**:
- `_docs/tasks/todo/*.md` (excluding `_*`) — task specs in the existing format (`Task` / `Component` / `Dependencies` / `Acceptance criteria` headers)
- `_docs/_repo-config.yaml` — `components[].path` list, used to compute the suite-level OWNED envelope (workspace root EXCLUDING any path under a component's folder)
- `_docs/tasks/_dependencies_table.md` — synthesized by this step if missing (see Procedure)
- `_docs/tasks/_suite_module_layout.md` — synthesized by this step if missing (see Procedure)
**Procedure**:
1. **Detection (already done by Step 3 pre-routing gate)**. List task files in `_docs/tasks/todo/` (excluding `_*`). If 0 → skip Step 3.5. If ≥1 → continue.
2. **Present Choose**:
```
══════════════════════════════════════
DECISION REQUIRED: <N> suite-level task(s) in _docs/tasks/todo/
══════════════════════════════════════
Task(s) detected:
- AZ-XXX: <title> (deps: <list or "—">)
- AZ-YYY: <title> (deps: <list or "—">)
...
A) Run implement skill on these task(s) now (then continue to Doc / E2E / CICD sync)
B) Skip implement this cycle — continue to Doc / E2E / CICD sync without executing tasks
C) Pause — review the tasks before deciding (end session, no state changes)
══════════════════════════════════════
Recommendation: A — running implement BEFORE syncs means subsequent
sync skills propagate the post-implementation state.
B is appropriate when tasks are blocked on user input
or external coordination. C when the tasks themselves
need owner clarification before execution.
══════════════════════════════════════
```
3. **On user A — Pre-flight**:
a. **Working tree clean check**. Run `git status --porcelain`. If non-empty, surface to the user with a Choose A/B/C identical to the implement skill's prerequisite gate (commit/stash manually; agent commits as `chore: WIP pre-implement`; abort).
b. **Synthesize `_docs/tasks/_dependencies_table.md`** if missing. Parse each in-scope task's `Dependencies:` field. Write a minimal table of the form:
```markdown
# Suite-Level Task Dependencies
| Task ID | Depends on | Notes |
|---------|------------|-------|
| AZ-XXX | (none) | — |
| AZ-YYY | AZ-XXX | — |
```
If a task lists a dependency that is neither in `todo/` nor `done/`, log a warning in the synthesized file but do not block — implement skill's Step 1 (Parse) will surface the issue if it actually blocks execution.
c. **Synthesize `_docs/tasks/_suite_module_layout.md`** if missing. Default content:
```markdown
# Suite-Level Module Layout (synthetic)
Generated by autodev meta-repo Step 3.5. The suite root has no per-feature decomposition; ownership is defined at the component-boundary level only.
## Per-Component Mapping
| Component | Owns | Imports from |
|-----------|----------------------------------|--------------|
| suite | (workspace root) excluding any path listed under `_repo-config.yaml.components[].path` | (read-only) every component's primary doc + `_docs/*.md` |
Suite-level tasks operate on: `.gitmodules`, `_infra/**`, `_docs/**` (excluding `_docs/tasks/_*` regenerated files), root `README.md`, `e2e/**` (suite e2e harness only).
Forbidden paths for suite-level tasks: `<component>/**` for every component listed in `_repo-config.yaml.components[].path` — those edits live in the component's own workspace `/autodev` cycle.
```
d. **Prepare invocation context**:
```
suite_level: true
TASKS_DIR: _docs/tasks/
module_layout_path: _docs/tasks/_suite_module_layout.md
```
4. **Invoke implement skill**. Read and execute `.cursor/skills/implement/SKILL.md` with the prepared context. The skill's "Suite-level invocation context" subsection (added in tandem with this flow change) honors the three flags above and skips:
- Step 14.5 (cumulative code review) — no `architecture_compliance_baseline.md` exists at the suite level; cross-task drift is captured by the next `monorepo-status` cycle instead.
- Step 15 (Product Implementation Completeness Gate) — the gate's inputs (`_docs/02_document/architecture.md`, `system-flows.md`, `components/*/description.md`) do not exist in the meta-repo artifact layout. Suite tasks are infrastructure / coordination work, not feature implementation.
All other implement skill steps (1–14, 16) execute unchanged. Tracker integration (Step 5: In Progress, Step 12: In Testing) runs normally.
5. **Post-implement re-status**. After the implement skill completes (last batch committed, all originally-todo tasks moved to `_docs/tasks/done/`), silently re-run Step 3's drift detection logic — do NOT re-render the full Status report; just re-evaluate the drift signals against the post-implementation tree. Then auto-chain per the post-implementation drift findings:
- Doc drift → Step 4 (Document Sync)
- Suite-e2e drift only → Step 4.5
- CI drift only → Step 5
- No drift → cycle complete
Note: the post-implement re-status is exactly why Step 3.5 is placed before sync. A repo rename will typically introduce doc + CI drift; the next invocation of Step 4 / Step 5 catches it on the same cycle.
6. **On user B (skip)** → mark Step 3.5 `skipped` in state file. Apply Step 3's original drift-based routing (compute from the pre-Step-3.5 Status report).
7. **On user C (pause)** → end session. Update state to `step: 3.5, status: in_progress, sub_step: {phase: 0, name: awaiting-task-review, detail: "<N> tasks pending review"}`. Tell the user to invoke `/autodev` again after deciding. **Do NOT modify any files** — pre-flight has not run yet.
**Self-verification** (executed before invoking implement):
- [ ] Working tree is clean (or user explicitly chose B in the WIP-stash sub-Choose)
- [ ] `_docs/tasks/_dependencies_table.md` exists (synthesized if it didn't)
- [ ] `_docs/tasks/_suite_module_layout.md` exists (synthesized if it didn't)
- [ ] All in-scope task files have a `Component:` field (skip + report any that don't — don't guess ownership)
- [ ] Tracker availability gate satisfied per `protocols.md` (or `tracker: local` previously chosen)
**Failure handling**:
- If implement returns FAILED → standard Failure Handling (`protocols.md`): retry up to 3 times, then escalate.
- If implement is interrupted mid-batch → next invocation re-detects via the implement skill's resumability protocol (read latest `_docs/03_implementation/suite_batch_*.md`). Step 3.5 itself is reentrant: on re-entry, if `todo/` still has tasks, it presents the Choose again with the remaining set.
- **Half-applied state risk** (acknowledged): if implement is interrupted between commits, the working tree is clean at the last commit boundary but the in-flight batch is lost. The user is responsible for inspecting and re-invoking. This is intentional — automated rollback of suite-level renames + `.gitmodules` edits is more dangerous than a human-driven recovery.
**Idempotency**: if `_docs/tasks/todo/` becomes empty after this step (all tasks moved to `done/`), the next `/autodev` invocation skips Step 3.5 entirely and proceeds with normal Status → sync flow.
- Else → **workflow done for this cycle**. Report "No drift. Meta-repo is in sync." Loop waits for next invocation.
---
@@ -355,28 +115,6 @@ The skill:
3. Applies doc edits
4. Skips any component with unconfirmed mapping (M5), reports
After completion:
- If the status report ALSO flagged suite-e2e drift → auto-chain to **Step 4.5 (Integration Test Sync)**
- Else if the status report ALSO flagged CI drift → auto-chain to **Step 5 (CICD Sync)**
- Else → end cycle, report done
---
**Step 4.5 — Integration Test Sync**
State-driven: reached by auto-chain from Step 3 (when status report flagged suite-e2e drift and no doc drift) or from Step 4 (when both doc and suite-e2e drift were flagged).
**Skip condition**: if `_docs/_repo-config.yaml` has no `suite_e2e:` block, this step is skipped entirely — there's no harness to sync. The status report should not flag suite-e2e drift in that case; if it does, that's a status-skill bug.
Action: Read and execute `.cursor/skills/monorepo-e2e/SKILL.md` with scope = components flagged by status.
The skill:
1. Verifies every path under `suite_e2e.*` exists (binary fixtures excepted — see the skill's Phase 1)
2. Classifies each flagged change against the suite-e2e impact table
3. Applies edits to `e2e/docker-compose.suite-e2e.yml`, `e2e/fixtures/init.sql`, `e2e/fixtures/expected_detections.json` metadata, and `e2e/runner/tests/*.spec.ts` selectors as needed
4. Bumps baseline `fixture_version` with a `-stale` suffix and appends a `_docs/_process_leftovers/` entry whenever the detection model revision changes (binary fixture cannot be regenerated automatically)
5. Reports synced files; does not run the suite e2e itself
After completion:
- If the status report ALSO flagged CI drift → auto-chain to **Step 5 (CICD Sync)**
- Else → end cycle, report done
@@ -385,11 +123,11 @@ After completion:
**Step 5 — CICD Sync**
State-driven: reached by auto-chain from Step 3 (when status report flagged CI drift and no doc/suite-e2e drift), Step 4, or Step 4.5.
State-driven: reached by auto-chain from Step 3 (when status report flagged CI drift and no doc drift) or from Step 4 (when both doc and CI drift were flagged).
Action: Read and execute `.cursor/skills/monorepo-cicd/SKILL.md` with scope = components flagged by status.
After completion, end cycle. Report files updated across doc, suite-e2e, and CI sync.
After completion, end cycle. Report files updated across both doc and CI sync.
---
@@ -418,24 +156,14 @@ After onboarding completes, the config is updated. Auto-chain back to **Step 3 (
| Completed Step | Next Action |
|---------------|-------------|
| Discover (1) | Auto-chain → Config Review (2) |
| Config Review (2, user picked A, confirmed_by_user: true) | Auto-chain → Glossary & Architecture Vision (2.5) |
| Config Review (2, user picked A, confirmed_by_user: true) | Auto-chain → Status (3) |
| Config Review (2, user picked B) | **Session boundary** — end session, await re-invocation |
| Glossary & Architecture Vision (2.5) | Auto-chain → Status (3) |
| Status (3, todo tasks present) | Auto-chain → Suite Implement (3.5) — pre-routing gate fires before drift-based routing |
| Status (3, no todo tasks, doc drift) | Auto-chain → Document Sync (4) |
| Status (3, no todo tasks, suite-e2e drift only) | Auto-chain → Integration Test Sync (4.5) |
| Status (3, no todo tasks, CI drift only) | Auto-chain → CICD Sync (5) |
| Status (3, no todo tasks, no drift) | **Cycle complete** — end session, await re-invocation |
| Status (3, doc drift) | Auto-chain → Document Sync (4) |
| Status (3, CI drift only) | Auto-chain → CICD Sync (5) |
| Status (3, no drift) | **Cycle complete** — end session, await re-invocation |
| Status (3, registry mismatch) | Ask user (A: discover, B: onboard, C: continue) |
| Suite Implement (3.5, user picked A, success) | Silent re-status; auto-chain per post-implementation drift (Step 4 / 4.5 / 5 / cycle complete) |
| Suite Implement (3.5, user picked B) | Mark `skipped`; auto-chain per Step 3's original drift findings |
| Suite Implement (3.5, user picked C) | **Session boundary** — end session, await re-invocation |
| Suite Implement (3.5, FAILED ×3) | Standard Failure Handling escalation (`protocols.md`) |
| Document Sync (4) + suite-e2e drift pending | Auto-chain → Integration Test Sync (4.5) |
| Document Sync (4) + CI drift only pending | Auto-chain → CICD Sync (5) |
| Document Sync (4) + no further drift | **Cycle complete** |
| Integration Test Sync (4.5) + CI drift pending | Auto-chain → CICD Sync (5) |
| Integration Test Sync (4.5) + no CI drift | **Cycle complete** |
| Document Sync (4) + CI drift pending | Auto-chain → CICD Sync (5) |
| Document Sync (4) + no CI drift | **Cycle complete** |
| CICD Sync (5) | **Cycle complete** |
## Status Summary — Step List
@@ -450,40 +178,30 @@ Flow-specific slot values:
Config: _docs/_repo-config.yaml [confirmed_by_user: <true|false>, last_updated: <date>]
```
| # | Step Name | Extra state tokens (beyond the shared set) |
|---|------------------------------------|--------------------------------------------|
| 1 | Discover | — |
| 2 | Config Review | `IN PROGRESS (awaiting human)` |
| 2.5 | Glossary & Architecture Vision | `SKIPPED (already captured)` |
| 3 | Status | `DONE (no drift)`, `DONE (N drifts)` |
| 3.5 | Suite Implement | `DONE (N tasks)`, `SKIPPED (no todo tasks)`, `SKIPPED (user picked B)`, `IN PROGRESS (batch M of ~N)`, `IN PROGRESS (awaiting-task-review)` |
| 4 | Document Sync | `DONE (N docs)`, `SKIPPED (no doc drift)` |
| 4.5 | Integration Test Sync | `DONE (N files)`, `SKIPPED (no suite-e2e drift)`, `SKIPPED (no suite_e2e config block)` |
| 5 | CICD Sync | `DONE (N files)`, `SKIPPED (no CI drift)` |
| # | Step Name | Extra state tokens (beyond the shared set) |
|---|------------------|--------------------------------------------|
| 1 | Discover | — |
| 2 | Config Review | `IN PROGRESS (awaiting human)` |
| 3 | Status | `DONE (no drift)`, `DONE (N drifts)` |
| 4 | Document Sync | `DONE (N docs)`, `SKIPPED (no doc drift)` |
| 5 | CICD Sync | `DONE (N files)`, `SKIPPED (no CI drift)` |
All rows accept the shared state tokens (`DONE`, `IN PROGRESS`, `NOT STARTED`, `FAILED (retry N/3)`); rows 2.5, 3.5, 4, 4.5, and 5 additionally accept `SKIPPED`.
All rows accept the shared state tokens (`DONE`, `IN PROGRESS`, `NOT STARTED`, `FAILED (retry N/3)`); rows 4 and 5 additionally accept `SKIPPED`.
Row rendering format:
```
Step 1 Discover [<state token>]
Step 2 Config Review [<state token>]
Step 2.5 Glossary & Architecture Vision [<state token>]
Step 3 Status [<state token>]
Step 3.5 Suite Implement [<state token>]
Step 4 Document Sync [<state token>]
Step 4.5 Integration Test Sync [<state token>]
Step 5 CICD Sync [<state token>]
Step 1 Discover [<state token>]
Step 2 Config Review [<state token>]
Step 3 Status [<state token>]
Step 4 Document Sync [<state token>]
Step 5 CICD Sync [<state token>]
```
## Notes for the meta-repo flow
- **Session boundaries**: Step 2 (Config Review pending), Step 2.5 (one-shot glossary/vision review), and Step 3.5 (when user picks C "Pause"). Step 3.5's A/B picks do NOT cross a session boundary — they auto-chain to syncs in the same session.
- **No session boundary except Step 2**: unlike existing-code flow (which has boundaries around decompose), meta-repo flow only pauses at config review. Syncing is fast enough to complete in one session.
- **Cyclical, not terminal**: no "done forever" state. Each invocation completes a drift cycle; next invocation starts fresh.
- **Tracker integration scope**: this flow does NOT create Jira/ADO tickets in its sync skills (Status / Document Sync / E2E / CICD). Step 3.5 (Suite Implement) IS tracker-integrated — it transitions existing tickets In Progress → In Testing per the implement skill's standard tracker handling. Suite-level tickets are authored manually by the operator (typically as children of an Epic that spans multiple components, like AZ-539); the flow doesn't auto-create them.
- **Per-component vs. suite-level work**:
- Tickets that touch component source code (`<component>/src/**`) belong in that component's own workspace `/autodev` cycle. The meta-repo flow does NOT execute them.
- Tickets that touch suite-root paths only (`.gitmodules`, `_infra/**`, suite `e2e/**`, root `README.md`, suite `_docs/**` outside `tasks/_*`) are eligible for Step 3.5.
- Tickets that span both (e.g., AZ-550 B11 consumer cutover, which touches `autopilot/`, `ui/`, AND suite `e2e/`) are NOT executable from a single workspace by design — split the ticket so the suite-level slice can run in Step 3.5 and the component slices run in their owning workspaces.
- **No tracker integration**: this flow does NOT create Jira/ADO tickets. Maintenance is not a feature — if a feature-level ticket spans the meta-repo's concerns, it lives in the per-component workspace.
- **Onboarding is opt-in**: never auto-onboarded. User must explicitly request.
- **Failure handling**: uses the same retry/escalation protocol as other flows (see `protocols.md`).
.cursor/skills/autodev/protocols.md
+4 -6
View File
@@ -110,11 +110,9 @@ Before entering a step from this table for the first time in a session, verify t
| Flow | Step | Sub-Step | Tracker Action |
|------|------|----------|----------------|
| greenfield | Plan | Step 6 — Epics | Create epics for each component |
| greenfield | Decompose | Implementation decomposition Step 1 + Step 2 — Product tasks | Create ticket per product task, link to epic |
| greenfield | Decompose Tests | Step 1t + Step 3 — All test tasks | Create ticket per task, link to epic |
| greenfield | Decompose | Step 1 + Step 2 + Step 3 — All tasks | Create ticket per task, link to epic |
| existing-code | Decompose Tests | Step 1t + Step 3 — All test tasks | Create ticket per task, link to epic |
| existing-code | New Task | Step 7 — Ticket | Create ticket per task, link to epic |
| meta-repo | Suite Implement | Step 3.5 — implement skill Step 5 / Step 12 | Transition existing tickets In Progress → In Testing per implement skill (does NOT create new tickets — operator authors them) |
### State File Marker
@@ -140,7 +138,7 @@ One retry ladder covers all failure modes: explicit failure returned by a sub-sk
Treat the sub-skill as **failed** when ANY of the following is observed:
- The sub-skill explicitly returns a failed result (including blocked tasks, auto-fix loop exhaustion, prerequisite violations).
- The sub-skill explicitly returns a failed result (including blocked subagents, auto-fix loop exhaustion, prerequisite violations).
- **Stuck signals**: the same artifact is rewritten 3+ times without meaningful change; the sub-skill re-asks a question that was already answered; no new artifact has been saved despite active execution.
### Retry ladder
@@ -293,7 +291,7 @@ For steps that produce `_docs/` artifacts (problem, research, plan, decompose, d
## Debug Protocol
When the implement skill's auto-fix loop fails (code review FAIL after 2 auto-fix attempts) or a task reports a blocker, the user is asked to intervene. This protocol guides the debugging process. (Retry budget and escalation are covered by Failure Handling above; this section is about *how* to diagnose once the user has been looped in.)
When the implement skill's auto-fix loop fails (code review FAIL after 2 auto-fix attempts) or an implementer subagent reports a blocker, the user is asked to intervene. This protocol guides the debugging process. (Retry budget and escalation are covered by Failure Handling above; this section is about *how* to diagnose once the user has been looped in.)
### Structured Debugging Workflow
@@ -389,7 +387,7 @@ The banner shell is defined here once. Each flow file contributes only its step-
where `<state token>` comes from the state-token set defined per row in the flow's step-list table.
- `<current-suffix>` — optional, flow-specific. The existing-code flow appends ` (cycle <N>)` when `state.cycle > 1`; other flows leave it empty.
- `Retry:` row — omit entirely when `retry_count` is 0. Include it with `<N>/3` otherwise.
- `<footer-extras>` — optional, flow-specific. The meta-repo flow adds a `Config:` line with `_docs/_repo-config.yaml` state; other flows leave it empty unless **parent suite docs** apply: if `<workspace-root>/../docs` exists and is a directory, append `Suite docs (parent): <absolute path>` on its own line (or `Suite docs (parent): absent` is **not** required — omit when missing). This line is orthogonal to flow-specific footer lines; both may appear.
- `<footer-extras>` — optional, flow-specific. The meta-repo flow adds a `Config:` line with `_docs/_repo-config.yaml` state; other flows leave it empty.
### State token set (shared)
.cursor/skills/autodev/state.md
+2 -15
View File
@@ -13,7 +13,7 @@ The autodev persists its position to `_docs/_autodev_state.md`. This is a lightw
## Current Step
flow: [greenfield | existing-code | meta-repo]
step: [1-17 for greenfield, 1-17 for existing-code, 1-6 for meta-repo (incl. fractional 2.5 and 3.5), or "done"]
step: [1-11 for greenfield, 1-17 for existing-code, 1-6 for meta-repo, or "done"]
name: [step name from the active flow's Step Reference Table]
status: [not_started / in_progress / completed / skipped / failed]
sub_step:
@@ -82,19 +82,6 @@ retry_count: 0
cycle: 1
```
```
flow: meta-repo
step: 3.5
name: Suite Implement
status: in_progress
sub_step:
phase: 7
name: batch-loop
detail: "AZ-543 batch 1 of 1; suite-level"
retry_count: 0
cycle: 1
```
```
flow: existing-code
step: 10
@@ -113,7 +100,7 @@ cycle: 3
1. **Create** on the first autodev invocation (after state detection determines Step 1)
2. **Update** after every change — this includes: batch completion, sub-step progress, step completion, session boundary, failed retry, or any meaningful state transition. The state file must always reflect the current reality.
3. **Read** as the first action on every invocation — before folder scanning
4. **Cross-check**: verify against actual `_docs/` folder contents. If they disagree, trust the folder structure and update the state file. **Parent suite `docs/`**: on every invocation, also probe `<workspace-root>/../docs` (the parent directory’s `docs` folder — typical suite-level shared documentation next to a component repo). If it exists, mention it in the Status Summary footer per `protocols.md`; use it only as supplemental reading context unless a flow step explicitly ties detection to it. It never replaces workspace `_docs/` for step detection by default.
4. **Cross-check**: verify against actual `_docs/` folder contents. If they disagree, trust the folder structure and update the state file
5. **Never delete** the state file
6. **Retry tracking**: increment `retry_count` on each failed auto-retry; reset to `0` on success. If `retry_count` reaches 3, set `status: failed`
7. **Failed state on re-entry**: if `status: failed` with `retry_count: 3`, do NOT auto-retry — present the issue to the user first
.cursor/skills/code-review/SKILL.md
+2 -2
View File
@@ -209,7 +209,7 @@ Bug, Spec-Gap, Security, Performance, Maintainability, Style, Scope, Architectur
The `/implement` skill invokes this skill after each batch completes:
1. Collects changed files from all tasks implemented in the batch
1. Collects changed files from all implementer agents in the batch
2. Passes task spec paths + changed files to this skill
3. If verdict is FAIL — presents findings to user (BLOCKING), user fixes or confirms
4. If verdict is PASS or PASS_WITH_WARNINGS — proceeds automatically (findings shown as info)
@@ -221,7 +221,7 @@ The `/implement` skill invokes this skill after each batch completes:
| Input | Type | Source | Required |
|-------|------|--------|----------|
| `task_specs` | list of file paths | Task `.md` files from `_docs/02_tasks/todo/` for the current batch | Yes |
| `changed_files` | list of file paths | Files modified by the tasks in the batch (from `git diff`) | Yes |
| `changed_files` | list of file paths | Files modified by implementer agents (from `git diff` or agent reports) | Yes |
| `batch_number` | integer | Current batch number (for report naming) | Yes |
| `project_restrictions` | file path | `_docs/00_problem/restrictions.md` | If exists |
| `solution_overview` | file path | `_docs/01_solution/solution.md` | If exists |
.cursor/skills/decompose/SKILL.md
+24 -27
View File
@@ -2,8 +2,8 @@
name: decompose
description: |
Decompose planned components into atomic implementable tasks with bootstrap structure plan.
Workflow entrypoints: implementation task decomposition, single component decomposition, and tests-only decomposition.
The invoking flow decides which entrypoint to run; this skill executes that selected sequence.
4-step workflow: bootstrap structure plan, component task decomposition, blackbox test task decomposition, and cross-task verification.
Supports full decomposition (_docs/ structure), single component mode, and tests-only mode.
Trigger phrases:
- "decompose", "decompose features", "feature decomposition"
- "task decomposition", "break down components"
@@ -20,7 +20,7 @@ Decompose planned components into atomic, implementable task specs with a bootst
## Core Principles
- **Atomic tasks**: each task does one thing; if it exceeds 5 complexity points, split it
- **Atomic tasks**: each task does one thing; if it exceeds 8 complexity points, split it
- **Behavioral specs, not implementation plans**: describe what the system should do, not how to build it
- **Flat structure**: all tasks are tracker-ID-prefixed files in TASKS_DIR — no component subdirectories
- **Save immediately**: write artifacts to disk after each task; never accumulate unsaved work
@@ -30,15 +30,14 @@ Decompose planned components into atomic, implementable task specs with a bootst
## Context Resolution
Resolve the selected entrypoint from the invocation context before any other logic runs. The caller decides whether this is implementation, single component, or tests-only decomposition; this skill only executes the selected sequence.
Determine the operating mode based on invocation before any other logic runs.
**Implementation task decomposition** (default; selected by flows before invoking this skill):
**Default** (no explicit input file provided):
- DOCUMENT_DIR: `_docs/02_document/`
- TASKS_DIR: `_docs/02_tasks/`
- TASKS_TODO: `_docs/02_tasks/todo/`
- Reads from: `_docs/00_problem/`, `_docs/01_solution/`, DOCUMENT_DIR
- Produces only implementation tasks. Blackbox/e2e test task files are produced only when the invoking flow selects tests-only decomposition.
**Single component mode** (provided file is within `_docs/02_document/` and inside a `components/` subdirectory):
@@ -56,24 +55,24 @@ Resolve the selected entrypoint from the invocation context before any other log
- TESTS_DIR: `DOCUMENT_DIR/tests/`
- Reads from: `_docs/00_problem/`, `_docs/01_solution/`, TESTS_DIR
Announce the selected entrypoint and resolved paths to the user before proceeding.
Announce the detected mode and resolved paths to the user before proceeding.
### Step Applicability by Mode
| Step | File | Implementation | Single | Tests-only |
|------|------|:--------------:|:------:|:----------:|
| Step | File | Default | Single | Tests-only |
|------|------|:-------:|:------:|:----------:|
| 1 Bootstrap Structure | `steps/01_bootstrap-structure.md` | ✓ | — | — |
| 1t Test Infrastructure | `steps/01t_test-infrastructure.md` | — | — | ✓ |
| 1.5 Module Layout | `steps/01-5_module-layout.md` | ✓ | — | — |
| 2 Task Decomposition | `steps/02_task-decomposition.md` | ✓ | ✓ | — |
| 3 Blackbox Test Tasks | `steps/03_blackbox-test-decomposition.md` | — | — | ✓ |
| 3 Blackbox Test Tasks | `steps/03_blackbox-test-decomposition.md` | ✓ | — | ✓ |
| 4 Cross-Verification | `steps/04_cross-verification.md` | ✓ | — | ✓ |
## Input Specification
### Required Files
**Implementation task decomposition:**
**Default:**
| File | Purpose |
|------|---------|
@@ -81,11 +80,10 @@ Announce the selected entrypoint and resolved paths to the user before proceedin
| `_docs/00_problem/restrictions.md` | Constraints and limitations |
| `_docs/00_problem/acceptance_criteria.md` | Measurable acceptance criteria |
| `_docs/01_solution/solution.md` | Finalized solution |
| `DOCUMENT_DIR/architecture.md` | Architecture from plan/document skill (must contain a `## Architecture Vision` H2 — confirmed user intent) |
| `DOCUMENT_DIR/glossary.md` | Project terminology (confirmed by user in plan Phase 2a.0 or document Step 4.5). Use it to keep task names, component references, and AC wording consistent with the user's vocabulary |
| `DOCUMENT_DIR/architecture.md` | Architecture from plan skill |
| `DOCUMENT_DIR/system-flows.md` | System flows from plan skill |
| `DOCUMENT_DIR/components/[##]_[name]/description.md` | Component specs from plan skill |
| `DOCUMENT_DIR/tests/` | Optional product acceptance context from test-spec skill; do not create test task files from it in this entrypoint |
| `DOCUMENT_DIR/tests/` | Blackbox test specs from plan skill |
**Single component mode:**
@@ -112,7 +110,7 @@ Announce the selected entrypoint and resolved paths to the user before proceedin
### Prerequisite Checks (BLOCKING)
**Implementation task decomposition:**
**Default:**
1. DOCUMENT_DIR contains `architecture.md` and `components/` — **STOP if missing**
2. Create TASKS_DIR and TASKS_TODO if they do not exist
@@ -146,8 +144,6 @@ TASKS_DIR/
**Naming convention**: Each task file is initially saved in `TASKS_TODO/` with a temporary numeric prefix (`[##]_[short_name].md`). After creating the work item ticket, rename the file to use the work item ticket ID as prefix (`[TRACKER-ID]_[short_name].md`). For example: `todo/01_initial_structure.md` → `todo/AZ-42_initial_structure.md`.
If tracker availability fails, follow `.cursor/rules/tracker.mdc` before continuing. Only when the user explicitly chooses `tracker: local` may the numeric prefix remain; in that mode set `Tracker: pending` and `Epic: pending` in the task header and keep the task eligible for later tracker sync.
### Save Timing
| Step | Save immediately after | Filename |
@@ -169,11 +165,11 @@ If TASKS_DIR subfolders already contain task files:
## Progress Tracking
At the start of execution, create a TodoWrite with all applicable steps for the selected entrypoint (see Step Applicability table). Update status as each step/component completes.
At the start of execution, create a TodoWrite with all applicable steps for the detected mode (see Step Applicability table). Update status as each step/component completes.
## Workflow
### Step 1: Bootstrap Structure Plan (implementation mode only)
### Step 1: Bootstrap Structure Plan (default mode only)
Read and follow `steps/01_bootstrap-structure.md`.
@@ -185,25 +181,25 @@ Read and follow `steps/01t_test-infrastructure.md`.
---
### Step 1.5: Module Layout (implementation mode only)
### Step 1.5: Module Layout (default mode only)
Read and follow `steps/01-5_module-layout.md`.
---
### Step 2: Task Decomposition (implementation and single component modes)
### Step 2: Task Decomposition (default and single component modes)
Read and follow `steps/02_task-decomposition.md`.
---
### Step 3: Blackbox Test Task Decomposition (tests-only mode only)
### Step 3: Blackbox Test Task Decomposition (default and tests-only modes)
Read and follow `steps/03_blackbox-test-decomposition.md`.
---
### Step 4: Cross-Task Verification (implementation and tests-only modes)
### Step 4: Cross-Task Verification (default and tests-only modes)
Read and follow `steps/04_cross-verification.md`.
@@ -211,7 +207,7 @@ Read and follow `steps/04_cross-verification.md`.
- **Coding during decomposition**: this workflow produces specs, never code
- **Over-splitting**: don't create many tasks if the component is simple — 1 task is fine
- **Tasks exceeding 5 points**: split them; no task should be too complex for a single implementer
- **Tasks exceeding 8 points**: split them; no task should be too complex for a single implementer
- **Cross-component tasks**: each task belongs to exactly one component
- **Skipping BLOCKING gates**: never proceed past a BLOCKING marker without user confirmation
- **Creating git branches**: branch creation is an implementation concern, not a decomposition one
@@ -224,7 +220,7 @@ Read and follow `steps/04_cross-verification.md`.
| Situation | Action |
|-----------|--------|
| Ambiguous component boundaries | ASK user |
| Task complexity exceeds 5 points after splitting | ASK user |
| Task complexity exceeds 8 points after splitting | ASK user |
| Missing component specs in DOCUMENT_DIR | ASK user |
| Cross-component dependency conflict | ASK user |
| Tracker epic not found for a component | ASK user for Epic ID |
@@ -236,14 +232,15 @@ Read and follow `steps/04_cross-verification.md`.
┌────────────────────────────────────────────────────────────────┐
│ Task Decomposition (Multi-Mode) │
├────────────────────────────────────────────────────────────────┤
│ CONTEXT: Invoke the selected entrypoint (implementation / single / tests-only) │
│ CONTEXT: Resolve mode (default / single component / tests-only) │
│ │
│ IMPLEMENTATION TASK DECOMPOSITION: │
│ DEFAULT MODE: │
│ 1. Bootstrap Structure → steps/01_bootstrap-structure.md │
│ [BLOCKING: user confirms structure] │
│ 1.5 Module Layout → steps/01-5_module-layout.md │
│ [BLOCKING: user confirms layout] │
│ 2. Component Tasks → steps/02_task-decomposition.md │
│ 3. Blackbox Tests → steps/03_blackbox-test-decomposition.md │
│ 4. Cross-Verification → steps/04_cross-verification.md │
│ [BLOCKING: user confirms dependencies] │
│ │
.cursor/skills/decompose/steps/02_task-decomposition.md
+2 -18
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@@ -26,7 +26,7 @@ For each component (or the single provided component):
4. Do not create tasks for other components — only tasks for the current component
5. Each task should be atomic, containing 1 API or a list of semantically connected APIs
6. Write each task spec using `templates/task.md`
7. Estimate complexity per task (1, 2, 3, 5 points); no task should exceed 5 points — split if it does
7. Estimate complexity per task (1, 2, 3, 5, 8 points); no task should exceed 8 points — split if it does
8. Note task dependencies (referencing tracker IDs of already-created dependency tasks, e.g., `AZ-42_initial_structure`)
9. **Cross-cutting rule**: if a concern spans ≥2 components (logging, config loading, auth/authZ, error envelope, telemetry, feature flags, i18n), create ONE shared task under the cross-cutting epic. Per-component tasks declare it as a dependency and consume it; they MUST NOT re-implement it locally. Duplicate local implementations are an `Architecture` finding (High) in code-review Phase 7 and a `Maintainability` finding in Phase 6.
10. **Shared-models / shared-API rule**: classify the task as shared if ANY of the following is true:
@@ -43,32 +43,16 @@ For each component (or the single provided component):
Consumers read the contract file, not the producer's task spec. This prevents interface drift when the producer's implementation detail leaks into consumers.
11. **Immediately after writing each task file**: create a work item ticket, link it to the component's epic, write the work item ticket ID and Epic ID back into the task header, then rename the file from `todo/[##]_[short_name].md` to `todo/[TRACKER-ID]_[short_name].md`.
## Runtime Completeness Decomposition Gate
Before Step 2 is considered complete, scan `architecture.md`, `system-flows.md`, component descriptions, and the solution for named internal runtime capabilities and dependencies. Examples include BASALT/OpenVINS/Kimera, FAISS, DINOv2, ONNX/TensorRT, ALIKED/DISK, LightGlue, RANSAC, PostGIS, MAVLink emission, FDR rollover, and any "A-Z" user-visible pipeline.
For every named internal capability:
1. Ensure at least one implementation task explicitly owns the production integration or production algorithm.
2. Do not treat "define protocol", "create adapter boundary", "add deterministic fallback", "create scaffold", or "prepare native bridge" as implementation of the capability unless the architecture explicitly says the real capability is out of scope.
3. If a capability needs external hardware/data to verify, still create the production implementation task. Verification may be hardware-gated later; implementation must not be omitted.
4. Add a `## Runtime Completeness` section to any affected task with:
- named capability/dependency,
- production code that must exist,
- allowed external stubs, if any,
- unacceptable substitutes such as fake/deterministic/internal stubs.
## Self-verification (per component)
- [ ] Every task is atomic (single concern)
- [ ] No task exceeds 5 complexity points
- [ ] No task exceeds 8 complexity points
- [ ] Task dependencies reference correct tracker IDs
- [ ] Tasks cover all interfaces defined in the component spec
- [ ] No tasks duplicate work from other components
- [ ] Every task has a work item ticket linked to the correct epic
- [ ] Every shared-models / shared-API task has a contract file at `_docs/02_document/contracts/<component>/<name>.md` and a `## Contract` section linking to it
- [ ] Every cross-cutting concern appears exactly once as a shared task, not N per-component copies
- [ ] Every named internal runtime capability has a production implementation task, not only an interface/scaffold/fallback task
## Save action
.cursor/skills/decompose/steps/03_blackbox-test-decomposition.md
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@@ -1,4 +1,4 @@
# Step 3: Blackbox Test Task Decomposition (tests-only mode only)
# Step 3: Blackbox Test Task Decomposition (default and tests-only modes)
**Role**: Professional Quality Assurance Engineer
**Goal**: Decompose blackbox test specs into atomic, implementable task specs.
@@ -6,6 +6,7 @@
## Numbering
- In default mode: continue sequential numbering from where Step 2 left off.
- In tests-only mode: start from 02 (01 is the test infrastructure bootstrap from Step 1t).
## Steps
@@ -13,26 +14,21 @@
1. Read all test specs from `DOCUMENT_DIR/tests/` (`blackbox-tests.md`, `performance-tests.md`, `resilience-tests.md`, `security-tests.md`, `resource-limit-tests.md`)
2. Group related test scenarios into atomic tasks (e.g., one task per test category or per component under test)
3. Each task should reference the specific test scenarios it implements and the environment/test-data specs
4. Add a **System Under Test Boundary** section to every e2e/blackbox test task:
- The test must drive the product through public runtime boundaries and compare actual outputs to `_docs/00_problem/input_data/expected_results/results_report.md` and any referenced machine-readable expected-result files.
- Stubs are allowed only for external systems outside the product boundary: flight controller/SITL, QGC observer, satellite-provider/Suite service, physical Jetson hardware, physical camera, licensed public datasets, and network services.
- Stubs, fakes, deterministic fallbacks, monkeypatches, or direct imports are not allowed for internal product modules that the scenario is meant to validate, such as VIO, safety/anchor wrapper, satellite retrieval, anchor verification, tile manager, MAVLink output adapter, or FDR.
- If an internal module is not implemented, the test must fail/block as missing product implementation; it must not pass by replacing that module with a test stub.
5. Dependencies:
4. Dependencies:
- In default mode: blackbox test tasks depend on the component implementation tasks they exercise
- In tests-only mode: blackbox test tasks depend on the test infrastructure bootstrap task (Step 1t)
6. Write each task spec using `templates/task.md`
7. Estimate complexity per task (1, 2, 3, 5 points); no task should exceed 5 points — split if it does
8. Note task dependencies (referencing tracker IDs of already-created dependency tasks)
9. **Immediately after writing each task file**: create a work item ticket under the "Blackbox Tests" epic, write the work item ticket ID and Epic ID back into the task header, then rename the file from `todo/[##]_[short_name].md` to `todo/[TRACKER-ID]_[short_name].md`.
5. Write each task spec using `templates/task.md`
6. Estimate complexity per task (1, 2, 3, 5, 8 points); no task should exceed 8 points — split if it does
7. Note task dependencies (referencing tracker IDs of already-created dependency tasks)
8. **Immediately after writing each task file**: create a work item ticket under the "Blackbox Tests" epic, write the work item ticket ID and Epic ID back into the task header, then rename the file from `todo/[##]_[short_name].md` to `todo/[TRACKER-ID]_[short_name].md`.
## Self-verification
- [ ] Every scenario from `tests/blackbox-tests.md` is covered by a task
- [ ] Every scenario from `tests/performance-tests.md`, `tests/resilience-tests.md`, `tests/security-tests.md`, and `tests/resource-limit-tests.md` is covered by a task
- [ ] No task exceeds 5 complexity points
- [ ] Dependencies correctly reference the test infrastructure task
- [ ] No task exceeds 8 complexity points
- [ ] Dependencies correctly reference the dependency tasks (component tasks in default mode, test infrastructure in tests-only mode)
- [ ] Every task has a work item ticket linked to the "Blackbox Tests" epic
- [ ] Every e2e/blackbox task forbids internal product stubs/fakes and requires comparison against expected-results artifacts
## Save action
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+4 -8
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@@ -1,4 +1,4 @@
# Step 4: Cross-Task Verification (implementation and tests-only modes)
# Step 4: Cross-Task Verification (default and tests-only modes)
**Role**: Professional software architect and analyst
**Goal**: Verify task consistency and produce `_dependencies_table.md`.
@@ -8,20 +8,17 @@
1. Verify task dependencies across all tasks are consistent
2. Check no gaps:
- In implementation mode: every product interface in `architecture.md` has implementation task coverage
- In default mode: every interface in `architecture.md` has tasks covering it
- In tests-only mode: every test scenario in `traceability-matrix.md` is covered by a task
- In implementation mode: every named internal runtime capability/dependency from architecture, solution, system flows, and component descriptions has a production implementation task, not only an interface/scaffold/fallback task
- In tests-only mode: every e2e/blackbox task has a System Under Test Boundary section that forbids stubbing internal product modules and requires comparison to expected-results artifacts
3. Check no overlaps: tasks don't duplicate work
4. Check no circular dependencies in the task graph
5. Produce `_dependencies_table.md` using `templates/dependencies-table.md`
## Self-verification
### Implementation mode
### Default mode
- [ ] Every product interface in `architecture.md` is covered by at least one implementation task
- [ ] Every named internal runtime capability has a production implementation task
- [ ] Every architecture interface is covered by at least one task
- [ ] No circular dependencies in the task graph
- [ ] Cross-component dependencies are explicitly noted in affected task specs
- [ ] `_dependencies_table.md` contains every task with correct dependencies
@@ -29,7 +26,6 @@
### Tests-only mode
- [ ] Every test scenario from `traceability-matrix.md` "Covered" entries has a corresponding task
- [ ] Every e2e/blackbox task validates actual product behavior and allows stubs only for external systems
- [ ] No circular dependencies in the task graph
- [ ] Test task dependencies reference the test infrastructure bootstrap
- [ ] `_dependencies_table.md` contains every task with correct dependencies
.cursor/skills/decompose/templates/dependencies-table.md
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@@ -28,4 +28,4 @@ Use this template after cross-task verification. Save as `TASKS_DIR/_dependencie
- Dependencies column lists tracker IDs (e.g., "AZ-43, AZ-44") or "None"
- No circular dependencies allowed
- Tasks should be listed in recommended execution order
- The `/implement` skill reads this table to compute dependency-aware batches; task execution remains sequential
- The `/implement` skill reads this table to compute parallel batches
.cursor/skills/decompose/templates/module-layout.md
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@@ -1,6 +1,6 @@
# Module Layout Template
The module layout is the **authoritative file-ownership map** used by the `/implement` skill to assign OWNED / READ-ONLY / FORBIDDEN files to each task. It is derived from `_docs/02_document/architecture.md` and the component specs at `_docs/02_document/components/`, and it follows the target language's standard project-layout conventions.
The module layout is the **authoritative file-ownership map** used by the `/implement` skill to assign OWNED / READ-ONLY / FORBIDDEN files to implementer subagents. It is derived from `_docs/02_document/architecture.md` and the component specs at `_docs/02_document/components/`, and it follows the target language's standard project-layout conventions.
Save as `_docs/02_document/module-layout.md`. This file is produced by the decompose skill (Step 1.5 module layout) and consumed by the implement skill (Step 4 file ownership). Task specs remain purely behavioral — they do NOT carry file paths. The layout is the single place where component → filesystem mapping lives.
@@ -104,4 +104,4 @@ The implement skill's Step 4 (File Ownership) reads this file and, for each task
3. Set READ-ONLY = the Public API files of every component listed in `Imports from`, plus `shared/*` Public API files.
4. Set FORBIDDEN = every other component's Owns glob.
Execution inside a batch is already sequential (one task at a time). This mapping is still required because it enforces scope discipline per task — preventing a task from drifting into files that belong to another component.
If two tasks in the same batch map to the same component, the implement skill schedules them sequentially (one implementer at a time for that component) to avoid file conflicts on shared internal files.
.cursor/skills/decompose/templates/task.md
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@@ -11,7 +11,7 @@ Save as `TASKS_DIR/[##]_[short_name].md` initially, then rename to `TASKS_DIR/[T
**Task**: [TRACKER-ID]_[short_name]
**Name**: [short human name]
**Description**: [one-line description of what this task delivers]
**Complexity**: [1|2|3|5] points
**Complexity**: [1|2|3|5|8] points
**Dependencies**: [AZ-43_shared_models, AZ-44_db_migrations] or "None"
**Component**: [component name for context]
**Tracker**: [TASK-ID]
@@ -102,7 +102,8 @@ Consumers MUST read that file — not this task spec — to discover the interfa
- 2 points: Non-trivial, low complexity, minimal coordination
- 3 points: Multi-step, moderate complexity, potential alignment needed
- 5 points: Difficult, interconnected logic, medium-high risk
- 8+ points: Too complex — split into smaller tasks
- 8 points: High difficulty, high ambiguity or coordination, multiple components
- 13 points: Too complex — split into smaller tasks
## Output Guidelines
.cursor/skills/deploy/steps/02_containerization.md
+1 -2
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@@ -26,8 +26,7 @@
- Application components under test
- Test runner container (black-box, no internal imports)
- Isolated database with seed data
- All tests runnable via `docker compose -f docker-compose.test.yml up --abort-on-container-exit --exit-code-from e2e-runner`
- See the Woodpecker two-workflow contract in [`../templates/ci_cd_pipeline.md`](../templates/ci_cd_pipeline.md) — the test runner entry point defined here becomes the first step of `.woodpecker/01-test.yml`.
- All tests runnable via `docker compose -f docker-compose.test.yml up --abort-on-container-exit`
7. Define image tagging strategy: `<registry>/<project>/<component>:<git-sha>` for CI, `latest` for local dev only
## Self-verification
.cursor/skills/deploy/templates/ci_cd_pipeline.md
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@@ -85,140 +85,3 @@ Save as `_docs/04_deploy/ci_cd_pipeline.md`.
| Deploy success | [Slack] | [team] |
| Deploy failure | [Slack/email + PagerDuty] | [on-call] |
```
---
## Reference Implementation: Woodpecker CI two-workflow contract
Use this when the project's CI is **Woodpecker** and the test layout follows the autodev e2e contract from [`../../decompose/templates/test-infrastructure-task.md`](../../decompose/templates/test-infrastructure-task.md) (an `e2e/` folder containing `Dockerfile`, `docker-compose.test.yml`, `conftest.py`, `requirements.txt`, `mocks/`, `fixtures/`, `tests/`).
The contract is **two workflows in `.woodpecker/`**, scheduled on the same agent label, with the build workflow gated on a successful test run:
- `.woodpecker/01-test.yml` — runs the e2e contract, publishes `results/report.csv` as an artifact, fails the pipeline on any test failure.
- `.woodpecker/02-build-push.yml` — `depends_on: [01-test]`. Builds the image, tags it `${CI_COMMIT_BRANCH}-${TAG_SUFFIX}`, pushes it to the registry. Skipped automatically if test failed.
The agent label is parameterized via `matrix:` so a single workflow file fans out across architectures: `labels: platform: ${PLATFORM}` routes each matrix entry to the matching agent. Both workflows for a repo must use the same matrix so test and build run on the same machine and share Docker layer cache. New architectures = new matrix entries; never new files.
### Multi-arch matrix conventions
| Variable | Meaning | Typical values |
|----------|---------|----------------|
| `PLATFORM` | Woodpecker agent label — selects which physical machine runs the entry. | `arm64`, `amd64` |
| `TAG_SUFFIX` | Image tag suffix appended after the branch name. | `arm`, `amd` |
| `DOCKERFILE` *(only when arches need different Dockerfiles)* | Path to the Dockerfile for this entry. | `Dockerfile`, `Dockerfile.jetson` |
Most repos use the same `Dockerfile` for both arches (multi-arch base images handle the rest), so `DOCKERFILE` can be omitted from the matrix and hardcoded in the build command. Repos with split per-arch Dockerfiles (e.g., `detections` uses `Dockerfile.jetson` on Jetson with TensorRT/CUDA-on-L4T) declare `DOCKERFILE` as a matrix var.
When only one architecture is currently in use, keep the matrix block with a single entry and the second entry commented out — adding a new arch is then a one-line uncomment, not a structural change.
### `.woodpecker/01-test.yml`
```yaml
when:
event: [push, pull_request, manual]
branch: [dev, stage, main]
matrix:
include:
- PLATFORM: arm64
TAG_SUFFIX: arm
# - PLATFORM: amd64
# TAG_SUFFIX: amd
labels:
platform: ${PLATFORM}
steps:
- name: e2e
image: docker
commands:
- cd e2e
- docker compose -f docker-compose.test.yml up --abort-on-container-exit --exit-code-from e2e-runner --build
- docker compose -f docker-compose.test.yml down -v
volumes:
- /var/run/docker.sock:/var/run/docker.sock
- name: report
image: docker
when:
status: [success, failure]
commands:
- test -f e2e/results/report.csv && cat e2e/results/report.csv || echo "no report"
volumes:
- /var/run/docker.sock:/var/run/docker.sock
```
Notes:
- `--abort-on-container-exit` shuts the whole compose down as soon as ANY service exits, so a crashed dependency surfaces immediately instead of hanging the runner.
- `--exit-code-from e2e-runner` ensures the pipeline's exit code reflects the test runner's, not the SUT's.
- The `report` step runs on `[success, failure]` so the report is always published; without this the CSV is lost on red builds.
- `down -v` between runs drops mock state and DB volumes — every test run starts clean.
### `.woodpecker/02-build-push.yml`
```yaml
when:
event: [push, manual]
branch: [dev, stage, main]
depends_on:
- 01-test
matrix:
include:
- PLATFORM: arm64
TAG_SUFFIX: arm
# - PLATFORM: amd64
# TAG_SUFFIX: amd
labels:
platform: ${PLATFORM}
steps:
- name: build-push
image: docker
environment:
REGISTRY_HOST:
from_secret: registry_host
REGISTRY_USER:
from_secret: registry_user
REGISTRY_TOKEN:
from_secret: registry_token
commands:
- echo "$REGISTRY_TOKEN" | docker login "$REGISTRY_HOST" -u "$REGISTRY_USER" --password-stdin
- export TAG=${CI_COMMIT_BRANCH}-${TAG_SUFFIX}
- export BUILD_DATE=$(date -u +%Y-%m-%dT%H:%M:%SZ)
- |
docker build -f Dockerfile \
--build-arg CI_COMMIT_SHA=$CI_COMMIT_SHA \
--label org.opencontainers.image.revision=$CI_COMMIT_SHA \
--label org.opencontainers.image.created=$BUILD_DATE \
--label org.opencontainers.image.source=$CI_REPO_URL \
-t $REGISTRY_HOST/azaion/<service>:$TAG .
- docker push $REGISTRY_HOST/azaion/<service>:$TAG
volumes:
- /var/run/docker.sock:/var/run/docker.sock
```
Notes:
- `depends_on: [01-test]` is enforced by Woodpecker — a failed `01-test` (any matrix entry) skips this workflow.
- The build workflow does NOT trigger on `pull_request` events: PRs get test signal only; pushes to `dev`/`stage`/`main` produce images. Avoids polluting the registry with PR images.
- Replace `<service>` with the actual service name (matches the registry namespace pattern `azaion/<service>`).
- For repos with split per-arch Dockerfiles, add `DOCKERFILE: Dockerfile.jetson` (or similar) to the matrix entry and substitute `${DOCKERFILE}` for `Dockerfile` in the `docker build -f` line.
### Variations by stack
The contract is language-agnostic because the runner is `docker compose`. The Dockerfile inside `e2e/` selects the test framework:
| Stack | `e2e/Dockerfile` runs |
|-------|----------------------|
| Python | `pytest --csv=/results/report.csv -v` |
| .NET | `dotnet test --logger:"trx;LogFileName=/results/report.trx"` (convert to CSV in a final step if needed) |
| Node/UI | `npm test -- --reporters=default --reporters=jest-junit --outputDirectory=/results` |
| Rust | `cargo test --no-fail-fast -- --format json > /results/report.json` |
When the repo has **only unit tests** (no `e2e/docker-compose.test.yml`), drop the compose orchestration and run the native test command directly inside a stack-appropriate image. Keep the same two-workflow split — `01-test.yml` runs unit tests, `02-build-push.yml` is unchanged.
### Manual-trigger override (test infrastructure not yet validated)
If a repo ships a complete `e2e/` layout but the test fixtures are not yet validated end-to-end (e.g., expected-results data is still being authored), gate `01-test.yml` on `event: [manual]` only and add a TODO comment pointing to the unblocking task. The `02-build-push.yml` workflow drops its `depends_on` clause for the manual-only window — an explicit and reversible exception, not a permanent split.
.cursor/skills/document/references/artifacts.md
-2
View File
@@ -31,7 +31,6 @@ _docs/
│ ├── components.md
│ └── flows/
├── 04_verification_log.md # Step 4
├── glossary.md # Step 4.5 (confirmed-by-user)
├── FINAL_report.md # Step 7
└── state.json # Resumability
```
@@ -50,7 +49,6 @@ Maintained in `DOCUMENT_DIR/state.json` for resumability:
"modules_remaining": ["services/auth", "api/endpoints"],
"module_batch": 1,
"components_written": [],
"step_4_5_glossary_vision": "not_started",
"last_updated": "2026-03-21T14:00:00Z"
}
```
.cursor/skills/document/workflows/full.md
+2 -102
View File
@@ -15,7 +15,7 @@ Covers three related modes that share the same 8-step pipeline:
## Progress Tracking
Create a TodoWrite with all steps (0 through 7, including the inline Step 2.5 Module Layout Derivation and Step 4.5 Glossary & Architecture Vision). Update status as each step completes.
Create a TodoWrite with all steps (0 through 7). Update status as each step completes.
## Steps
@@ -251,107 +251,7 @@ Apply corrections inline to the documents that need them.
**BLOCKING**: Present verification summary to user. Do NOT proceed until user confirms corrections are acceptable or requests additional fixes.
---
### Step 4.5: Glossary & Architecture Vision (BLOCKING)
**Role**: Software architect + business analyst
**Goal**: Reconcile the AI's verified understanding of the codebase with the user's intended terminology and architecture vision. Existing-code projects often carry domain language and structural intent that is invisible from code alone (synonyms, deprecated names, modules that are "supposed to" be split, components the user thinks of as one logical unit even though they live in two folders). This step makes that intent explicit before any downstream skill (refactor, decompose, new-task) acts on the docs.
**When this step runs**:
- Always, after Step 4 (Verification Pass) — for Full and Resume modes.
- **Skipped** in Focus Area mode (the glossary/vision is system-wide; running it on a partial scan would produce a partial glossary). Resume the user once a full pass exists.
**Inputs** (already on disk after Step 4):
- `DOCUMENT_DIR/architecture.md`, `system-flows.md`, `data_model.md`, `deployment/*`
- `DOCUMENT_DIR/components/*/description.md`
- `DOCUMENT_DIR/modules/*.md`
- `DOCUMENT_DIR/04_verification_log.md` (so the AI knows which doc parts are confirmed vs. flagged)
**Outputs**:
- `DOCUMENT_DIR/glossary.md` (NEW)
- `DOCUMENT_DIR/architecture.md` updated in place: a new `## Architecture Vision` section is prepended (or merged into an existing "Overview" / "Vision" heading if already present); existing technical sections are preserved verbatim
**Procedure**:
1. **Draft glossary** from verified docs:
- Domain entities, processes, roles named in module/component docs
- Acronyms / abbreviations
- Internal codenames (project, service, model names) that recur in the codebase
- Synonym pairs the AI noticed (e.g., the codebase uses "flight" but module comments say "mission")
- Stakeholder personas if any docs reference them
Each entry: one-line definition + source reference (`source: components/03_flights/description.md`). Skip generic CS/industry terms.
2. **Draft architecture vision** as the AI currently understands the codebase:
- **One paragraph**: what the system is, who runs it, the runtime topology shape (monolith / services / pipeline / library / hybrid), and the dominant pattern (e.g., "submodule-based meta-repo with REST + SSE between UI and backend").
- **Components & responsibilities** (one-line each), pulled from `components/*/description.md`.
- **Major data flows** (one or two sentences each), pulled from `system-flows.md`.
- **Architectural principles / non-negotiables** the AI inferred from the code (e.g., "DB-driven config", "all UI traffic via REST + SSE only", "no per-component shared state"). Mark each with `inferred-from: <source>`.
- **Open questions / drift signals**: places where the code disagrees with itself, or where the AI cannot tell intent from implementation (e.g., two components doing similar work — is that legacy duplication or deliberate?).
3. **Present condensed view** to the user (NOT the full draft files — a synopsis only):
```
══════════════════════════════════════
REVIEW: Glossary + Architecture Vision (existing code)
══════════════════════════════════════
Glossary (N terms drafted from verified docs):
- <Term>: <one-line definition>
- ...
Architecture Vision — as inferred from the codebase:
<one-paragraph synopsis>
Components / responsibilities:
- <component>: <one-line>
- ...
Principles / non-negotiables (inferred):
- <principle> [inferred-from: <source>]
- ...
Open questions / drift signals:
- <q1>
- <q2>
══════════════════════════════════════
A) Inferred vision matches my intent — write the files
B) Add / correct entries (provide diffs — terms, components,
principles, or rename pairs)
C) Resolve the open questions / drift signals first
══════════════════════════════════════
Recommendation: pick C if any drift signals exist;
otherwise B if the vision misses
project-specific intent; A only when
the inferred vision is exactly right.
══════════════════════════════════════
```
4. **Iterate**:
- On B → integrate the user's diffs/additions, re-present, loop until A.
- On C → ask the listed open questions in one batch (M4-style), integrate answers, re-present.
- **Do NOT proceed to step 5 until the user picks A.**
5. **Save**:
- Write `DOCUMENT_DIR/glossary.md`, alphabetical, with a top-line `**Status**: confirmed-by-user` and the date.
- Update `DOCUMENT_DIR/architecture.md`:
- If a `## Architecture Vision` (or `## Vision` / `## Overview`) section already exists at the top, replace its body with the confirmed paragraph + components + principles.
- Otherwise, insert `## Architecture Vision` as the first H2 after the title; preserve every existing H2 below.
- Do NOT delete or re-order existing technical sections (Tech Stack, Deployment Model, Data Model, NFRs, ADRs).
6. **Update `state.json`**: mark `step_4_5_glossary_vision: confirmed`. Resume on rerun must skip this step unless the user explicitly invokes `/document --refresh-vision`.
**Self-verification**:
- [ ] Every glossary entry traces to at least one file under `DOCUMENT_DIR/`
- [ ] Every component listed in the vision matches a folder under `DOCUMENT_DIR/components/`
- [ ] All open questions are answered or explicitly deferred (with the user's acknowledgement)
- [ ] `architecture.md` still contains all H2 sections it had before this step
- [ ] User picked option A on the latest condensed view
**BLOCKING**: Do NOT proceed to the session boundary / Step 5 until both files are saved and the user has picked A.
---
**Session boundary**: After Step 4.5 is confirmed, suggest a session break before proceeding to the synthesis steps (5–7). These steps produce different artifact types and benefit from fresh context:
**Session boundary**: After verification is confirmed, suggest a session break before proceeding to the synthesis steps (5–7). These steps produce different artifact types and benefit from fresh context:
```
══════════════════════════════════════
.cursor/skills/implement/SKILL.md
+56 -161
View File
@@ -1,59 +1,41 @@
---
name: implement
description: |
Implement tasks sequentially with dependency-aware batching and integrated code review.
Orchestrate task implementation with dependency-aware batching, parallel subagents, and integrated code review.
Reads flat task files and _dependencies_table.md from TASKS_DIR, computes execution batches via topological sort,
implements tasks one at a time in dependency order, runs code-review skill after each batch, and loops until done.
launches up to 4 implementer subagents in parallel, runs code-review skill after each batch, and loops until done.
Use after /decompose has produced task files.
Trigger phrases:
- "implement", "start implementation", "implement tasks"
- "execute tasks"
- "run implementers", "execute tasks"
category: build
tags: [implementation, batching, code-review]
tags: [implementation, orchestration, batching, parallel, code-review]
disable-model-invocation: true
---
# Implementation Runner
# Implementation Orchestrator
Implement all tasks produced by the `/decompose` skill. This skill reads task specs, computes execution order, writes the code and tests for each task **sequentially** (no subagents, no parallel execution), validates results via the `/code-review` skill, and escalates issues.
Orchestrate the implementation of all tasks produced by the `/decompose` skill. This skill is a **pure orchestrator** — it does NOT write implementation code itself. It reads task specs, computes execution order, delegates to `implementer` subagents, validates results via the `/code-review` skill, and escalates issues.
For each task the main agent receives a task spec, analyzes the codebase, implements the feature, writes tests, and verifies acceptance criteria — then moves on to the next task.
The `implementer` agent is the specialist that writes all the code — it receives a task spec, analyzes the codebase, implements the feature, writes tests, and verifies acceptance criteria.
## Core Principles
- **Sequential execution**: implement one task at a time. Do NOT spawn subagents and do NOT run tasks in parallel. (See `.cursor/rules/no-subagents.mdc`.)
- **Dependency-aware ordering**: tasks run only when all their dependencies are satisfied
- **Batching for review, not parallelism**: tasks are grouped into batches so `/code-review` and commits operate on a coherent unit of work — all tasks inside a batch are still implemented one after the other
- **Orchestrate, don't implement**: this skill delegates all coding to `implementer` subagents
- **Dependency-aware batching**: tasks run only when all their dependencies are satisfied
- **Max 4 parallel agents**: never launch more than 4 implementer subagents simultaneously
- **File isolation**: no two parallel agents may write to the same file
- **Integrated review**: `/code-review` skill runs automatically after each batch
- **Completeness before testing**: product implementation is not done until code is checked against task outcomes, included scope, architecture/component promises, named runtime dependencies, and unresolved scaffold/native placeholders — not just task AC tests
- **Runtime dependency reality**: production code cannot satisfy a task by exposing only a protocol, fake runner, deterministic fallback, or "native bridge" placeholder when the task/architecture promises a concrete internal capability such as BASALT VIO, FAISS retrieval, LightGlue matching, or a full A-Z localization pipeline. Stubs are allowed only for external systems and tests.
- **Auto-start**: batches start immediately — no user confirmation before a batch
- **Auto-start**: batches launch immediately — no user confirmation before a batch
- **Gate on failure**: user confirmation is required only when code review returns FAIL
- **Commit per batch**: after each batch is confirmed, commit. Ask the user whether to push to remote unless the user previously opted into auto-push for this session.
## Context Resolution
- TASKS_DIR: `_docs/02_tasks/`
- Task files: selected `*.md` files in `TASKS_DIR/todo/` (excluding files starting with `_`)
- Task files: all `*.md` files in `TASKS_DIR/todo/` (excluding files starting with `_`)
- Dependency table: `TASKS_DIR/_dependencies_table.md`
### Task Selection Context
The invoking flow decides which task category this run should execute. The implement skill must honor that selected context instead of consuming every file in `todo/`.
| Context | Selected task files |
|---------|---------------------|
| Product implementation | Task specs that are not test-only and not refactoring specs |
| Test implementation | `*_test_infrastructure.md` plus task specs whose `Component` or `Epic` identifies `Blackbox Tests` |
| Refactoring | Task specs whose filename or task ID includes `_refactor_` |
If no explicit context is provided, infer it from the active autodev step:
- greenfield Step 7 or existing-code Step 10 → Product implementation
- greenfield Step 10 or existing-code Step 6 → Test implementation
- refactor Phase 4 → Refactoring
Unselected task files remain in `TASKS_DIR/todo/` for their later flow step.
### Task Lifecycle Folders
```
@@ -64,31 +46,9 @@ TASKS_DIR/
└── done/ ← completed tasks (moved here after implementation)
```
### Suite-level invocation context (meta-repo flow)
When invoked from `.cursor/skills/autodev/flows/meta-repo.md` Step 3.5 (or any caller that supplies the same context envelope), the skill receives:
```
suite_level: true
TASKS_DIR: <override> # e.g., _docs/tasks/ (vs. default _docs/02_tasks/)
module_layout_path: <override> # e.g., _docs/tasks/_suite_module_layout.md
```
When `suite_level: true` is present, the following gate adjustments apply — and ONLY these. All other steps (1–14, 16) execute unchanged:
1. **TASKS_DIR override** is honored throughout the skill (Step 1 Parse, Step 13 Archive, Step 15 input paths if it ran). Default `_docs/02_tasks/` is replaced by the supplied path.
2. **module_layout_path override** is read instead of the hardcoded `_docs/02_document/module-layout.md` in Step 4 (Assign File Ownership). The supplied file uses the same `Per-Component Mapping` schema. If both the override and the hardcoded path are missing, behavior is unchanged from default mode (STOP and instruct).
3. **Step 14.5 (Cumulative Code Review) — SKIPPED**. The meta-repo has no `_docs/02_document/architecture_compliance_baseline.md`; cross-task drift is captured by the next `monorepo-status` cycle instead.
4. **Step 15 (Product Implementation Completeness Gate) — SKIPPED**. The gate's hard inputs (`_docs/02_document/architecture.md`, `system-flows.md`, `components/*/description.md`) do not exist in the meta-repo artifact layout. Suite-level tasks are infrastructure / coordination work (renames, cross-repo edits, suite-root infra additions), not feature implementation; the equivalent completeness signal is the next `monorepo-status` drift report (which the meta-repo flow re-runs immediately after Step 3.5 returns).
5. **Final report filename**: `_docs/03_implementation/suite_implementation_report_{run_name}.md` (in addition to the existing feature/test/refactor variants). Batch reports follow `_docs/03_implementation/suite_batch_{NN}_report.md`.
6. **Tracker integration** (Step 5: In Progress, Step 12: In Testing) runs unchanged — suite-level tickets follow the same tracker rules as any other.
Without `suite_level: true`, none of these adjustments apply and the skill runs exactly as documented in default mode.
## Prerequisite Checks (BLOCKING)
1. `TASKS_DIR/todo/` exists and contains at least one task file for the selected context — **STOP if missing**
- Exception for Product implementation re-entry: if no selected product tasks remain in `todo/`, but the active autodev state is Step 7 or the latest product completeness report is missing/invalid/contains `FAIL`, skip directly to Step 15 (Product Implementation Completeness Gate). This gate may create remediation tasks and return to Step 1. Do not write a final implementation report from this state.
1. `TASKS_DIR/todo/` exists and contains at least one task file — **STOP if missing**
2. `_dependencies_table.md` exists — **STOP if missing**
3. At least one task is not yet completed — **STOP if all done**
4. **Working tree is clean** — run `git status --porcelain`; the output must be empty.
@@ -96,16 +56,16 @@ Without `suite_level: true`, none of these adjustments apply and the skill runs
- A) Commit or stash stray changes manually, then re-invoke `/implement`
- B) Agent commits stray changes as a single `chore: WIP pre-implement` commit and proceeds
- C) Abort
- Rationale: each batch ends with a commit. Unrelated uncommitted changes would get silently folded into batch commits otherwise.
- Rationale: implementer subagents edit files in parallel and commit per batch. Unrelated uncommitted changes get silently folded into batch commits otherwise.
- This check is repeated at the start of each batch iteration (see step 6 / step 14 Loop).
## Algorithm
### 1. Parse
- Read selected task `*.md` files from `TASKS_DIR/todo/` (excluding files starting with `_`)
- Read all task `*.md` files from `TASKS_DIR/todo/` (excluding files starting with `_`)
- Read `_dependencies_table.md` — parse into a dependency graph (DAG)
- Validate: no circular dependencies in the selected task graph, all referenced selected-task dependencies exist or are already completed in `TASKS_DIR/done/`
- Validate: no circular dependencies, all referenced dependencies exist
### 2. Detect Progress
@@ -118,23 +78,22 @@ Without `suite_level: true`, none of these adjustments apply and the skill runs
- Topological sort remaining tasks
- Select tasks whose dependencies are ALL satisfied (completed)
- A batch is simply a coherent group of tasks for review + commit. Within the batch, tasks are implemented sequentially in topological order.
- Cap the batch size at a reasonable review scope (default: 4 tasks)
- If a ready task depends on any task currently being worked on in this batch, it must wait for the next batch
- Cap the batch at 4 parallel agents
- If the batch would exceed 20 total complexity points, suggest splitting and let the user decide
### 4. Assign File Ownership
The authoritative file-ownership map is `_docs/02_document/module-layout.md` (produced by the decompose skill's Step 1.5), unless `suite_level: true` was supplied in the invocation context — in which case the `module_layout_path` override is read instead (see "Suite-level invocation context" above). Task specs are purely behavioral — they do NOT carry file paths. Derive ownership from the layout, not from the task spec's prose.
The authoritative file-ownership map is `_docs/02_document/module-layout.md` (produced by the decompose skill's Step 1.5). Task specs are purely behavioral — they do NOT carry file paths. Derive ownership from the layout, not from the task spec's prose.
For each task in the batch:
- Read the task spec's **Component** field.
- Look up the component in `_docs/02_document/module-layout.md` → Per-Component Mapping.
- Set **OWNED** = the component's `Owns` glob (the files this task is allowed to write).
- Set **OWNED** = the component's `Owns` glob (exclusive write for the duration of the batch).
- Set **READ-ONLY** = Public API files of every component in the component's `Imports from` list, plus all `shared/*` Public API files.
- Set **FORBIDDEN** = every other component's `Owns` glob, and every other component's internal (non-Public API) files.
- If the task is a shared / cross-cutting task (lives under `shared/*`), OWNED = that shared directory; READ-ONLY = nothing; FORBIDDEN = every component directory.
Since execution is sequential, there is no parallel-write conflict to resolve; ownership here is a **scope discipline** check — it stops a task from drifting into unrelated components even when alone.
- If two tasks in the same batch map to the same component or overlapping `Owns` globs, schedule them sequentially instead of in parallel.
If `_docs/02_document/module-layout.md` is missing or the component is not found:
- STOP the batch.
@@ -143,30 +102,31 @@ If `_docs/02_document/module-layout.md` is missing or the component is not found
### 5. Update Tracker Status → In Progress
For each task in the batch, transition its ticket status to **In Progress** via the configured work item tracker (see `protocols.md` for tracker detection) before starting work. If `tracker: local`, skip this step. If a tracker operation fails unexpectedly, follow `.cursor/rules/tracker.mdc`.
For each task in the batch, transition its ticket status to **In Progress** via the configured work item tracker (see `protocols.md` for tracker detection) before launching the implementer. If `tracker: local`, skip this step.
### 6. Implement Tasks Sequentially
### 6. Launch Implementer Subagents
**Per-batch dirty-tree re-check**: before starting the batch, run `git status --porcelain`. On the first batch this is guaranteed clean by the prerequisite check. On subsequent batches, the previous batch ended with a commit so the tree should still be clean. If the tree is dirty at this point, STOP and surface the dirty files to the user using the same A/B/C choice as the prerequisite check. The most likely causes are a failed commit in the previous batch, a user who edited files mid-loop, or a pre-commit hook that re-wrote files and was not captured.
**Per-batch dirty-tree re-check**: before launching subagents, run `git status --porcelain`. On the first batch this is guaranteed clean by the prerequisite check. On subsequent batches, the previous batch ended with a commit so the tree should still be clean. If the tree is dirty at this point, STOP and surface the dirty files to the user using the same A/B/C choice as the prerequisite check. The most likely causes are a failed commit in the previous batch, a user who edited files mid-loop, or a pre-commit hook that re-wrote files and was not captured.
For each task in the batch **in topological order, one at a time**:
1. Read the task spec file.
2. Respect the file-ownership envelope computed in Step 4 (OWNED / READ-ONLY / FORBIDDEN).
3. Implement the feature and write/update tests for every acceptance criterion in the spec. Tests for internal product behavior must exercise the production implementation path. If a test cannot run in the current environment (e.g., TensorRT requires GPU), the test must still exist and skip/block with a clear prerequisite reason, but that skip does not make missing production code complete.
4. Run the relevant tests locally before moving on to the next task in the batch. If tests fail, fix in-place — do not defer.
5. Capture a short per-task status line (files changed, tests pass/fail, any blockers) for the batch report.
For each task in the batch, launch an `implementer` subagent with:
- Path to the task spec file
- List of files OWNED (exclusive write access)
- List of files READ-ONLY
- List of files FORBIDDEN
- **Explicit instruction**: the implementer must write or update tests that validate each acceptance criterion in the task spec. If a test cannot run in the current environment (e.g., TensorRT requires GPU), the test must still be written and skip with a clear reason.
Do NOT spawn subagents and do NOT attempt to implement two tasks simultaneously, even if they touch disjoint files. See `.cursor/rules/no-subagents.mdc`.
Launch all subagents immediately — no user confirmation.
### 7. Collect Status
### 7. Monitor
- After all tasks in the batch are finished, aggregate the per-task status lines into a structured batch status.
- If any task reported "Blocked", log the blocker with the failing task's ID and continue — the batch report will surface it.
- Wait for all subagents to complete
- Collect structured status reports from each implementer
- If any implementer reports "Blocked", log the blocker and continue with others
**Stuck detection** — while implementing a task, watch for these signals in your own progress:
- The same file has been rewritten 3+ times without tests going green → stop, mark the task Blocked, and move to the next task in the batch (the user will be asked at the end of the batch).
- You have tried 3+ distinct approaches without evidence-driven progress → stop, mark Blocked, move on.
- Do NOT loop indefinitely on a single task. Record the blocker and proceed.
**Stuck detection** — while monitoring, watch for these signals per subagent:
- Same file modified 3+ times without test pass rate improving → flag as stuck, stop the subagent, report as Blocked
- Subagent has not produced new output for an extended period → flag as potentially hung
- If a subagent is flagged as stuck, do NOT let it continue looping — stop it and record the blocker in the batch report
### 8. AC Test Coverage Verification
@@ -179,8 +139,8 @@ Before code review, verify that every acceptance criterion in each task spec has
- **Not covered**: no test exists for this AC
If any AC is **Not covered**:
- This is a **BLOCKING** failure — the missing test must be written before proceeding
- Go back to the offending task, add tests for the specific ACs that lack coverage, then re-run this check
- This is a **BLOCKING** failure — the implementer must write the missing test before proceeding
- Re-launch the implementer with the specific ACs that need tests
- If the test cannot run in the current environment (GPU required, platform-specific, external service), the test must still exist and skip with `pytest.mark.skipif` or `pytest.skip()` explaining the prerequisite
- A skipped test counts as **Covered** — the test exists and will run when the environment allows
@@ -229,22 +189,18 @@ Track `auto_fix_attempts` and `escalated_findings` in the batch report for retro
### 12. Update Tracker Status → In Testing
After the batch is committed (and pushed if the user approved pushing), transition the ticket status of each task in the batch to **In Testing** via the configured work item tracker. If `tracker: local`, skip this step. If a tracker operation fails unexpectedly, follow `.cursor/rules/tracker.mdc`.
After the batch is committed and pushed, transition the ticket status of each task in the batch to **In Testing** via the configured work item tracker. If `tracker: local`, skip this step.
### 13. Archive Completed Tasks
Move each completed task file from `TASKS_DIR/todo/` to `TASKS_DIR/done/`.
For product implementation, this archive means "batch implementation accepted." The Product Implementation Completeness Gate can still require follow-up remediation tasks before the feature is complete; it does not move original task files back to `todo/`.
### 14. Loop
- Go back to step 2 until all tasks in `todo/` are done
### 14.5. Cumulative Code Review (every K batches)
**Skipped entirely when `suite_level: true`** (see "Suite-level invocation context" above) — the meta-repo has no `architecture_compliance_baseline.md` to evaluate against; cross-task drift is captured by the next `monorepo-status` cycle.
- **Trigger**: every K completed batches (default `K = 3`; configurable per run via a `cumulative_review_interval` knob in the invocation context)
- **Purpose**: per-batch review (Step 9) catches batch-local issues; cumulative review catches issues that only appear when tasks are combined — architecture drift, cross-task inconsistency, duplicate symbols introduced across different batches, contracts that drifted across producer/consumer batches
- **Scope**: the union of files changed since the **last** cumulative review (or since the start of the run if this is the first)
@@ -260,81 +216,22 @@ For product implementation, this archive means "batch implementation accepted."
- **Interaction with Auto-Fix Gate**: Architecture findings (new category from code-review Phase 7) always escalate per the implement auto-fix matrix; they cannot silently auto-fix
- **Resumability**: if interrupted, the next invocation checks for the latest `cumulative_review_batches_*.md` and computes the changed-file set from batch reports produced after that review
### 15. Product Implementation Completeness Gate
### 15. Final Test Run
Run this gate after all **product implementation** tasks are complete and before writing any final product implementation report or allowing autodev to proceed to testability/test decomposition. Skip this gate when (a) the remaining context is explicitly test implementation or refactoring (as determined by the task files and report filename rules), OR (b) `suite_level: true` was supplied in the invocation context (the gate's inputs do not exist in the meta-repo artifact layout — see "Suite-level invocation context" above).
**Goal**: catch the failure mode where narrow tests validate scaffold behavior while the task's actual outcome, included scope, architecture promise, or named integration remains unimplemented.
Inputs:
- Completed product task specs from `_docs/02_tasks/done/` for the current cycle
- `_docs/02_document/architecture.md`
- `_docs/02_document/system-flows.md`
- Relevant `_docs/02_document/components/*/description.md` files
- Current source code under each completed task's ownership envelope
- Batch reports and code-review reports for the current cycle
For each completed product task:
1. Read these sections from the task spec: `Description`, `Outcome`, `Scope / Included`, `Acceptance Criteria`, `Non-Functional Requirements`, `Constraints`, and explicit named technologies or integrations.
2. Compare those promises against actual source code, not only tests or report prose.
3. Search the task's owned component files for unresolved implementation markers: `placeholder`, `stub`, `reserved`, `TODO`, `NotImplemented`, `pass`, `deterministic`, `fake`, `mock`, `scaffold`, `native bridge`, and empty native/readme-only integration directories. Ignore test fixtures/mocks only when they are under test-owned paths and not used as production behavior.
4. Verify that each named runtime dependency in the task promise is integrated as production behavior, not merely represented by an interface. Examples: if a task promises FAISS, DINOv2, BASALT, LightGlue, OpenCV, RANSAC, a database, cloud service, or hardware SDK, the production code must either call that dependency or contain an adapter that loads and executes the real dependency package. A deterministic fallback, fake runner, empty `native/` package, or "bridge to be supplied later" is **FAIL** unless the task itself explicitly scoped the dependency out before implementation started.
5. Distinguish internal implementation from external prerequisites:
- Internal product capabilities (VIO, anchor verification, cache retrieval, safety wrapper, FDR, MAVLink emission) must be implemented in production code before the task can pass.
- External systems/hardware/data (Jetson device, physical camera, ArduPilot process, QGC, third-party service credentials, unavailable licensed dataset) may be `BLOCKED` only when production code exists and the missing prerequisite is outside the product boundary.
6. Verify tests exercise the real implementation path where local prerequisites exist. Environment-gated tests may skip only with an explicit prerequisite reason; they do not make missing production code complete.
7. For any architecture promise that describes an end-to-end user outcome, verify there is an executable production pipeline connecting the relevant components. Isolated component contracts and test-only harness orchestration are not enough.
8. Classify each task:
- **PASS**: task promises are implemented or explicitly out of scope in the task itself.
- **BLOCKED**: production code exists but cannot be fully verified due to external hardware/data/license/runtime prerequisites; the blocker is explicit and tests report blocked/skipped with reason.
- **FAIL**: promised production behavior is missing, only scaffolded, or only represented in tests/reports.
Save the audit to `_docs/03_implementation/implementation_completeness_cycle[N]_report.md` with:
- Per-task classification
- Evidence files/symbols checked
- Any unresolved scaffold/native placeholders
- Any named promised technologies not integrated
- Required remediation task suggestions, each sized to 5 points or less
Gate:
- If every product task is `PASS` or `BLOCKED` with explicit prerequisite evidence, continue to Final Test Run.
- If any product task is `FAIL`, STOP. Do not write the final product implementation report and do not proceed to any downstream autodev step. Completed original task files remain in `done/`; the missing work is represented by remediation tasks. Present a Choose block:
- A) Create remediation tasks now and return to implementation
- B) Mark the missing behavior explicitly out of scope in task/docs, then re-run this gate
- C) Abort for manual correction
- Recommendation must normally be A unless the user deliberately accepts reduced scope.
Remediation task creation:
1. For each `FAIL`, create one or more task specs using `.cursor/skills/decompose/templates/task.md`; each remediation task must be sized at 5 points or less.
2. Save each task to `_docs/02_tasks/todo/` with a short name prefixed by `remediate_`.
3. Set **Component** to the failed task's component and set **Dependencies** to the failed task ID plus any remediation prerequisites.
4. Create or defer tracker tickets using the same tracker rules as decompose/new-task: if tracker is available, create tickets immediately; if the user explicitly chose `tracker: local`, keep numeric prefixes with `Tracker: pending` / `Epic: pending`.
5. Append the remediation tasks to `_docs/02_tasks/_dependencies_table.md`.
6. Return to Step 1 (Parse) in **Product implementation** context. The final product implementation report can be written only after remediation tasks complete and this gate reruns without `FAIL`.
### 16. Final Test Run
- After all batches are complete, run the full test suite once unless the invoking flow's immediate next step is `Run Tests`.
- If the next flow step is `Run Tests`, record a handoff in the final implementation report and let `.cursor/skills/test-run/SKILL.md` own the full-suite gate to avoid duplicate full runs.
- When this step does run, read and execute `.cursor/skills/test-run/SKILL.md` (detect runner, run suite, diagnose failures, present blocking choices).
- Test failures are a **blocking gate** — do not proceed until the test-run skill completes with a user decision.
- When tests pass, report final summary.
- After all batches are complete, run the full test suite once
- Read and execute `.cursor/skills/test-run/SKILL.md` (detect runner, run suite, diagnose failures, present blocking choices)
- Test failures are a **blocking gate** — do not proceed until the test-run skill completes with a user decision
- When tests pass, report final summary
## Batch Report Persistence
After each batch completes, save the batch report to `_docs/03_implementation/batch_[NN]_cycle[N]_report.md` for feature implementation (or `batch_[NN]_report.md` for test/refactor runs). Create the directory if it doesn't exist. For product implementation, produce the FINAL implementation report only after the Product Implementation Completeness Gate passes. For test and refactor implementation, produce the FINAL report after all selected tasks complete and the full-suite gate is either run or handed off per Step 16. The filename depends on context:
After each batch completes, save the batch report to `_docs/03_implementation/batch_[NN]_cycle[N]_report.md` for feature implementation (or `batch_[NN]_report.md` for test/refactor runs). Create the directory if it doesn't exist. When all tasks are complete, produce a FINAL implementation report with a summary of all batches. The filename depends on context:
- **Test implementation** (tasks from test decomposition): `_docs/03_implementation/implementation_report_tests.md`
- **Feature implementation**: `_docs/03_implementation/implementation_report_{feature_slug}_cycle{N}.md` where `{feature_slug}` is derived from the batch task names (e.g., `implementation_report_core_api_cycle2.md`) and `{N}` is the current `state.cycle` from `_docs/_autodev_state.md`. If `state.cycle` is absent (pre-migration), default to `cycle1`.
- **Refactoring**: `_docs/03_implementation/implementation_report_refactor_{run_name}.md`
- **Suite-level** (when `suite_level: true` was supplied — see "Suite-level invocation context" above): `_docs/03_implementation/suite_implementation_report_{run_name}.md`. Batch reports use `_docs/03_implementation/suite_batch_{NN}_report.md`. `{run_name}` is derived from the batch task IDs (e.g., `suite_implementation_report_az543_az549_az550.md`).
Determine the context from the task files being implemented: if all tasks have test-related names or belong to a test epic, use the tests filename; if `suite_level: true` was supplied, use the suite filename; otherwise derive the feature slug from the component names and append the cycle suffix.
Determine the context from the task files being implemented: if all tasks have test-related names or belong to a test epic, use the tests filename; otherwise derive the feature slug from the component names and append the cycle suffix.
Batch report filenames must also include the cycle counter when running feature implementation: `_docs/03_implementation/batch_{NN}_cycle{N}_report.md` (test and refactor runs may use the plain `batch_{NN}_report.md` form since they are not cycle-scoped).
@@ -367,10 +264,9 @@ After each batch, produce a structured report:
| Situation | Action |
|-----------|--------|
| Same task rewritten 3+ times without green tests | Mark Blocked, continue batch, escalate at batch end |
| Implementer fails same approach 3+ times | Stop it, escalate to user |
| Task blocked on external dependency (not in task list) | Report and skip |
| File ownership violated (task wrote outside OWNED) | ASK user |
| Product completeness gate finds missing promised implementation | STOP — create remediation tasks or get explicit user scope reduction |
| File ownership conflict unresolvable | ASK user |
| Test failure after final test run | Delegate to test-run skill — blocking gate |
| All tasks complete | Report final summary, suggest final commit |
| `_dependencies_table.md` missing | STOP — run `/decompose` first |
@@ -385,8 +281,7 @@ Each batch commit serves as a rollback checkpoint. If recovery is needed:
## Safety Rules
- Never start a task whose dependencies are not yet completed
- Never run tasks in parallel and never spawn subagents — see `.cursor/rules/no-subagents.mdc`
- If a task is flagged as stuck, stop working on it and report — do not let it loop indefinitely
- Always run the Product Implementation Completeness Gate before final product reports
- Always run or hand off the full test suite after all batches complete (step 16)
- Never launch tasks whose dependencies are not yet completed
- Never allow two parallel agents to write to the same file
- If a subagent fails or is flagged as stuck, stop it and report — do not let it loop indefinitely
- Always run the full test suite after all batches complete (step 15)
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@@ -3,31 +3,29 @@
## Topological Sort with Batch Grouping
The `/implement` skill uses a topological sort to determine execution order,
then groups tasks into batches for code review and commit. Execution within a
batch is **sequential** — see `.cursor/rules/no-subagents.mdc`.
then groups tasks into batches for parallel execution.
## Algorithm
1. Build adjacency list from `_dependencies_table.md`
2. Compute in-degree for each task node
3. Initialize the ready set with all nodes that have in-degree 0
3. Initialize batch 0 with all nodes that have in-degree 0
4. For each batch:
a. Select up to 4 tasks from the ready set (default batch size cap)
b. Implement the selected tasks one at a time in topological order
c. When all tasks in the batch complete, remove them from the graph and
decrement in-degrees of dependents
d. Add newly zero-in-degree nodes to the ready set
a. Select up to 4 tasks from the ready set
b. Check file ownership — if two tasks would write the same file, defer one to the next batch
c. Launch selected tasks as parallel implementer subagents
d. When all complete, remove them from the graph and decrement in-degrees of dependents
e. Add newly zero-in-degree nodes to the next batch's ready set
5. Repeat until the graph is empty
## Ordering Inside a Batch
## File Ownership Conflict Resolution
Tasks inside a batch are executed in topological order — a task is only
started after every task it depends on (inside the batch or in a previous
batch) is done. When two tasks have the same topological rank, prefer the
lower-numbered (more foundational) task first.
When two tasks in the same batch map to overlapping files:
- Prefer to run the lower-numbered task first (it's more foundational)
- Defer the higher-numbered task to the next batch
- If both have equal priority, ask the user
## Complexity Budget
Each batch should not exceed 20 total complexity points.
If it does, split the batch and let the user choose which tasks to include.
The budget exists to keep the per-batch code review scope reviewable.
.cursor/skills/monorepo-discover/SKILL.md
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@@ -129,8 +129,7 @@ If `_docs/_repo-config.yaml` already exists:
- Entries removed (component removed from registry)
4. **Ask the user** whether to apply the diff.
5. If applied, **preserve `confirmed: true` flags** for entries that still match — don't reset human-approved mappings.
6. **Preserve user-owned top-level keys verbatim**: `glossary_doc:` (written by autodev meta-repo Step 2.5) and any `assumptions_log:` entries are NEVER edited or removed by this skill. Carry them through unchanged. If the file referenced by `glossary_doc:` no longer exists on disk, surface as an `unresolved:` question — do not auto-clear the field.
7. If user declines, stop — leave config untouched.
6. If user declines, stop — leave config untouched.
### Phase 8: Batch question checkpoint (M4)
.cursor/skills/monorepo-document/SKILL.md
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@@ -15,8 +15,6 @@ Propagates component changes into the unified documentation set. Strictly scoped
| Root `README.md` **only** if `_repo-config.yaml` lists it as a doc target (e.g., services table) | Install scripts (`ci-*.sh`) → use `monorepo-cicd` |
| Docs index (`_docs/README.md` or similar) cross-reference tables | Component-internal docs (`<component>/README.md`, `<component>/docs/*`) |
| Cross-cutting docs listed in `docs.cross_cutting` | `_docs/_repo-config.yaml` itself (only `monorepo-discover` and `monorepo-onboard` write it) |
| Body of cross-cutting docs **except** the `## Architecture Vision` section (preserved verbatim — owned by autodev meta-repo Step 2.5) | The file at `glossary_doc:` (user-confirmed; only autodev meta-repo Step 2.5 rewrites it). New project terms surfaced during sync are reported back to the user, not silently appended |
| `## Architecture Vision` body — read-only, may be referenced for terminology consistency but never edited | — |
If a component change requires CI/env updates too, tell the user to also run `monorepo-cicd`. This skill does NOT cross domains.
@@ -168,8 +166,6 @@ Append to `_docs/_repo-config.yaml` under `assumptions_log:`:
- Change `confirmed_by_user` or any `confirmed: <bool>` flag
- Auto-commit or push
- Guess a mapping not in the config
- Edit `glossary_doc:` (the file recorded under the config's `glossary_doc:` key)
- Edit the `## Architecture Vision` section of any cross-cutting doc; if a sync would conflict with that section, surface the conflict to the user and skip — do not silently rewrite user-confirmed content
## Edge cases
.cursor/skills/monorepo-e2e/SKILL.md
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@@ -1,152 +0,0 @@
---
name: monorepo-e2e
description: Syncs the suite-level integration e2e harness (`e2e/docker-compose.suite-e2e.yml`, fixtures, Playwright runner) when component contracts drift in ways that affect the cross-service scenario. Reads `_docs/_repo-config.yaml` to know which suite-e2e artifacts are in play. Touches ONLY suite-e2e files — never per-component CI, docs, or component internals. Use when a component changes a port, env var, public API endpoint, DB schema column, or detection model that the suite e2e exercises.
---
# Monorepo Suite-E2E
Propagates component changes into the suite-level integration e2e harness. Strictly scoped — never edits docs, component internals, per-component CI configs, or the production deploy compose.
## Scope — explicit
| In scope | Out of scope |
| -------- | ------------ |
| `e2e/docker-compose.suite-e2e.yml` (overlay, healthchecks, seed services) | Production `_infra/deploy/<target>/docker-compose.yml` — `monorepo-cicd` owns it |
| `e2e/fixtures/init.sql` (seeded rows that the spec depends on) | Component DB migrations — owned by each component |
| `e2e/fixtures/expected_detections.json` (detection baseline) | Detection model itself — owned by `detections/` |
| `e2e/runner/tests/*.spec.ts` selector / contract-driven edits | New scenarios (user-driven, not drift-driven) |
| `e2e/runner/Dockerfile` / `package.json` Playwright version bumps | Net-new e2e infrastructure (use `monorepo-onboard` or initial scaffolding) |
| `.woodpecker/suite-e2e.yml` (suite-level pipeline) | Per-component `.woodpecker/01-test.yml` / `02-build-push.yml` — `monorepo-cicd` owns those |
| Suite-e2e leftover entries under `_docs/_process_leftovers/` | Per-component leftovers — owned by each component |
If a component change needs doc updates too, tell the user to also run `monorepo-document`. If it needs production-deploy or per-component CI updates, run `monorepo-cicd`. This skill **only** updates the suite-e2e surface.
## Preconditions (hard gates)
1. `_docs/_repo-config.yaml` exists.
2. Top-level `confirmed_by_user: true`.
3. `suite_e2e.*` section is populated in config (see "Required config block" below). If absent, abort and ask the user to extend the config via `monorepo-discover`.
4. Components-in-scope have confirmed contract mappings (port, public API path, DB tables touched), OR user explicitly approves inferred ones.
## Required config block
This skill expects `_docs/_repo-config.yaml` to carry:
```yaml
suite_e2e:
overlay: e2e/docker-compose.suite-e2e.yml
fixtures:
init_sql: e2e/fixtures/init.sql
baseline_json: e2e/fixtures/expected_detections.json
binary_fixtures:
- e2e/fixtures/sample.mp4
- e2e/fixtures/model.tar.gz
runner:
dockerfile: e2e/runner/Dockerfile
package_json: e2e/runner/package.json
spec_dir: e2e/runner/tests
pipeline: .woodpecker/suite-e2e.yml
scenario:
description: "Upload video → detect → overlays → dataset → DB persistence"
components_exercised:
- ui
- annotations
- detections
- postgres-local
api_contracts:
- component: ui
path: /api/admin/auth/login
- component: annotations
path: /api/annotations/media/batch
- component: annotations
path: /api/annotations/media/{id}/annotations
db_tables:
- media
- annotations
- detection
- detection_classes
model_pin:
detections_repo_path: <path-to-model-config-or-classes-source>
classes_source: annotations/src/Database/DatabaseMigrator.cs
```
If `suite_e2e:` is missing the skill **stops** — it does not invent a default mapping.
## Mitigations (M1–M7)
- **M1** Separation: this skill only touches suite-e2e files; no production deploy compose, no per-component CI, no docs, no component internals.
- **M3** Factual vs. interpretive: port, env var, API path, DB column — FACTUAL, read from the components' code. Whether a baseline still matches the model — DEFERRED to the user (the skill flags drift, never silently re-records).
- **M4** Batch questions at checkpoints.
- **M5** Skip over guess: a component change that doesn't map cleanly to one of the in-scope artifacts → skip and report.
- **M6** Assumptions footer + append to `_repo-config.yaml` `assumptions_log`.
- **M7** Drift detection: verify every path under `suite_e2e.*` exists on disk; stop if not.
## Workflow
### Phase 1: Drift check (M7)
Verify every file listed under `suite_e2e.*` (excluding `binary_fixtures`, which are gitignored) exists on disk. Missing file → stop and ask:
- Run `monorepo-discover` to refresh, OR
- Skip the missing artifact (recorded in report)
For `binary_fixtures` paths that are absent (expected — they live in S3/LFS), check whether `expected_detections.json._meta.video_sha256` is still a `TBD-...` placeholder. If yes, surface this as a known leftover (`_docs/_process_leftovers/2026-04-22_suite-e2e-binary-fixtures.md`) and continue.
### Phase 2: Determine scope
Same as `monorepo-cicd` Phase 2 — ask the user, or auto-detect. For **auto-detect**, flag commits that touch suite-e2e-relevant concerns:
| Commit pattern | Suite-e2e impact |
| -------------- | ---------------- |
| New port exposed by `<component>` | Healthcheck override may change in `e2e/docker-compose.suite-e2e.yml` |
| New required env var on `<component>` | `e2e/docker-compose.suite-e2e.yml` `e2e-runner` env block + `init.sql` seed |
| Public API path renamed / removed | Spec selector / API call path in `e2e/runner/tests/*.spec.ts` |
| DB schema column renamed in a `db_tables` entry | `init.sql` column reference + spec `pg.query` text |
| New required DB table referenced by spec | `init.sql` insert block (skip if owned by component migration) |
| Detection model rev change in `detections/` | `expected_detections.json` `_meta.model.revision` + flag baseline as stale |
| New canonical detection class added | `expected_detections.json._meta` annotation |
Present the flagged list; confirm.
### Phase 3: Classify changes per component
| Change type | Target suite-e2e files |
| ----------- | ---------------------- |
| Port / env var change | `e2e/docker-compose.suite-e2e.yml` |
| API path / contract change | `e2e/runner/tests/*.spec.ts` |
| DB schema reference change | `e2e/fixtures/init.sql` and spec SQL queries |
| Model / class catalog change | `e2e/fixtures/expected_detections.json` (mark `_meta.fixture_version` bump + leftover entry for binary refresh) |
| Playwright dependency drift | `e2e/runner/package.json` + `e2e/runner/Dockerfile` |
| Suite scenario steps gone stale | **Stop and ask** — scenario edits are user-driven, not drift-driven |
### Phase 4: Apply edits
Edit each in-scope file. After each batch, run `ReadLints` on touched files. Do NOT run the suite e2e itself — that's a downstream pipeline operation, not a sync-skill responsibility.
For `expected_detections.json`: when the model revision changes, the skill **does not** re-record the baseline — the binary fixture cannot be regenerated from the dev environment. Instead:
1. Set `_meta.model.revision` to the new revision.
2. Set `_meta.fixture_version` to a new bumped version with a `-stale` suffix (e.g., `0.2.0-stale`).
3. Append a new entry to `_docs/_process_leftovers/` describing the required re-record.
4. Leave `expected.by_class` untouched — the spec's tolerance check will fail loudly until the binary refresh lands.
### Phase 5: Update assumptions log
Append a new `assumptions_log:` entry to `_docs/_repo-config.yaml` recording:
- Date, components in scope, which suite-e2e files were touched
- Any inferred contract mappings still tagged `confirmed: false`
- Any leftover entries created
### Phase 6: Report
Render a Choose-format summary of the synced files, surface any `_process_leftovers/` entries created, and end. Do NOT auto-commit.
## Self-verification
- [ ] No file outside `e2e/`, `.woodpecker/suite-e2e.yml`, or `_docs/_process_leftovers/` was edited
- [ ] `_docs/_repo-config.yaml` `suite_e2e:` block was not silently mutated except for `assumptions_log` append
- [ ] `expected_detections.json` was not re-recorded (only metadata bumped + leftover added)
- [ ] Every spec edit traces to a flagged commit pattern in Phase 2
- [ ] `ReadLints` clean on every touched file
## Failure handling
Same retry / escalation protocol as `monorepo-cicd` — see `protocols.md`. The most common failure mode is the binary-fixture leftover (sample.mp4 missing or SHA-mismatched); this skill does not attempt to resolve it, only surfaces it.
.cursor/skills/monorepo-status/SKILL.md
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@@ -59,8 +59,6 @@ Mark each as `complete` / `partial` / `missing` and explain.
- Every component in `components:` appears in the registry — flag mismatches
- Every `docs.root` file cross-referenced in config exists on disk — flag missing
- Every `ci.orchestration_files` and `ci.install_scripts` exists — flag missing
- `glossary_doc:` (if recorded in config) points to a file that exists on disk — flag missing
- The cross-cutting architecture doc identified by `docs.cross_cutting` contains a `## Architecture Vision` section — flag missing (signals the meta-repo flow's Step 2.5 was skipped or the section was removed)
### Section 5: Unresolved questions
@@ -115,8 +113,6 @@ In registry, not in config: [list or "(none)"]
In config, not in registry: [list or "(none)"]
Config-referenced docs missing: [list or "(none)"]
Config-referenced CI files missing: [list or "(none)"]
glossary_doc: [path or "not recorded — run /autodev to capture"]
Architecture Vision section: [present | missing in <doc>]
═══════════════════════════════════════════════════
Unresolved questions
.cursor/skills/new-task/SKILL.md
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@@ -75,7 +75,7 @@ Record the description verbatim for use in subsequent steps.
**Role**: Technical analyst
**Goal**: Determine whether deep research is needed.
Read the user's description and the existing codebase documentation from DOCUMENT_DIR (architecture.md including its `## Architecture Vision` section, glossary.md, components/, system-flows.md). Use `glossary.md` to keep the new task's name, acceptance-criteria wording, and component references aligned with the user's confirmed vocabulary; flag the task to the user if the request appears to violate an Architecture Vision principle, do not silently allow it.
Read the user's description and the existing codebase documentation from DOCUMENT_DIR (architecture.md, components/, system-flows.md).
**Consult LESSONS.md**: if `_docs/LESSONS.md` exists, read it and look for entries in categories `estimation`, `architecture`, `dependencies` that might apply to the task under consideration. If a relevant lesson exists (e.g., "estimation: auth-related changes historically take 2x estimate"), bias the classification and recommendation accordingly. Note in the output which lessons (if any) were applied.
@@ -134,8 +134,7 @@ The `<task_slug>` is a short kebab-case name derived from the feature descriptio
**Goal**: Determine where and how to insert the new functionality, and whether existing tests cover the new requirements.
1. Read the codebase documentation from DOCUMENT_DIR:
- `architecture.md` — overall structure (the `## Architecture Vision` H2 is user-confirmed intent and must not be violated by the new task without explicit approval)
- `glossary.md` — project terminology; reuse the user's vocabulary in task names, AC, and component references
- `architecture.md` — overall structure
- `components/` — component specs
- `system-flows.md` — data flows (if exists)
- `data_model.md` — data model (if exists)
@@ -282,7 +281,7 @@ Present using the Choose format for each decision that has meaningful alternativ
- Update **Epic** field: `[EPIC-ID]`
3. Rename the file from `[##]_[short_name].md` to `[TICKET-ID]_[short_name].md`
If the work item tracker is not authenticated or unavailable, follow `.cursor/rules/tracker.mdc` before continuing. Only if the user explicitly chooses `tracker: local`:
If the work item tracker is not authenticated or unavailable (`tracker: local`):
- Keep the numeric prefix
- Set **Tracker** to `pending`
- Set **Epic** to `pending`
@@ -337,7 +336,7 @@ After the user chooses **Done**:
| Research skill hits a blocker | Follow research skill's own escalation rules |
| Codebase analysis reveals conflicting architectures | **ASK** user which pattern to follow |
| Complexity exceeds 5 points | **WARN** user and suggest splitting into multiple tasks |
| Work item tracker MCP unavailable | Follow `.cursor/rules/tracker.mdc`; do not continue in local mode unless the user explicitly chooses it |
| Work item tracker MCP unavailable | **WARN**, continue with local-only task files |
## Trigger Conditions
.cursor/skills/plan/SKILL.md
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@@ -69,7 +69,7 @@ Capture any new questions, findings, or insights that arise during test specific
### Step 2: Solution Analysis
Read and follow `steps/02_solution-analysis.md`. The step opens with **Phase 2a.0: Glossary & Architecture Vision** (BLOCKING) — drafts `_docs/02_document/glossary.md` and a one-paragraph architecture vision, presents the condensed view to the user, iterates until confirmed, then proceeds into the architecture, data-model, and deployment phases. The confirmed vision becomes the first `## Architecture Vision` H2 of `architecture.md`.
Read and follow `steps/02_solution-analysis.md`.
---
@@ -107,7 +107,6 @@ Read and follow `steps/07_quality-checklist.md`.
- **Coding during planning**: this workflow produces documents, never code
- **Multi-responsibility components**: if a component does two things, split it
- **Skipping BLOCKING gates**: never proceed past a BLOCKING marker without user confirmation
- **Skipping the glossary/vision gate (Phase 2a.0)**: drafting `architecture.md` from raw `solution.md` without confirming terminology and vision means the AI's mental model is not aligned with the user's; every downstream artifact will inherit that drift
- **Diagrams without data**: generate diagrams only after the underlying structure is documented
- **Copy-pasting problem.md**: the architecture doc should analyze and transform, not repeat the input
- **Vague interfaces**: "component A talks to component B" is not enough; define the method, input, output
@@ -138,10 +137,8 @@ Read and follow `steps/07_quality-checklist.md`.
│ │
│ 1. Blackbox Tests → test-spec/SKILL.md │
│ [BLOCKING: user confirms test coverage] │
│ 2. Solution Analysis → glossary + vision, architecture, │
│ data model, deployment │
│ [BLOCKING 2a.0: user confirms glossary + vision] │
│ [BLOCKING 2a: user confirms architecture] │
│ 2. Solution Analysis → architecture, data model, deployment │
│ [BLOCKING: user confirms architecture] │
│ 3. Component Decomp → component specs + interfaces │
│ [BLOCKING: user confirms components] │
│ 4. Review & Risk → risk register, iterations │
.cursor/skills/plan/steps/02_solution-analysis.md
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@@ -4,105 +4,20 @@
**Goal**: Produce `architecture.md`, `system-flows.md`, `data_model.md`, and `deployment/` from the solution draft
**Constraints**: No code, no component-level detail yet; focus on system-level view
### Phase 2a.0: Glossary & Architecture Vision (BLOCKING)
**Role**: Software architect + business analyst
**Goal**: Align the AI's mental model of the project with the user's intent BEFORE drafting `architecture.md`. Capture domain terminology and the user's high-level architecture vision so every downstream artifact (architecture, components, flows, tests, epics) is grounded in confirmed user intent — not in AI inference.
**Inputs**:
- `_docs/00_problem/problem.md`, `acceptance_criteria.md`, `restrictions.md`
- `_docs/00_problem/input_data/*`
- `_docs/01_solution/solution.md` (and any earlier `solution_draft*.md` siblings)
- Any blackbox-test findings produced in Step 1
**Outputs**:
- `_docs/02_document/glossary.md` (NEW)
- A confirmed "Architecture Vision" paragraph + bullet list held in working memory and used as the spine of Phase 2a's `architecture.md`
**Procedure**:
1. **Draft glossary** — extract project-specific terminology from inputs (NOT generic software terms). Include:
- Domain entities, processes, and roles
- Acronyms / abbreviations
- Internal codenames or product names
- Synonym pairs in active use (e.g., "flight" vs. "mission")
- Stakeholder personas referenced in problem.md
Each entry: one-line definition, plus a parenthetical source (`source: problem.md`, `source: solution.md §3`).
Skip terms that have a single well-known industry meaning (REST, JSON, etc.).
2. **Draft architecture vision** — synthesize from inputs:
- **One paragraph**: what the system is, who uses it, the shape of the runtime topology (monolith / services / pipeline / library / hybrid).
- **Components & responsibilities** (one-line each). At this stage these are *intent-level*, not the formal decomposition that Step 3 produces.
- **Major data flows** (one or two sentences each).
- **Architectural principles / non-negotiables** the user has implied (e.g., "DB-driven config", "no per-component state outside Redis", "all UI traffic via REST + SSE only").
- **Open architectural questions** the AI cannot resolve from inputs alone.
3. **Present condensed view** to the user (NOT the full draft files — a synopsis only):
```
══════════════════════════════════════
REVIEW: Glossary + Architecture Vision
══════════════════════════════════════
Glossary (N terms drafted):
- <Term>: <one-line definition>
- ...
Architecture Vision:
<one-paragraph synopsis>
Components / responsibilities:
- <component>: <one-line>
- ...
Principles / non-negotiables:
- <principle>
- ...
Open questions (AI could not resolve):
- <q1>
- <q2>
══════════════════════════════════════
A) Looks correct — write glossary.md, use vision for Phase 2a
B) I want to add / correct entries (provide diffs)
C) Answer the open questions first, then re-present
══════════════════════════════════════
Recommendation: pick C if open questions exist, otherwise A
══════════════════════════════════════
```
4. **Iterate**:
- On B → integrate the user's diffs/additions, re-present the condensed view, loop until A.
- On C → ask the listed open questions one round (M4-style batch), integrate answers, re-present.
- **Do NOT proceed to step 5 until the user picks A.**
5. **Save**:
- Write `_docs/02_document/glossary.md` with terms in alphabetical order. Include a top-line `**Status**: confirmed-by-user` and the date.
- Hold the confirmed vision (paragraph + components + principles) in working memory; Phase 2a will materialize it into `architecture.md` and **must** preserve every confirmed principle and component intent verbatim.
**Self-verification**:
- [ ] Every glossary entry traces to at least one input file (no invented terms)
- [ ] Every component listed in the vision is one the inputs reference
- [ ] All open questions are either answered or explicitly deferred (with the user's acknowledgement)
- [ ] User picked option A on the latest condensed view
**BLOCKING**: Do NOT proceed to Phase 2a until `glossary.md` is saved and the user has confirmed the architecture vision.
### Phase 2a: Architecture & Flows
1. Read all input files thoroughly
2. Incorporate findings, questions, and insights discovered during Step 1 (blackbox tests)
3. **Apply confirmed vision from Phase 2a.0**: the architecture document must include a top-level `## Architecture Vision` section that contains the user-confirmed paragraph, components, and principles verbatim. The rest of `architecture.md` (tech stack, deployment model, NFRs, ADRs) builds on top of that section, never contradicts it
4. Research unknown or questionable topics via internet; ask user about ambiguities
5. Document architecture using `templates/architecture.md` as structure
6. Document system flows using `templates/system-flows.md` as structure
3. Research unknown or questionable topics via internet; ask user about ambiguities
4. Document architecture using `templates/architecture.md` as structure
5. Document system flows using `templates/system-flows.md` as structure
**Self-verification**:
- [ ] `architecture.md` opens with a `## Architecture Vision` section matching Phase 2a.0
- [ ] Architecture covers all capabilities mentioned in solution.md
- [ ] System flows cover all main user/system interactions
- [ ] No contradictions with problem.md, restrictions.md, or the confirmed vision
- [ ] No contradictions with problem.md or restrictions.md
- [ ] Technology choices are justified
- [ ] Blackbox test findings are reflected in architecture decisions
- [ ] Every term used in `architecture.md` that is project-specific appears in `glossary.md`
**Save action**: Write `architecture.md` and `system-flows.md`
.cursor/skills/plan/steps/06_work-item-epics.md
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@@ -58,4 +58,4 @@ Do NOT create minimal epics with just a summary and short description. The epic
8. **Create "Blackbox Tests" epic** — this epic will parent the blackbox test tasks created by the `/decompose` skill. It covers implementing the test scenarios defined in `tests/`.
**Save action**: Epics created via the configured tracker MCP. Also saved locally in `epics.md` with ticket IDs. If tracker availability fails, follow `.cursor/rules/tracker.mdc`; only if the user explicitly chooses `tracker: local`, save locally only with pending tracker markers.
**Save action**: Epics created via the configured tracker MCP. Also saved locally in `epics.md` with ticket IDs. If `tracker: local`, save locally only.
.cursor/skills/plan/templates/epic-spec.md
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@@ -133,4 +133,4 @@ Link to architecture.md and relevant component spec.]
- `component` — a normal per-component epic
- `cross-cutting` — a shared concern that spans ≥2 components
- `tests` — the blackbox-tests epic (always exactly one)
- Complexity points for child issues follow the project standard: 1, 2, 3, 5. Do not create issues above 5 points — split them.
- Complexity points for child issues follow the project standard: 1, 2, 3, 5, 8. Do not create issues above 5 points — split them.
.cursor/skills/problem/SKILL.md
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@@ -181,8 +181,6 @@ Categorized measurable criteria with markdown headers and bullet points:
Every criterion must have a measurable value. Vague criteria like "should be fast" are not acceptable — push for "less than 400ms end-to-end".
**AC must be design-independent**: describe testable outcomes only — no libraries, algorithms, params, or design choices. Implementation follows AC, never reverse. (IEEE 830 / Atlassian / GitScrum)
### input_data/
At least one file. Options:
.cursor/skills/refactor/SKILL.md
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@@ -24,8 +24,6 @@ Phase details live in `phases/` — read the relevant file before executing each
- **Save immediately**: write artifacts to disk after each phase
- **Delegate execution**: all code changes go through the implement skill via task files
- **Ask, don't assume**: when scope or priorities are unclear, STOP and ask the user
- **Exact-fit recommendations**: do not recommend a replacement pattern, library, service, architecture, algorithm, or "modern approach" merely because it improves structure or solves a similar class of problem. It must fit confirmed product constraints, acceptance criteria, operating context, integration boundaries, and current code realities. Otherwise reject it, mark it experimental, or ask the user before adding it to the roadmap.
- **Per-mode API capability verification on replacements**: when a refactor proposes replacing or adding a library/SDK/framework/service that exposes multiple modes or configurations, pin the exact mode the refactored code will use (inputs, outputs, runtime) and verify *that mode* via mandatory `context7` lookup plus a saved Minimum Viable Example before promoting the recommendation to `Selected`. Capability claims at the category level ("supports A, B, C modes") must be cross-checked against the literal mode enumeration — `A, B → A+B` style conflations are the recurring silent-failure path.
## Context Resolution
@@ -59,7 +57,7 @@ Create REFACTOR_DIR and RUN_DIR if missing. If a RUN_DIR with the same name alre
Both modes produce `RUN_DIR/list-of-changes.md` (template: `templates/list-of-changes.md`). Both modes then convert that file into task files in TASKS_DIR during Phase 2.
**Guided mode cleanup**: after `RUN_DIR/list-of-changes.md` is created from the input file, delete the original input file only if it lives outside `RUN_DIR`. If the provided file is already the canonical `RUN_DIR/list-of-changes.md`, keep it as the audit record.
**Guided mode cleanup**: after `RUN_DIR/list-of-changes.md` is created from the input file, delete the original input file to avoid duplication.
## Workflow
@@ -81,10 +79,10 @@ Both modes produce `RUN_DIR/list-of-changes.md` (template: `templates/list-of-ch
- "refactor [specific target]" → skip phase 1 if docs exist
- Default → all phases
**Testability-run specifics** (guided mode invoked by autodev existing-code Step 4 or greenfield Step 8):
**Testability-run specifics** (guided mode invoked by autodev existing-code flow Step 4):
- Run name is `01-testability-refactoring`.
- Phase 3 (Safety Net) is skipped by design — no tests exist yet. Compensating control: the `list-of-changes.md` gate in Phase 1 must be reviewed and approved by the user before Phase 4 runs.
- Scope is MINIMAL and surgical; reject change entries that drift into full refactor territory (see the invoking flow's testability step for allowed/disallowed lists). Flagged entries go to `RUN_DIR/deferred_to_refactor.md` for the next optional full-refactor step or backlog consideration.
- Scope is MINIMAL and surgical; reject change entries that drift into full refactor territory (see existing-code flow Step 4 for allowed/disallowed lists). Flagged entries go to `RUN_DIR/deferred_to_refactor.md` for Step 8 (optional full refactor) consideration.
- After Phase 4 (Execution) completes, write `RUN_DIR/testability_changes_summary.md` as Phase 4.5. Format: one bullet per applied change.
```markdown
# Testability Changes Summary ({{run_name}})
.cursor/skills/refactor/phases/01-discovery.md
+1 -1
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@@ -95,7 +95,7 @@ Also copy to project standard locations:
**Critical step — do not skip.** Before producing the change list, cross-reference documented business flows against actual implementation. This catches issues that static code inspection alone misses.
1. **Read documented flows**: Load `DOCUMENT_DIR/system-flows.md`, `DOCUMENT_DIR/architecture.md` (paying special attention to its `## Architecture Vision` section — that's the user-confirmed structural intent), `DOCUMENT_DIR/glossary.md`, `DOCUMENT_DIR/module-layout.md`, every file under `DOCUMENT_DIR/contracts/`, and `SOLUTION_DIR/solution.md` (whichever exist). Extract every documented business flow, data path, architectural decision, module ownership boundary, and contract shape. Any refactor change that contradicts a confirmed Architecture Vision principle must either be rejected or surfaced to the user before being added to `list-of-changes.md` — those principles are not refactor targets without explicit user approval.
1. **Read documented flows**: Load `DOCUMENT_DIR/system-flows.md`, `DOCUMENT_DIR/architecture.md`, `DOCUMENT_DIR/module-layout.md`, every file under `DOCUMENT_DIR/contracts/`, and `SOLUTION_DIR/solution.md` (whichever exist). Extract every documented business flow, data path, architectural decision, module ownership boundary, and contract shape.
2. **Trace each flow through code**: For every documented flow (e.g., "video batch processing", "image tiling", "engine initialization"), walk the actual code path line by line. At each decision point ask:
- Does the code match the documented/intended behavior?
.cursor/skills/refactor/phases/02-analysis.md
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@@ -7,29 +7,14 @@
## 2a. Deep Research
1. Analyze current implementation patterns
2. Extract the **Project Constraint Matrix** from `problem.md`, `restrictions.md`, `acceptance_criteria.md`, current architecture/docs, and actual code constraints. Include required inputs/outputs, operating context, lifecycle assumptions, integration boundaries, non-functional targets, and hard disqualifiers.
3. Research modern approaches for similar systems
4. For each alternative pattern/library/service/architecture/algorithm, research intrinsic implementation constraints: required inputs/outputs, runtime assumptions, supported deployment modes, resource needs, operational limits, licensing/security constraints, and known failure reports.
**API Capability Verification — Per-Mode (MANDATORY, BLOCKING for proposed replacements)**
When a refactor recommendation replaces (or adds) a library/SDK/framework/service, the same per-mode verification used by `/research` Step 2 applies — selecting a replacement on category fit alone is the same silent-failure path. For every replacement candidate that has multiple modes or configurations:
1. **Pin the exact mode/configuration** the refactored code will use, in one explicit sentence. Inputs (data shapes, sensor counts, payloads, rates), outputs (per `acceptance_criteria.md` and contract files), runtime (matching the project's deployment).
2. **Run `context7` (or equivalent docs lookup)** for the candidate. **Mandatory for every replacement library/SDK/framework candidate**, not optional. Minimum three queries per candidate: mode enumeration, project's exact mode (with input/output shapes), disqualifier probe ("does this mode produce the required output? are there published limitations on this runtime?"). Append URLs to `RUN_DIR/analysis/research_findings.md` references section.
3. **Save a Minimum Viable Example (MVE)** for the pinned mode under `RUN_DIR/analysis/mve_evidence.md` with: source, inputs in example, outputs in example, project inputs, project outputs required, match assessment ✅/⚠️/❌. If no official example covers the project's exact configuration, the recommendation cannot be `Selected` based on category fit alone — it must be `Experimental only` (with required-evidence note) or `Rejected`.
4. **Treat "the same library in a different mode" as a different recommendation.** If the project's pinned mode is `<X>` but the only documented evidence covers `<Y>`, do not silently soften the description. Open a separate recommendation row, with its own MVE, fit assessment, and disqualifiers.
5. **Common silent-failure pattern**: a fact summary paraphrases docs as "supports A, B, C, D modes" when the docs actually mean "supports A; B; C and D as separate orthogonal modes" — no `A+B` combination exists. Cross-check paraphrased capability claims against the literal mode enumeration.
5. Identify what could be done differently
6. Suggest improvements only when they fit the Project Constraint Matrix. A cleaner or more modern approach that violates product constraints must be marked `Rejected` or `Experimental only`, not added as a roadmap recommendation.
2. Research modern approaches for similar systems
3. Identify what could be done differently
4. Suggest improvements based on state-of-the-art practices
Write `RUN_DIR/analysis/research_findings.md`:
- Current state analysis: patterns used, strengths, weaknesses
- Alternative approaches per component: current vs alternative, pros/cons, migration effort
- Prioritized recommendations: quick wins + strategic improvements
- Constraint-fit table: recommendation, **pinned mode/config**, constraints checked, **API capability evidence (MVE link)**, evidence, mismatches/disqualifiers, status (`Selected` / `Rejected` / `Experimental only` / `Needs user decision`)
- For every recommendation that replaces or adds a library/SDK/framework, append a **Restrictions × Candidate-Mode sub-matrix** that walks every numbered line of `restrictions.md` and `acceptance_criteria.md` against the candidate's pinned mode, marking each cell ✅ Pass / ❌ Fail / ❓ Verify / N/A with cited evidence. A recommendation cannot be `Selected` while any cell is ❌ or ❓.
## 2b. Solution Assessment & Hardening Tracks
@@ -37,7 +22,6 @@ Write `RUN_DIR/analysis/research_findings.md`:
2. Identify weak points in codebase, map to specific code areas
3. Perform gap analysis: acceptance criteria vs current state
4. Prioritize changes by impact and effort
5. Reject or escalate any proposed refactor that improves code structure while weakening required behavior, integration contracts, runtime constraints, safety/security posture, or acceptance criteria
Present optional hardening tracks for user to include in the roadmap:
@@ -63,9 +47,6 @@ Write `RUN_DIR/analysis/refactoring_roadmap.md`:
- Gap analysis: what's missing, what needs improvement
- Phased roadmap: Phase 1 (critical fixes), Phase 2 (major improvements), Phase 3 (enhancements)
- Selected hardening tracks and their items
- Applicability gate: each roadmap item must state constraint fit, mismatches, required evidence, and status (`Selected` / `Rejected` / `Experimental only` / `Needs user decision`)
**BLOCKING applicability gate**: Before 2c and 2d, every recommendation in the roadmap must be `Selected`. Items marked `Rejected` are excluded. Items marked `Experimental only` or `Needs user decision` require a user decision before task creation.
## 2c. Create Epic
@@ -74,7 +55,7 @@ Create a work item tracker epic for this refactoring run:
1. Epic name: the RUN_DIR name (e.g., `01-testability-refactoring`)
2. Create the epic via configured tracker MCP
3. Record the Epic ID — all tasks in 2d will be linked under this epic
4. If tracker is unavailable, follow `.cursor/rules/tracker.mdc`; only use `PENDING` placeholders if the user explicitly chooses `tracker: local`
4. If tracker unavailable, use `PENDING` placeholder and note for later
## 2d. Task Decomposition
@@ -98,12 +79,6 @@ Convert the finalized `RUN_DIR/list-of-changes.md` into implementable task files
**Self-verification**:
- [ ] All acceptance criteria are addressed in gap analysis
- [ ] Recommendations are grounded in actual code, not abstract
- [ ] Every recommendation has been checked against the Project Constraint Matrix
- [ ] No recommendation violates product restrictions, acceptance criteria, documented architecture decisions, or actual code integration boundaries
- [ ] Every replacement library/SDK/framework recommendation has a pinned mode/config, a saved MVE in `mve_evidence.md`, and a Restrictions × Candidate-Mode sub-matrix with no ❌ or ❓ cells
- [ ] `context7` (or equivalent) was consulted for every replacement library/SDK/framework recommendation
- [ ] Paraphrased capability claims have been cross-checked against the literal mode-enumeration evidence (no `A, B → A+B` style conflation)
- [ ] Rejected and experimental approaches are documented but not converted into implementation tasks without user approval
- [ ] Roadmap phases are prioritized by impact
- [ ] Epic created and all tasks linked to it
- [ ] Every entry in list-of-changes.md has a corresponding task file in TASKS_DIR
.cursor/skills/refactor/phases/04-execution.md
+4 -4
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@@ -10,7 +10,7 @@
- All `[TRACKER-ID]_refactor_*.md` files are present
- Each task file has valid header fields (Task, Name, Description, Complexity, Dependencies)
2. Verify `TASKS_DIR/_dependencies_table.md` includes the refactoring tasks
3. Verify all tests pass (safety net from Phase 3 is green), unless this is a testability run where Phase 3 was intentionally skipped
3. Verify all tests pass (safety net from Phase 3 is green)
4. If any check fails, go back to the relevant phase to fix
## 4b. Delegate to Implement Skill
@@ -21,9 +21,9 @@ The implement skill will:
1. Parse task files and dependency graph from TASKS_DIR
2. Detect already-completed tasks (skip non-refactoring tasks from prior workflow steps)
3. Compute execution batches for the refactoring tasks
4. Implement tasks sequentially in topological order (no subagents, no parallelism)
4. Launch implementer subagents (up to 4 in parallel)
5. Run code review after each batch
6. Commit per batch and push only when the user approved pushing
6. Commit and push per batch
7. Update work item ticket status
Do NOT modify, skip, or abbreviate any part of the implement skill's workflow. The refactor skill is delegating execution, not optimizing it.
@@ -47,7 +47,7 @@ After the implement skill completes:
For each successfully completed refactoring task:
1. Transition the work item ticket status to **Done** via the configured tracker MCP
2. If tracker is unavailable, follow `.cursor/rules/tracker.mdc`; if the user explicitly chose `tracker: local`, note the pending status transitions in `RUN_DIR/execution_log.md`
2. If tracker unavailable, note the pending status transitions in `RUN_DIR/execution_log.md`
For any failed or blocked tasks, leave their status as-is (the implement skill already set them to In Testing or blocked).
.cursor/skills/refactor/phases/07-documentation.md
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@@ -32,7 +32,7 @@ For each component doc affected:
## 7d. Update System-Level Documentation
If structural changes were made (new modules, removed modules, changed interfaces):
1. Update `_docs/02_document/architecture.md` if architecture changed — but **never edit the `## Architecture Vision` section**. That section is user-confirmed (plan Phase 2a.0 / document Step 4.5); if a refactor invalidates a vision principle, surface it to the user and let them update the vision themselves before continuing. Update only the technical sections below the Vision H2.
1. Update `_docs/02_document/architecture.md` if architecture changed
2. Update `_docs/02_document/system-flows.md` if flow sequences changed
3. Update `_docs/02_document/diagrams/components.md` if component relationships changed
.cursor/skills/refactor/templates/list-of-changes.md
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@@ -23,7 +23,6 @@ Save as `RUN_DIR/list-of-changes.md`. Produced during Phase 1 (Discovery).
- **Problem**: [what makes this problematic / untestable / coupled]
- **Change**: [what to do — behavioral description, not implementation steps]
- **Rationale**: [why this change is needed]
- **Constraint Fit**: [which product constraints / acceptance criteria / integration boundaries this preserves; or "Rejected — violates ..."]
- **Risk**: [low | medium | high]
- **Dependencies**: [other change IDs this depends on, or "None"]
@@ -32,7 +31,6 @@ Save as `RUN_DIR/list-of-changes.md`. Produced during Phase 1 (Discovery).
- **Problem**: [description]
- **Change**: [description]
- **Rationale**: [description]
- **Constraint Fit**: [description]
- **Risk**: [low | medium | high]
- **Dependencies**: [C01, or "None"]
```
@@ -46,8 +44,6 @@ Save as `RUN_DIR/list-of-changes.md`. Produced during Phase 1 (Discovery).
- **File(s)** must reference actual files verified to exist in the codebase
- **Problem** describes the current state, not the desired state
- **Change** describes what the system should do differently — behavioral, not prescriptive
- **Constraint Fit** proves the change preserves confirmed product requirements, restrictions, acceptance criteria, architecture decisions, and integration contracts
- Do not include changes whose only benefit is structural cleanliness if they weaken required behavior or violate constraints; record those as rejected in analysis instead
- **Dependencies** reference other change IDs within this list; cross-run dependencies use tracker IDs
- In guided mode, the input file entries are validated against actual code and enriched with file paths, risk, and dependencies before writing
- In automatic mode, entries are derived from Phase 1 component analysis and Phase 2 research findings
.cursor/skills/research/SKILL.md
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@@ -30,27 +30,6 @@ Transform vague topics raised by users into high-quality, deliverable research r
- **Internet-first investigation** — do not rely on training data for factual claims; search the web extensively for every sub-question, rephrase queries when results are thin, and keep searching until you have converging evidence from multiple independent sources
- **Multi-perspective analysis** — examine every problem from at least 3 different viewpoints (e.g., end-user, implementer, business decision-maker, contrarian, domain expert, field practitioner); each perspective should generate its own search queries
- **Question multiplication** — for each sub-question, generate multiple reformulated search queries (synonyms, related terms, negations, "what can go wrong" variants, practitioner-focused variants) to maximize coverage and uncover blind spots
- **Component option breadth** — for every component area, build a broad option landscape before selecting. Search direct candidates, adjacent-domain alternatives, commercial/open-source variants, classical/simple baselines, current SOTA, and "do not use" failure cases. A component may not be narrowed to one candidate until alternatives have been searched and rejected with evidence.
- **Component research depth** — for every serious component candidate, go beyond discovery pages. Read official docs, repository/license files, issue discussions, benchmarks, deployment guides, version/platform requirements, security notes, maintenance signals, and real-world failure reports. Extract evidence for inputs/outputs, lifecycle assumptions, runtime/storage/latency fit, integration boundaries, licensing, operational risks, and unsupported scenarios before assigning any selection status.
- **Exact-fit component selection** — never select a component, tool, library, service, architecture pattern, or algorithm merely because it solves a similar class of problem. It must be proven compatible with the project's explicit operating context, constraints, required inputs/outputs, non-functional requirements, lifecycle assumptions, and acceptance criteria. If fit is unproven or mismatched, mark it `Rejected`, `Experimental only`, or escalate for user decision before it can shape the solution.
- **Per-mode API capability verification** *(applies only to technical-component selection — see Research Output Class below)* — when a candidate library/SDK/framework/service exposes multiple modes or configurations, *the candidate is not a single thing*. Pin the exact mode the project will use (one explicit sentence: inputs, outputs, runtime), and verify *that mode* against the project's required inputs/outputs via official docs (mandatory `context7` lookup) plus a saved Minimum Viable Example. Capability claims at the category level ("supports X, Y, Z modes") must be cross-checked against the literal mode enumeration before being treated as project-applicable. Two modes of one library are two distinct candidates for the purposes of the Component Applicability Gate. Does not apply to non-technical research (concept comparison, market/policy investigation, knowledge organization, etc.).
## Research Output Class (BLOCKING — set in Step 1)
Before applying any of the technical-component gates (per-mode API capability verification, Component Applicability Gate, Restrictions × Candidate-Mode sub-matrix, MVE evidence, mandatory `context7` lookup), classify the research output into one of two classes. Record the decision in `00_question_decomposition.md` once, near the top, so every downstream step honors it.
| Class | What the output recommends or selects | Examples | Technical-component gates apply? |
|-------|---------------------------------------|----------|----------------------------------|
| **Technical-component selection** | One or more libraries, SDKs, frameworks, services, protocols, data formats, infrastructure patterns, algorithms, or APIs that will be implemented or operated against | "Pick a vector database", "Compare auth-token strategies for our API", "Should we use Kafka or RabbitMQ?", architecture / tech-stack / migration drafts (Mode A, Mode B) | **Yes — all gates active** |
| **Non-technical investigation** | Concept comparisons, knowledge organization, root-cause investigation of an event, market/policy/regulatory/social analysis, literature review, decision support without committing to specific tooling | "Why did adoption stall in Q3?", "Compare phenomenology vs constructivism", "Map regulatory landscape for X", "What do practitioners say about onboarding under remote-first orgs?" | **No — skip API/MVE/sub-matrix gates; the rest of the 8-step engine still applies** |
How to decide:
1. Inspect the question and the input files (`problem.md`, `restrictions.md`, `acceptance_criteria.md`, or the standalone input file).
2. If the deliverable will name specific software/services/protocols that someone will then build with or operate, it is **Technical-component selection**.
3. If the deliverable is a report, comparison, or recommendation that does not commit to specific tooling, it is **Non-technical investigation**.
4. **Mixed runs are valid.** Some research questions have a non-technical core but include one technical sub-question (or vice versa). In that case classify per component area within the run, not the run as a whole, and note in `00_question_decomposition.md` which component areas trigger the technical-component gates.
When the run is purely **Non-technical investigation**, the rest of the research engine — question decomposition, perspective rotation, exhaustive web search, fact extraction, comparison framework, reasoning chain, validation, deliverable formatting — still applies in full. The sections that get skipped are explicitly the technical gates listed in the table above.
## Context Resolution
.cursor/skills/research/references/quality-checklists.md
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@@ -27,26 +27,13 @@
- [ ] Iterative deepening completed: follow-up questions from initial findings were searched
- [ ] No sub-question relies solely on training data without web verification
## Component Option Breadth
- [ ] `00_question_decomposition.md` contains a Component Option Search Plan
- [ ] Every component area was searched across simple baseline, established production, open-source, commercial/vendor, current SOTA, adjacent-domain, no-build/defer, and known-bad options where applicable
- [ ] Every component area has at least 3 realistic candidates, or a documented explanation of why broad searches found fewer
- [ ] Each lead candidate has official/source-of-truth evidence plus independent validation when available
- [ ] Each component area includes at least one baseline/fallback option and at least one rejected or experimental option when possible
- [ ] Alternative names, synonyms, and neighboring-domain terms were searched before declaring the option landscape complete
- [ ] Licensing, runtime, platform, maintenance, and unsupported-scenario searches were performed for every lead, fallback, and rejected candidate
## Mode A Specific
- [ ] Phase 1 completed: AC assessment was presented to and confirmed by user
- [ ] AC assessment consistent: Solution draft respects the (possibly adjusted) acceptance criteria and restrictions
- [ ] Competitor analysis included: Existing solutions were researched
- [ ] All components have comparison tables: Each component lists alternatives with tools, advantages, limitations, security, cost
- [ ] Component options are broad: component tables include baseline, production, open-source, commercial/vendor, SOTA/research, adjacent-domain, defer/no-build, and disqualified options where applicable
- [ ] Tools/libraries verified: Suggested tools actually exist and work as described
- [ ] Component fit matrix completed: `06_component_fit_matrix.md` (or `06_component_fit_matrix/` if split) exists and every selected component/tool/pattern is marked `Selected`
- [ ] No field-adjacent substitution: no selected candidate is chosen only because it solves a similar class of problem while failing the project's explicit constraints
- [ ] Testing strategy covers AC: Tests map to acceptance criteria
- [ ] Tech stack documented (if Phase 3 ran): `tech_stack.md` has evaluation tables, risk assessment, and learning requirements
- [ ] Security analysis documented (if Phase 4 ran): `security_analysis.md` has threat model and per-component controls
@@ -58,9 +45,6 @@
- [ ] New draft is self-contained: Written as if from scratch, no "updated" markers
- [ ] Performance column included: Mode B comparison tables include performance characteristics
- [ ] Previous draft issues addressed: Every finding in the table is resolved in the new draft
- [ ] Existing selected components were challenged against a broad alternative landscape before being kept
- [ ] Existing component fit audited: every old and new component/tool/pattern was checked against `restrictions.md`, `acceptance_criteria.md`, and the Project Constraint Matrix
- [ ] Rejected/experimental candidates are not lead recommendations unless the user explicitly accepted the risk
## Timeliness Check (High-Sensitivity Domain BLOCKING)
@@ -80,7 +64,7 @@ When the research topic has Critical or High sensitivity level:
## Target Audience Consistency Check (BLOCKING)
- [ ] Research boundary clearly defined: `00_question_decomposition.md` has clear population/geography/timeframe/level boundaries
- [ ] Every source has target audience annotated in `01_source_registry.md` (or category files under `01_source_registry/` if split)
- [ ] Every source has target audience annotated in `01_source_registry.md`
- [ ] Mismatched sources properly handled (excluded, annotated, or marked reference-only)
- [ ] No audience confusion in fact cards: Every fact has target audience consistent with research boundary
- [ ] No audience confusion in the report: Policies/research/data cited have consistent target audiences
@@ -92,33 +76,3 @@ When the research topic has Critical or High sensitivity level:
- [ ] Cited facts have corresponding statements in the original text (no over-interpretation)
- [ ] Source publication/update dates annotated; technical docs include version numbers
- [ ] Unverifiable information annotated `[limited source]` and not sole support for core conclusions
## Exact-Fit Validation (BLOCKING)
- [ ] Project Constraint Matrix extracted from problem context before component selection
- [ ] Component fit matrix includes `Component Area`, `Option Family`, and `Pinned Mode/Config` columns
- [ ] Every selected component/tool/library/service/pattern/algorithm has evidence for required inputs/outputs and integration boundaries
- [ ] Every selected candidate has evidence for the operating context and lifecycle assumptions it must support
- [ ] Every selected candidate has evidence for non-functional targets that are binding for the project
- [ ] Known unsupported scenarios and failure reports were searched for every selected candidate
- [ ] Mismatches are recorded as disqualifiers, not softened into generic limitations
- [ ] Any candidate with unproven fit is marked `Experimental only` or escalated for user decision
- [ ] Any candidate with documented constraint conflict is marked `Rejected`
## API Capability Verification (BLOCKING)
**Applicability**: this checklist applies only when the run is classified as **Technical-component selection** (see SKILL.md → Research Output Class). For non-technical research (concept comparison, market/policy investigation, root-cause analysis, knowledge organization), skip this checklist entirely and note the skip in `05_validation_log.md`. For mixed runs, apply only to technical component areas.
For every lead candidate that is a library/SDK/framework/service:
- [ ] The exact mode/configuration the project will use is pinned in one explicit sentence (inputs, outputs, runtime); no vague "supports X" language
- [ ] `context7` (or equivalent docs lookup) was run for the candidate, with at least 3 queries: mode enumeration, project's exact mode, disqualifier probe
- [ ] All consulted URLs from context7 / official docs are appended to `01_source_registry.md` (or files under `01_source_registry/` if split)
- [ ] A Minimum Viable Example (MVE) was saved for the pinned mode in `02_fact_cards.md` / `02_fact_cards/` (or `02_mve_evidence.md`) with: source, inputs in example, outputs in example, project inputs, project outputs required, match assessment ✅/⚠️/❌
- [ ] When the MVE inputs or outputs do not exactly match the project's, the mismatch is cited from the official docs (not inferred), and the candidate is `Experimental only` or `Rejected`
- [ ] When a library has multiple modes, each project-relevant mode appears as its own candidate row (not a single library row that softens across modes)
- [ ] Restrictions × Candidate-Modes sub-matrix in `06_component_fit_matrix.md` (or files under `06_component_fit_matrix/` if split) is filled for every lead candidate, with one row per numbered restriction and per numbered acceptance criterion
- [ ] Sub-matrix uses ✅ / ❌ / ❓ / N/A only — no free-form prose substitutes
- [ ] No `Selected` candidate has any ❌ or ❓ cell in its sub-matrix
- [ ] "Validation gate required" footnotes are explicitly classified as either *API capability* (must be resolved here) or *runtime quality* (may be carried forward)
- [ ] Paraphrased capability claims in fact cards have been cross-checked against the literal mode-enumeration evidence (no `mono, inertial → mono-inertial` style conflation)
.cursor/skills/research/references/source-tiering.md
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@@ -89,7 +89,7 @@ Value Translation:
## Source Registry Entry Template
For each source consulted, immediately append to `01_source_registry.md` (or the appropriate category file under `01_source_registry/` if the artifact has been split — see splittable-artifacts convention in `steps/00_project-integration.md`):
For each source consulted, immediately append to `01_source_registry.md`:
```markdown
## Source #[number]
- **Title**: [source title]
.cursor/skills/research/steps/00_project-integration.md
+4 -32
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@@ -57,49 +57,22 @@ RESEARCH_DIR/
├── 03_comparison_framework.md # Step 4 output: selected framework and populated data
├── 04_reasoning_chain.md # Step 6 output: fact → conclusion reasoning
├── 05_validation_log.md # Step 7 output: use-case validation results
├── 06_component_fit_matrix.md # Step 7.5 output: component exact-fit gate
└── raw/ # Raw source archive (optional)
├── source_1.md
└── source_2.md
```
#### Splittable artifacts — Layout convention
The following three artifacts MAY equivalently be a **folder** of the same base name when the single-file form has grown unwieldy (typically ≳ 1000 lines or ≳ 200 KB):
- `01_source_registry.md` ↔ `01_source_registry/`
- `02_fact_cards.md` ↔ `02_fact_cards/`
- `06_component_fit_matrix.md` ↔ `06_component_fit_matrix/`
When using the folder form:
- Place a `00_summary.md` index file at the folder root with a short common summary table and the cross-cutting status the single-file form would have carried in its preamble.
- Split per-entry content into category files (e.g. one file per sub-question or per component): `SQ1_*.md`, `C1_*.md`, etc. Keep entry numbering global across the folder so cross-references like "Source #42" still resolve to exactly one place.
- Cross-references from outside the folder may point at either `01_source_registry/00_summary.md` (for the index) or directly at the relevant category file.
```
RESEARCH_DIR/01_source_registry/ # split form (when single-file is too large)
├── 00_summary.md # index + investigation status + compact source table
├── SQ1_existing_systems.md # category file
├── SQ2_canonical_pipeline.md # category file
├── C1_vio.md # per-component file
└── ...
```
Throughout the rest of this skill (other steps, references, templates), the singular `XX.md` form is used as a logical name; treat each occurrence as applying equally to the folder form when the artifact has been split.
### Save Timing & Content
| Step | Save immediately after completion | Filename |
|------|-----------------------------------|----------|
| Mode A Phase 1 | AC & restrictions assessment tables | `00_ac_assessment.md` |
| Step 0-1 | Question type classification + sub-question list | `00_question_decomposition.md` |
| Step 2 | Each consulted source link, tier, summary | `01_source_registry.md` *(splittable, see convention)* |
| Step 3 | Each fact card (statement + source + confidence) | `02_fact_cards.md` *(splittable, see convention)* |
| Step 2 | Each consulted source link, tier, summary | `01_source_registry.md` |
| Step 3 | Each fact card (statement + source + confidence) | `02_fact_cards.md` |
| Step 4 | Selected comparison framework + initial population | `03_comparison_framework.md` |
| Step 6 | Reasoning process for each dimension | `04_reasoning_chain.md` |
| Step 7 | Validation scenarios + results + review checklist | `05_validation_log.md` |
| Step 7.5 | Component exact-fit gate and selection status | `06_component_fit_matrix.md` *(splittable, see convention)* |
| Step 8 | Complete solution draft | `OUTPUT_DIR/solution_draft##.md` |
### Save Principles
@@ -117,12 +90,11 @@ Throughout the rest of this skill (other steps, references, templates), the sing
|------|---------|----------------|
| `00_ac_assessment.md` | AC & restrictions assessment (Mode A only) | After Phase 1 completion |
| `00_question_decomposition.md` | Question type, sub-question list | After Step 0-1 completion |
| `01_source_registry.md` *(splittable)* | All source links and summaries | Continuously updated during Step 2 |
| `02_fact_cards.md` *(splittable)* | Extracted facts and sources | Continuously updated during Step 3 |
| `01_source_registry.md` | All source links and summaries | Continuously updated during Step 2 |
| `02_fact_cards.md` | Extracted facts and sources | Continuously updated during Step 3 |
| `03_comparison_framework.md` | Selected framework and populated data | After Step 4 completion |
| `04_reasoning_chain.md` | Fact → conclusion reasoning | After Step 6 completion |
| `05_validation_log.md` | Use-case validation and review | After Step 7 completion |
| `06_component_fit_matrix.md` *(splittable)* | Exact-fit matrix for every proposed component/tool/pattern with status `Selected` / `Rejected` / `Experimental only` / `Needs user decision` | Before Step 8 deliverable formatting |
| `OUTPUT_DIR/solution_draft##.md` | Complete solution draft | After Step 8 completion |
| `OUTPUT_DIR/tech_stack.md` | Tech stack evaluation and decisions | After Phase 3 (optional) |
| `OUTPUT_DIR/security_analysis.md` | Threat model and security controls | After Phase 4 (optional) |
.cursor/skills/research/steps/01_mode-a-initial-research.md
+8 -12
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@@ -6,9 +6,7 @@ Triggered when no `solution_draft*.md` files exist in OUTPUT_DIR, or when the us
**Role**: Professional software architect
> **AC must be design-independent**: describe testable outcomes only — no libraries, algorithms, params, or design choices. Implementation follows AC, never reverse. (IEEE 830 / Atlassian / GitScrum)
A focused preliminary research pass **before** the main solution research. The goal is to validate that the acceptance criteria and restrictions are realistic before designing a solution around them. Any revision proposed in this phase must respect the design-independence rule above — propose AC changes as outcome/budget edits, not as implementation prescriptions.
A focused preliminary research pass **before** the main solution research. The goal is to validate that the acceptance criteria and restrictions are realistic before designing a solution around them.
**Input**: All files from INPUT_DIR (or INPUT_FILE in standalone mode)
@@ -75,18 +73,16 @@ Full 8-step research methodology. Produces the first solution draft.
**Task** (drives the 8-step engine):
1. Research existing/competitor solutions for similar problems — search broadly across industries and adjacent domains, not just the obvious competitors
2. Research the problem thoroughly — all possible ways to solve it, split into components; search for how different fields approach analogous problems
3. Derive a **Project Constraint Matrix** before evaluating component options. Extract exact constraints from `problem.md`, `restrictions.md`, `acceptance_criteria.md`, input data notes, and the Phase 1 AC assessment. Include required inputs/outputs, operating context, runtime envelope, data availability, lifecycle boundaries, non-functional targets, integration boundaries, security constraints, and explicit out-of-scope decisions.
4. For each component, research all possible solutions and find the most efficient state-of-the-art approaches — use multiple query variants and perspectives from Step 1
5. For each promising approach, search for real-world deployment experience: success stories, failure reports, lessons learned, and practitioner opinions
6. Search for contrarian viewpoints — who argues against the common approaches and why? What failure modes exist?
7. Verify that suggested tools/libraries actually exist and work as described — check official repos, latest releases, and community health (stars, recent commits, open issues)
8. For every candidate component/tool/library/service/pattern/algorithm, prove exact fit against the Project Constraint Matrix. A field-adjacent solution is not selectable unless its documented implementation assumptions match the project's constraints. Mismatches must be recorded as disqualifiers and the candidate marked `Rejected`, `Experimental only`, or `Needs user decision`.
9. Include security considerations in each component analysis
10. Provide rough cost estimates for proposed solutions
3. For each component, research all possible solutions and find the most efficient state-of-the-art approaches — use multiple query variants and perspectives from Step 1
4. For each promising approach, search for real-world deployment experience: success stories, failure reports, lessons learned, and practitioner opinions
5. Search for contrarian viewpoints — who argues against the common approaches and why? What failure modes exist?
6. Verify that suggested tools/libraries actually exist and work as described — check official repos, latest releases, and community health (stars, recent commits, open issues)
7. Include security considerations in each component analysis
8. Provide rough cost estimates for proposed solutions
Be concise in formulating. The fewer words, the better, but do not miss any important details.
**Save action**: Write `RESEARCH_DIR/06_component_fit_matrix.md` (or its split-folder equivalent under `RESEARCH_DIR/06_component_fit_matrix/`, per the splittable-artifacts convention in `00_project-integration.md`) before the final draft, then write `OUTPUT_DIR/solution_draft##.md` using template: `templates/solution_draft_mode_a.md`
**Save action**: Write `OUTPUT_DIR/solution_draft##.md` using template: `templates/solution_draft_mode_a.md`
---
.cursor/skills/research/steps/02_mode-b-solution-assessment.md
+7 -14
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@@ -10,25 +10,18 @@ Full 8-step research methodology applied to assessing and improving an existing
**Task** (drives the 8-step engine):
1. Read the existing solution draft thoroughly
2. Derive or refresh the **Project Constraint Matrix** from all files in INPUT_DIR. Include required inputs/outputs, operating context, runtime envelope, data availability, lifecycle boundaries, non-functional targets, integration boundaries, security constraints, and explicit out-of-scope decisions.
3. Audit every component/decision in the existing draft against the Project Constraint Matrix before researching alternatives:
- If a component's documented implementation assumptions match the project constraints, keep it eligible and record evidence.
- If fit is unproven, mark it `Experimental only` until evidence is found.
- If constraints conflict, mark it `Rejected` and search for alternatives.
- If rejecting it changes product behavior or risk materially, escalate for user decision.
4. Research in internet extensively — for each component/decision in the draft, search for:
2. Research in internet extensively — for each component/decision in the draft, search for:
- Known problems and limitations of the chosen approach
- What practitioners say about using it in production
- Better alternatives that may have emerged recently
- Common failure modes and edge cases
- How competitors/similar projects solve the same problem differently
5. Search specifically for contrarian views: "why not [chosen approach]", "[chosen approach] criticism", "[chosen approach] failure"
6. Identify security weak points and vulnerabilities — search for CVEs, security advisories, and known attack vectors for each technology in the draft
7. Identify performance bottlenecks — search for benchmarks, load test results, and scalability reports
8. For each identified weak point, search for multiple solution approaches and compare them
9. For every revised candidate, prove exact fit against the Project Constraint Matrix. Do not select field-adjacent or "similar problem" options unless their intrinsic implementation constraints match the project.
10. Based on findings, form a new solution draft in the same format
3. Search specifically for contrarian views: "why not [chosen approach]", "[chosen approach] criticism", "[chosen approach] failure"
4. Identify security weak points and vulnerabilities — search for CVEs, security advisories, and known attack vectors for each technology in the draft
5. Identify performance bottlenecks — search for benchmarks, load test results, and scalability reports
6. For each identified weak point, search for multiple solution approaches and compare them
7. Based on findings, form a new solution draft in the same format
**Save action**: Write `RESEARCH_DIR/06_component_fit_matrix.md` (or its split-folder equivalent under `RESEARCH_DIR/06_component_fit_matrix/`, per the splittable-artifacts convention in `00_project-integration.md`) before the final draft, then write `OUTPUT_DIR/solution_draft##.md` (incremented) using template: `templates/solution_draft_mode_b.md`
**Save action**: Write `OUTPUT_DIR/solution_draft##.md` (incremented) using template: `templates/solution_draft_mode_b.md`
**Optional follow-up**: After Mode B completes, the user can request Phase 3 (Tech Stack Consolidation) or Phase 4 (Security Deep Dive) using the revised draft. These phases work identically to their Mode A descriptions in `steps/01_mode-a-initial-research.md`.
.cursor/skills/research/steps/03_engine-investigation.md
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@@ -40,7 +40,6 @@ Key principle: Critical-sensitivity topics (AI/LLMs, blockchain) require sources
- "What existing/competitor solutions address this problem?"
- "What are the component parts of this problem?"
- "For each component, what are the state-of-the-art solutions?"
- "For each component, what are the practical alternatives across simple baseline, established production option, open-source option, commercial option, current SOTA, adjacent-domain option, and no-build/defer option?"
- "What are the security considerations per component?"
- "What are the cost implications of each approach?"
@@ -49,7 +48,6 @@ Key principle: Critical-sensitivity topics (AI/LLMs, blockchain) require sources
- "What are the security vulnerabilities in the proposed architecture?"
- "Where are the performance bottlenecks?"
- "What solutions exist for each identified issue?"
- "For each component already selected in the draft, what alternatives should be considered before keeping, replacing, or rejecting it?"
**General sub-question patterns** (use when applicable):
- **Sub-question A**: "What is X and how does it work?" (Definition & mechanism)
@@ -86,27 +84,6 @@ For **each sub-question**, generate **at least 3-5 search query variants** befor
Record all planned queries in `00_question_decomposition.md` alongside each sub-question.
#### Component Option Breadth (MANDATORY)
Before Step 2, identify the component areas implied by the problem and create a search plan for options in each area. A component area is any replaceable tool, library, model, service, algorithm, data format, protocol, infrastructure pattern, or validation approach that could materially affect the solution.
For every component area, generate search queries for these option families unless clearly not applicable:
- **Simple baseline**: low-complexity classical or manual approach that can serve as a fallback or regression baseline.
- **Established production option**: mature library/service/pattern with field usage.
- **Open-source candidate**: permissive-license option with inspectable implementation and community history.
- **Commercial/vendor option**: paid or vendor-supported option, including SDK/platform constraints.
- **Current SOTA / research option**: recent model, paper, or benchmark leader that may be promising but immature.
- **Adjacent-domain option**: solution from a neighboring domain with similar constraints.
- **No-build / defer option**: whether the component can be avoided, simplified, or moved out of scope.
- **Known bad option**: candidate or family that appears attractive but has documented failure modes or disqualifiers.
For each component area, record:
- Candidate names and option families to search.
- At least 5 query variants covering alternatives, comparisons, limitations, licensing, runtime/scale, and exact project constraints.
- The minimum evidence needed to mark a candidate `Selected`, `Rejected`, `Experimental only`, or `Needs user decision`.
Add this as a "Component Option Search Plan" section in `00_question_decomposition.md`.
**Research Subject Boundary Definition (BLOCKING - must be explicit)**:
When decomposing questions, you must explicitly define the **boundaries of the research subject**:
@@ -117,9 +94,6 @@ When decomposing questions, you must explicitly define the **boundaries of the r
| **Geography** | Which region is being studied? | Chinese universities vs US universities vs global |
| **Timeframe** | Which period is being studied? | Post-2020 vs full historical picture |
| **Level** | Which level is being studied? | Undergraduate vs graduate vs vocational |
| **Operating context** | What exact environment, lifecycle phase, and runtime conditions must the solution support? | In-flight embedded runtime vs offline post-processing; production web traffic vs admin batch job |
| **Required interfaces** | What inputs, outputs, protocols, data shapes, and ownership boundaries are fixed? | One camera vs stereo rig; REST API vs message queue; local file boundary vs service API |
| **Non-functional envelope** | What latency, throughput, storage, memory, availability, safety, security, cost, and maintainability targets are binding? | <400 ms p95, 8 GB RAM, 99.9% availability, reversible migrations |
**Common mistake**: User asks about "university classroom issues" but sources include policies targeting "K-12 students" — mismatched target populations will invalidate the entire research.
@@ -142,11 +116,9 @@ Record the audit result in `00_question_decomposition.md` as a "Completeness Aud
- Summary of relevant problem context from INPUT_DIR
- Classified question type and rationale
- **Research subject boundary definition** (population, geography, timeframe, level)
- **Project Constraint Matrix summary** (operating context, required interfaces, non-functional envelope, lifecycle assumptions, and hard disqualifiers extracted from input files)
- List of decomposed sub-questions
- **Chosen perspectives** (at least 3 from the Perspective Rotation table) with rationale
- **Search query variants** for each sub-question (at least 3-5 per sub-question)
- **Component Option Search Plan** (component areas, option families, candidate names, query variants, required evidence)
- **Completeness audit** (taxonomy cross-reference + domain discovery results)
4. Write TodoWrite to track progress
@@ -160,7 +132,7 @@ Tier sources by authority, **prioritize primary sources** (L1 > L2 > L3 > L4). C
**Tool Usage**:
- Use `WebSearch` for broad searches; `WebFetch` to read specific pages
- Use the `context7` MCP server (`resolve-library-id` then `query-docs` / `get-library-docs`) for up-to-date library/framework documentation. **Mandatory per lead candidate** — see "API Capability Verification" below.
- Use the `context7` MCP server (`resolve-library-id` then `get-library-docs`) for up-to-date library/framework documentation
- Always cross-verify training data claims against live sources for facts that may have changed (versions, APIs, deprecations, security advisories)
- When citing web sources, include the URL and date accessed
@@ -173,77 +145,17 @@ Do not stop at the first few results. The goal is to build a comprehensive evide
- Consult at least **2 different source tiers** per sub-question (e.g., L1 official docs + L4 community discussion)
- If initial searches yield fewer than 3 relevant sources for a sub-question, **broaden the search** with alternative terms, related domains, or analogous problems
**Minimum search effort per component area**:
- Search every option family from the "Component Option Search Plan" before choosing a lead candidate.
- For each lead, fallback, or rejected candidate, search at least one official/source-of-truth page and at least one independent validation source when available.
- Search `"[component] alternatives"`, `"[candidate] vs [alternative]"`, `"[candidate] limitations"`, `"[candidate] license"`, `"[candidate] production"`, and `"[candidate] [binding project constraint]"`.
- If fewer than 3 realistic candidates are found for a component area, explicitly document why the landscape is narrow and search adjacent domains before accepting that result.
- Include at least one simple baseline and one "do not use" or disqualified candidate per component area when possible; these prevent false confidence in the selected option.
**Candidate implementation-limit searches (MANDATORY)**:
For every component/tool/library/service/pattern/algorithm that may be selected or recommended, search for its intrinsic implementation constraints. Do not rely on product category labels, marketing summaries, or examples from a different operating context. Include query variants for:
- Official supported inputs/outputs, protocols, data formats, and deployment modes
- Required hardware/runtime/platform/version constraints
- Timing, throughput, memory, storage, synchronization, and scaling assumptions
- Lifecycle assumptions: offline vs online, batch vs real time, development vs production, single tenant vs multi tenant, local vs networked
- Known unsupported scenarios, limitations, issue reports, production failures, and workarounds
- Licensing, security, maintenance, and community-health constraints
- Exact phrases from the project's restrictions and acceptance criteria combined with the candidate name
**API Capability Verification — Per-Mode (MANDATORY, BLOCKING for lead candidates)**:
**Applicability**: this section applies only when the run is classified as **Technical-component selection** in the SKILL's Research Output Class section, and only to lead candidates that are libraries/SDKs/frameworks/services/protocols/data formats with multiple modes or configurations. For non-technical research (concept comparison, market/policy investigation, knowledge organization, root-cause analysis without tooling commitments), skip this entire sub-section and continue with the rest of Step 2 — the broader candidate implementation-limit search above is sufficient. State the skip explicitly once in `02_fact_cards.md` (or in `02_fact_cards/00_summary.md` if split): `API Capability Verification: not applicable — this run is a Non-technical investigation, no library/SDK/service candidates`.
Most libraries/SDKs/services expose **multiple modes or configurations** (e.g., monocular vs stereo VO, sync vs async API, batch vs streaming inference, write-through vs write-behind cache). Selecting a candidate "because it supports X" without pinning *which mode* the project will use, and *whether that exact mode produces the required outputs from the required inputs*, is the most common silent-failure path in research. A library can support a class of problem in mode A while being unusable for the project's specific configuration in mode B.
For every lead candidate that is a library/SDK/framework/service with multiple modes or configurations, do the following — in this order, before marking the candidate `Selected`:
1. **Pin the exact mode/configuration the project will use.**
Derived from the Project Constraint Matrix: which inputs are available (sensor count, sensor types, data shapes, rates), which outputs are required (per `acceptance_criteria.md` and contract files), which hardware/runtime is fixed (per `restrictions.md`). Write this as a single sentence: "We will use `<library>` in `<mode/config>` with inputs `<list>` and expect outputs `<list>` on `<runtime>`." Do not progress past this step on a vague mode description.
2. **Run `context7` (or equivalent docs lookup) for the candidate** — this is **mandatory for every lead library/SDK/framework candidate**, not optional. Minimum three queries per candidate:
1. *Mode enumeration*: "What modes/configurations does `<library>` support? List every value of the mode/config enum and what each requires as input."
2. *Project's exact mode*: "Show a minimum runnable example of `<library>` in `<the pinned mode>` with `<the project's input shape>`. What does it produce?"
3. *Disqualifier probe*: "Does `<library>` `<the pinned mode>` produce `<the required output>`? Are there published limitations of `<the pinned mode>` for `<the project's runtime/hardware>`?"
For services without context7 coverage, use official docs site + WebFetch on the API reference page + the project's example/tutorial directory in the source repo. Append every consulted URL to `01_source_registry.md` (or the appropriate category file under `01_source_registry/` if split — see splittable-artifacts convention in `00_project-integration.md`).
3. **Save a Minimum Viable Example (MVE) for the pinned mode.**
Append to `02_fact_cards.md` / `02_fact_cards/` (or a sibling `02_mve_evidence.md`) at least one block per lead library candidate with:
```markdown
## MVE — <library> in <pinned mode>
- **Source**: <official URL or context7 reference, with date>
- **Inputs in the example**: <e.g., 2 calibrated cameras + IMU at 200 Hz>
- **Outputs in the example**: <e.g., 6-DoF pose with covariance>
- **Project inputs**: <e.g., 1 camera + IMU at 200 Hz>
- **Project outputs required**: <e.g., 6-DoF pose with metric translation>
- **Match assessment**: ✅ exact match / ⚠️ partial (specify dimension) / ❌ mismatch (specify dimension)
- **If ⚠️ or ❌**: cite the official-docs sentence that establishes the mismatch.
```
If no official example covers the project's exact configuration → the candidate cannot be marked `Selected` based on category fit alone. Status must be `Experimental only` (with required-evidence note) or `Rejected` (when the docs explicitly disqualify the configuration).
4. **Bind every numbered Restriction and Acceptance Criterion to the candidate's pinned mode.**
For each numbered line in `restrictions.md` and `acceptance_criteria.md`, decide one of: `Pass` (the pinned mode satisfies it with cited evidence), `Fail` (the pinned mode contradicts it with cited evidence), `Verify` (no evidence either way; deeper investigation required), `N/A` (the line is irrelevant to this component area). Record this in `02_fact_cards.md` (or the candidate's per-component file under `02_fact_cards/` if split) under the candidate's MVE block. The structural matrix in Step 7.5 reads from these bindings.
5. **Treat "the same library in a different mode" as a different candidate.**
If the project's pinned mode is `Monocular` but the only documented evidence covers `Stereo`, do not silently soften "rotation only" into "rotation + translation". Open a separate candidate row for the Monocular mode, with its own MVE, fit assessment, and disqualifiers. Two modes of one library are two distinct candidates for the purposes of this gate.
**Common silent-failure pattern this guards against**: a fact card paraphrases the docs as "supports A, B, C, D modes" when the docs actually mean "supports A; B; C and D as separate orthogonal modes". A category-level "Selected" decision then carries through every downstream artifact, masking that the project's required A+B combination does not exist as a single mode.
**Search broadening strategies** (use when results are thin):
- Try adjacent fields: if researching "drone indoor navigation", also search "robot indoor navigation", "warehouse AGV navigation"
- Try different communities: academic papers, industry whitepapers, military/defense publications, hobbyist forums
- Try different geographies: search in English + search for European/Asian approaches if relevant
- Try historical evolution: "history of X", "evolution of X approaches", "X state of the art 2024 2025"
- Try failure analysis: "X project failure", "X post-mortem", "X recall", "X incident report"
- Try disqualifier probes: "X unsupported", "X limitations", "X requirements", "X with [project constraint]", "X without [required input]", "X real-time [target]", "X production failure"
**Search saturation rule**: Continue searching until new queries stop producing substantially new information. If the last 3 searches only repeat previously found facts, the sub-question is saturated.
**Save action**:
For each source consulted, **immediately** append to `01_source_registry.md` (or the appropriate category file under `01_source_registry/` if split) using the entry template from `references/source-tiering.md`.
For each source consulted, **immediately** append to `01_source_registry.md` using the entry template from `references/source-tiering.md`.
---
@@ -273,7 +185,7 @@ Transform sources into **verifiable fact cards**:
- ❓ Low: Inference or from unofficial sources
**Save action**:
For each extracted fact, **immediately** append to `02_fact_cards.md` (or the appropriate category file under `02_fact_cards/` if split):
For each extracted fact, **immediately** append to `02_fact_cards.md`:
```markdown
## Fact #[number]
- **Statement**: [specific fact description]
@@ -282,7 +194,6 @@ For each extracted fact, **immediately** append to `02_fact_cards.md` (or the ap
- **Target Audience**: [which group this fact applies to, inherited from source or further refined]
- **Confidence**: ✅/⚠️/❓
- **Related Dimension**: [corresponding comparison dimension]
- **Fit Impact**: [supports selection / disqualifies / makes experimental / needs user decision]
```
**Target audience in fact statements**:
@@ -318,7 +229,7 @@ After initial fact extraction, review what you have found and identify **knowled
- Failure cases and edge conditions
- Recent developments that may change the picture
4. **Update artifacts**: Append new sources to `01_source_registry.md`, new facts to `02_fact_cards.md` (use the appropriate category files under `01_source_registry/` and `02_fact_cards/` if split)
4. **Update artifacts**: Append new sources to `01_source_registry.md`, new facts to `02_fact_cards.md`
**Exit criteria**: Proceed to Step 4 when:
- Every sub-question has at least 3 facts with at least one from L1/L2
.cursor/skills/research/steps/04_engine-analysis.md
+2 -76
View File
@@ -24,18 +24,6 @@ Write to `03_comparison_framework.md`:
| ... | | | |
```
**Required exact-fit dimensions for component/tool decisions**:
When the output selects or recommends a component, tool, library, service, architecture pattern, or algorithm, the framework MUST include these dimensions unless explicitly not applicable:
- Option family (`Simple baseline`, `Established production`, `Open-source`, `Commercial/vendor`, `Current SOTA`, `Adjacent-domain`, `No-build/defer`, `Known bad`)
- Required inputs/outputs and ownership boundaries
- Operating context and lifecycle fit
- Non-functional envelope fit
- Implementation assumptions and hard disqualifiers
- Evidence quality and source tier
- Selection status (`Selected`, `Rejected`, `Experimental only`, `Needs user decision`)
For each component area, include multiple candidates in the initial population. Do not present only the preferred option unless the investigation found no realistic alternatives; if so, state the searches that proved the narrow landscape.
---
### Step 5: Reference Point Baseline Alignment
@@ -109,8 +97,6 @@ Validate conclusions against a typical scenario:
- [ ] Are there any important dimensions missed?
- [ ] Is there any over-extrapolation?
- [ ] Are conclusions actionable/verifiable?
- [ ] Does every selected component/tool/pattern match the Project Constraint Matrix?
- [ ] Are mismatches marked as disqualifiers instead of hidden as generic "limitations"?
**Save action**:
Write to `05_validation_log.md`:
@@ -142,66 +128,6 @@ If using Y: [expected behavior]
---
### Step 7.5: Component Applicability Gate (BLOCKING)
**Applicability**: this gate applies only when the run is classified as **Technical-component selection** in the SKILL's Research Output Class section. For non-technical research (concept comparison, market/policy investigation, root-cause analysis without tooling, knowledge organization), skip this entire step and proceed to Step 8 — there are no components to gate. State the skip once in `05_validation_log.md`: `Step 7.5 (Component Applicability Gate): not applicable — Non-technical investigation`. For mixed runs (some component areas technical, some not), apply this gate only to the technical component areas; the non-technical ones do not produce 7.5 rows.
Before finalizing the solution draft, build an exact-fit matrix for every component/tool/library/service/pattern/algorithm that is selected, recommended, rejected, or treated as a fallback. Free-form prose in a "Project Constraints Checked" column is **not sufficient** — mismatches hide inside rationale text. The matrix must be structured per restriction and per acceptance criterion.
#### 7.5.1 Top-level Component Fit Matrix
```markdown
# Component Fit Matrix
| Component Area | Candidate | Pinned Mode/Config | Option Family | Intended Role | API Capability Evidence | Mismatches / Disqualifiers | Status | Decision Rationale |
|----------------|-----------|--------------------|---------------|---------------|-------------------------|----------------------------|--------|--------------------|
| [area] | [name] | [exact mode/config the project will use, copied verbatim from the MVE block in Step 2] | [family] | [role] | MVE: [link to MVE block in `02_fact_cards.md` / `02_fact_cards/` or `02_mve_evidence.md`]; docs: [Source #] | [none / list] | Selected / Rejected / Experimental only / Needs user decision | [why] |
```
The new **Pinned Mode/Config** column is mandatory. A row without a pinned mode is incomplete. The new **API Capability Evidence** column links to the Minimum Viable Example saved during Step 2's API Capability Verification — without an MVE link the candidate cannot be `Selected`.
#### 7.5.2 Restrictions × Candidate-Modes Sub-Matrix (MANDATORY)
For each lead candidate row in the top-level matrix, append a structured cross-check that walks every numbered line of `restrictions.md` and `acceptance_criteria.md` against the candidate's **pinned mode/config**.
```markdown
## Sub-Matrix — <Candidate Name> in <Pinned Mode>
| Restriction / AC | Candidate-mode behavior | Result | Evidence |
|------------------|-------------------------|--------|----------|
| R1: <verbatim line from restrictions.md> | <how the pinned mode behaves under this restriction> | ✅ Pass / ❌ Fail / ❓ Verify / N/A | [Fact # / Source # / MVE link] |
| R2: ... | ... | ... | ... |
| ... | ... | ... | ... |
| AC-1.1: <verbatim line from acceptance_criteria.md> | <how the pinned mode satisfies (or contradicts) this AC's measurable target> | ✅ / ❌ / ❓ / N/A | [Fact # / Source # / MVE link] |
| AC-1.2: ... | ... | ... | ... |
| ... | ... | ... | ... |
```
Cell semantics:
- ✅ **Pass** — the candidate's pinned mode satisfies this line, with cited official-doc or MVE evidence.
- ❌ **Fail** — the candidate's pinned mode contradicts this line, with cited evidence. Even one ❌ disqualifies the candidate from `Selected` status.
- ❓ **Verify** — no evidence yet either way; further investigation required (loops back to Step 2 / Step 3.5). A row left ❓ at the end of analysis blocks the candidate.
- **N/A** — the line is irrelevant to this component area (state why in one phrase).
A candidate row may not be marked `Selected` while any cell is ❌ or ❓.
#### 7.5.3 Decision Rules
- `Selected` is allowed only when (a) the top-level row has an MVE link, (b) the sub-matrix has zero ❌, (c) the sub-matrix has zero ❓, and (d) the candidate's documented implementation assumptions match the project's explicit constraints and acceptance criteria.
- `Experimental only` is required when a candidate might work but lacks proof for the exact operating context (e.g., MVE exists for a similar configuration but not the exact one).
- `Rejected` is required when documented assumptions conflict with project constraints (any sub-matrix row is ❌ with cited evidence).
- `Needs user decision` is required when a mismatch changes scope, cost, safety, product behavior, or acceptance criteria — and the user has not yet been consulted.
- Each component area must include at least one selected or fallback-safe option, plus the most credible rejected/experimental alternatives discovered during web research.
- A component area with only one candidate is incomplete unless `00_question_decomposition.md` documents the broader searches and why they yielded no realistic alternatives.
- A candidate may not appear as the lead solution in Step 8 unless this gate marks it `Selected`.
- "Validation gate required" footnotes are not equivalent to `Selected`. If the validation gate concerns API capability (does the mode produce the required output?), that is a Step-2 / Step-7.5 question and must be resolved here, not deferred to runtime. Only validation gates concerning *runtime quality* (e.g., "does this VO converge on this terrain class?") may be carried forward as `Selected with runtime gate`.
**Save action**: Write `06_component_fit_matrix.md` (or, when split, the equivalent files under `06_component_fit_matrix/` — typically `00_summary.md` for the top-level matrix plus per-component sub-matrix files) containing both 7.5.1 (top-level) and 7.5.2 (per-candidate sub-matrices).
**BLOCKING**: If any lead candidate has ❌, ❓, `Experimental only`, `Rejected`, or `Needs user decision` status, do not silently proceed. Ask the user or choose a different selected candidate.
---
### Step 8: Deliverable Formatting
Make the output **readable, traceable, and actionable**.
@@ -213,8 +139,8 @@ Integrate all intermediate artifacts. Write to `OUTPUT_DIR/solution_draft##.md`
Sources to integrate:
- Extract background from `00_question_decomposition.md`
- Reference key facts from `02_fact_cards.md` (or files under `02_fact_cards/` if split)
- Reference key facts from `02_fact_cards.md`
- Organize conclusions from `04_reasoning_chain.md`
- Generate references from `01_source_registry.md` (or files under `01_source_registry/` if split)
- Generate references from `01_source_registry.md`
- Supplement with use cases from `05_validation_log.md`
- For Mode A: include AC assessment from `00_ac_assessment.md`
.cursor/skills/research/templates/solution_draft_mode_a.md
+4 -13
View File
@@ -10,21 +10,12 @@
[Architecture solution that meets restrictions and acceptance criteria.]
> **Applicability** — the table columns `Pinned Mode/Config` and `API Capability Evidence` apply only to technical-component runs (per SKILL.md → Research Output Class). For non-technical research outputs (concept comparison, market/policy report, investigation answer), this Architecture section may be replaced with a comparison/analysis section that does not use these columns; or the columns may be marked `N/A` per row when the row describes a non-technical "component" (a process, a policy, an organizational construct). For mixed runs, fill the columns only on rows that describe libraries/SDKs/frameworks/services/protocols/data formats/algorithms.
### Component: [Component Name]
| Solution | Tools | Pinned Mode/Config | Advantages | Limitations | Requirements | Security | Cost | API Capability Evidence | Fit |
|----------|-------|--------------------|-----------|-------------|-------------|----------|------|-------------------------|-----|
| [Option 1] | [lib/platform] | [exact mode/config used: inputs, outputs, runtime] | [pros] | [cons] | [intrinsic requirements] | [security] | [cost] | MVE: [link to MVE block]; docs: [Source #] | [Selected / Rejected / Experimental only / Needs user decision — cite exact-fit evidence and disqualifiers] |
| [Option 2] | [lib/platform] | [exact mode/config used] | [pros] | [cons] | [intrinsic requirements] | [security] | [cost] | MVE: [link]; docs: [Source #] | [Selected / Rejected / Experimental only / Needs user decision] |
**Exact-fit evidence**:
- Project constraints checked: [inputs/outputs, operating context, lifecycle, NFRs, acceptance criteria]
- Evidence: [Fact # / Source #]
- Disqualifiers: [none or list]
- Restrictions × Candidate-Modes sub-matrix: see `06_component_fit_matrix.md` (or `06_component_fit_matrix/` if split) § <Candidate Name>
- API capability gates: ✅ MVE saved / ⚠️ partial — see disqualifiers / ❌ no MVE — candidate is Experimental only or Rejected
| Solution | Tools | Advantages | Limitations | Requirements | Security | Cost | Fit |
|----------|-------|-----------|-------------|-------------|----------|------|-----|
| [Option 1] | [lib/platform] | [pros] | [cons] | [reqs] | [security] | [cost] | [fit assessment] |
| [Option 2] | [lib/platform] | [pros] | [cons] | [reqs] | [security] | [cost] | [fit assessment] |
[Repeat per component]
.cursor/skills/research/templates/solution_draft_mode_b.md
+4 -13
View File
@@ -13,21 +13,12 @@
[Architecture solution that meets restrictions and acceptance criteria.]
> **Applicability** — the table columns `Pinned Mode/Config` and `API Capability Evidence` apply only to technical-component runs (per SKILL.md → Research Output Class). For non-technical assessment outputs (e.g., reassessing a policy approach, comparing organizational designs), this Architecture section may be replaced with the assessment content that does not use these columns; or the columns may be marked `N/A` per row for non-technical "components". For mixed runs, fill the columns only on rows that describe libraries/SDKs/frameworks/services/protocols/data formats/algorithms.
### Component: [Component Name]
| Solution | Tools | Pinned Mode/Config | Advantages | Limitations | Requirements | Security | Performance | API Capability Evidence | Fit |
|----------|-------|--------------------|-----------|-------------|-------------|----------|------------|-------------------------|-----|
| [Option 1] | [lib/platform] | [exact mode/config used: inputs, outputs, runtime] | [pros] | [cons] | [intrinsic requirements] | [security] | [perf] | MVE: [link to MVE block]; docs: [Source #] | [Selected / Rejected / Experimental only / Needs user decision — cite exact-fit evidence and disqualifiers] |
| [Option 2] | [lib/platform] | [exact mode/config used] | [pros] | [cons] | [intrinsic requirements] | [security] | [perf] | MVE: [link]; docs: [Source #] | [Selected / Rejected / Experimental only / Needs user decision] |
**Exact-fit evidence**:
- Project constraints checked: [inputs/outputs, operating context, lifecycle, NFRs, acceptance criteria]
- Evidence: [Fact # / Source #]
- Disqualifiers: [none or list]
- Restrictions × Candidate-Modes sub-matrix: see `06_component_fit_matrix.md` (or `06_component_fit_matrix/` if split) § <Candidate Name>
- API capability gates: ✅ MVE saved / ⚠️ partial — see disqualifiers / ❌ no MVE — candidate is Experimental only or Rejected
| Solution | Tools | Advantages | Limitations | Requirements | Security | Performance | Fit |
|----------|-------|-----------|-------------|-------------|----------|------------|-----|
| [Option 1] | [lib/platform] | [pros] | [cons] | [reqs] | [security] | [perf] | [fit assessment] |
| [Option 2] | [lib/platform] | [pros] | [cons] | [reqs] | [security] | [perf] | [fit assessment] |
[Repeat per component]
.cursor/skills/test-run/SKILL.md
+2 -13
View File
@@ -22,7 +22,7 @@ test-run has two modes. The caller passes the mode explicitly; if missing, defau
| Mode | Scope | Typical caller | Input artifacts |
|------|-------|---------------|-----------------|
| `functional` (default) | Unit / integration / blackbox tests — correctness | autodev Steps that verify after Implement Tests or Implement | `scripts/run-tests.sh`, `_docs/02_document/tests/environment.md`, `_docs/02_document/tests/blackbox-tests.md` |
| `perf` | Performance / load / stress / soak tests — latency, throughput, error-rate thresholds | autodev greenfield Step 15, existing-code Step 15 (pre-deploy) | `scripts/run-performance-tests.sh`, `_docs/02_document/tests/performance-tests.md`, AC thresholds in `_docs/00_problem/acceptance_criteria.md` |
| `perf` | Performance / load / stress / soak tests — latency, throughput, error-rate thresholds | autodev greenfield Step 9, existing-code Step 15 (pre-deploy) | `scripts/run-performance-tests.sh`, `_docs/02_document/tests/performance-tests.md`, AC thresholds in `_docs/00_problem/acceptance_criteria.md` |
Direct user invocation (`/test-run`) defaults to `functional`. If the user says "perf tests", "load test", "performance", or passes a performance scenarios file, run `perf` mode.
@@ -32,17 +32,6 @@ After selecting a mode, read its corresponding workflow below; do not mix them.
## Functional Mode
### 0. System-Under-Test Reality Gate
Before accepting any functional, blackbox, or e2e result as a pass, verify what the tests actually exercised.
1. If `_docs/00_problem/input_data/expected_results/results_report.md` exists, at least one e2e/blackbox run must compare actual product outputs against that mapping or the machine-readable files it references.
2. Stubs are allowed only for external systems outside the product boundary: flight controller/SITL, QGC observer, satellite-provider/Suite service, physical Jetson hardware, physical camera, unavailable licensed datasets, and network services.
3. Stubs, fakes, deterministic fallbacks, monkeypatches, or direct replacement of internal product modules are not allowed for the behavior under test. Internal examples include VIO, safety/anchor wrapper, satellite retrieval, anchor verification, tile manager, MAVLink output adapter, FDR, and the A-Z localization pipeline.
4. If tests pass only because an internal module is fake/scaffolded, classify the run as **failed** with category `missing product implementation`.
5. If a scenario is blocked because external hardware/data is absent, verify the production code path exists before accepting the block as legitimate. Missing internal production code is not an environment block.
6. If the test runner writes CSV/Markdown reports, inspect them. A zero exit code is not enough; blocked/internal-stubbed scenarios still require classification.
### 1. Detect Test Runner
Check in order — first match wins:
@@ -105,7 +94,7 @@ Categorize skips as: **explicit skip (dead code)**, **runtime skip (unreachable)
### 5. Handle Outcome
**All tests pass, zero skipped, and the System-Under-Test Reality Gate passes** → return success to the autodev for auto-chain.
**All tests pass, zero skipped** → return success to the autodev for auto-chain.
**Any test fails or errors** → this is a **blocking gate**. Never silently ignore failures. **Always investigate the root cause before deciding on an action.** Read the failing test code, read the error output, check service logs if applicable, and determine whether the bug is in the test or in the production code.
.cursor/skills/test-spec/templates/expected-results.md
+2 -2
View File
@@ -95,7 +95,7 @@ Examples:
File: `expected_results/image_01_detections.json`
```json
​```json
{
"input": "image_01.jpg",
"expected": {
@@ -119,7 +119,7 @@ File: `expected_results/image_01_detections.json`
]
}
}
```
​```
```
---
.dockerignore
-29
View File
@@ -1,29 +0,0 @@
.git/
.github/
.venv/
venv/
env/
__pycache__/
*.py[cod]
.pytest_cache/
.mypy_cache/
.ruff_cache/
.coverage*
htmlcov/
build/
dist/
_skbuild/
CMakeFiles/
CMakeCache.txt
cmake_install.cmake
*.engine
*.calib
*.index
*.faiss
*.onnx
tests/fixtures/large_replays/
tests/fixtures/flight_derkachi/
tests/fixtures/tiles_corpus/
_docs/
*.log
.DS_Store
.editorconfig
-24
View File
@@ -1,24 +0,0 @@
root = true
[*]
charset = utf-8
end_of_line = lf
insert_final_newline = true
trim_trailing_whitespace = true
indent_style = space
indent_size = 4
[*.{yml,yaml,json,toml}]
indent_size = 2
[*.{cpp,c,h,hpp,cc,hh}]
indent_size = 4
[*.{cmake,CMakeLists.txt}]
indent_size = 2
[Makefile]
indent_style = tab
[*.md]
trim_trailing_whitespace = false
.env.example
-53
View File
@@ -1,53 +0,0 @@
# gps-denied-onboard — environment variables
# See _docs/02_document/module-layout.md and AZ-263_initial_structure.md § Environment Variables.
# Required: selects the FC adapter at the composition root.
# One of: ardupilot_plane | inav
GPS_DENIED_FC_PROFILE=ardupilot_plane
# Required: runtime tier gate; 1=workstation/CI, 2=Jetson production
GPS_DENIED_TIER=1
# Required: Postgres connection used by C6 (tile cache + descriptor index)
DB_URL=postgresql://gps_denied:dev@db:5432/gps_denied
# Required (dev/operator only): satellite-provider base URL for tile download
# Not set in flight (no egress)
SATELLITE_PROVIDER_URL=http://mock-sat:5100
# Required: path to JSON camera calibration loaded at startup
CAMERA_CALIBRATION_PATH=/fixtures/calibration/adti26.json
# Required: structured log level (DEBUG | INFO | WARNING | ERROR)
LOG_LEVEL=DEBUG
# Required: structured log sink (console | journald | fdr)
LOG_SINK=console
# Required (production): per-flight MAVLink 2.0 signing key path
# Dev key from tests/fixtures/mavlink_signing/dev_key in dev-tier1.
MAVLINK_SIGNING_KEY=tests/fixtures/mavlink_signing/dev_key
# CMake / runtime BUILD_* gating flags
# Defaults below match the airborne deployment binary (ADR-002 / ADR-011).
# Strategy flags use OFF for opt-in non-default strategies; ON for the
# deployment defaults that the runtime expects to be linked.
BUILD_VINS_MONO=OFF
BUILD_SALAD=OFF
BUILD_C11_TILE_MANAGER=OFF
# Replay-mode strategy flags (ADR-011) — must be ON in the airborne and
# research binaries so replay can run from the same image. The CI test
# compose files already set these explicitly; production sets them ON.
# BUILD_VIDEO_FILE_FRAME_SOURCE=ON
# BUILD_TLOG_REPLAY_ADAPTER=ON
# BUILD_REPLAY_SINK_JSONL=ON
# Dev-only: enables `signing_key_source='dev_static'` on the AP FC adapter.
# MUST stay OFF on production images; ON only in dev/CI containers.
# BUILD_DEV_STATIC_KEY=OFF
# Required: C7 inference backend (tensorrt | pytorch_fp16 | onnx_trt_ep)
INFERENCE_BACKEND=pytorch_fp16
# Required: filesystem paths for runtime artifacts
FDR_PATH=/var/lib/gps-denied/fdr
TILE_CACHE_PATH=/var/lib/gps-denied/tiles
.github/pull_request_template.md
+15
View File
@@ -0,0 +1,15 @@
## Summary
[1-3 bullet points describing the change]
## Related Tasks
[JIRA-ID links]
## Testing
- [ ] Unit tests pass
- [ ] Integration tests pass
- [ ] Manual testing done (if applicable)
## Checklist
- [ ] No new linter warnings
- [ ] No secrets committed
- [ ] API docs updated (if applicable)
.github/workflows/ci-tier2.yml
-25
View File
@@ -1,25 +0,0 @@
name: ci-tier2
on:
push:
branches: [stage, main]
workflow_dispatch:
jobs:
build-tier2:
runs-on: [self-hosted, jetson, orin-nano-super]
steps:
- uses: actions/checkout@v4
- name: Native build (deployment)
run: |
cmake -S . -B build -DBUILD_VINS_MONO=OFF -DBUILD_VPR_SALAD=OFF -DBUILD_C11_TILE_MANAGER=OFF
cmake --build build --parallel
ac-bound-nfts:
runs-on: [self-hosted, jetson, orin-nano-super]
needs: build-tier2
steps:
- uses: actions/checkout@v4
- name: AC-bound NFTs (NFT-PERF / NFT-LIM / NFT-RES / NFT-SEC / IT-12)
run: |
pytest -m tier2 -q tests/perf tests/security tests/resilience
.github/workflows/ci.yml
-104
View File
@@ -1,104 +0,0 @@
name: ci-tier1
on:
push:
branches: [dev, stage, main]
pull_request:
branches: [dev, stage, main]
jobs:
lint:
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: "3.10"
# AZ-300 — `[inference]` (torch + torchvision + onnxruntime) is now
# required for `mypy src` to type-check `c7_inference.pytorch_fp16_runtime`
# and for `pytest` to collect `test_pytorch_fp16_runtime.py`. Tier-1
# CI uses the CPU-only torch wheel; CUDA-gated tests skip themselves
# via `pytest.mark.skipif(not torch.cuda.is_available(), ...)`.
- run: pip install -e ".[dev,inference]"
- run: ruff check src tests
- run: mypy src
unit:
runs-on: ubuntu-22.04
needs: lint
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: "3.10"
- run: pip install -e ".[dev,inference]"
- name: pytest unit (per-component coverage gate)
run: pytest -q --cov=gps_denied_onboard --cov-fail-under=75 tests/unit
integration:
runs-on: ubuntu-22.04
needs: unit
steps:
- uses: actions/checkout@v4
- name: docker compose up
run: docker compose -f docker-compose.test.yml up --abort-on-container-exit --exit-code-from e2e-runner --build
build:
name: build-${{ matrix.kind }}
runs-on: ubuntu-22.04
needs: lint
strategy:
fail-fast: false
matrix:
kind: [deployment, research]
include:
# AZ-332 — BUILD_OKVIS2 forced OFF in Tier-1 CI until the tier2
# follow-up wires `okvis::ThreadedKFVio` end-to-end. The C++
# binding skeleton + CMake glue still ship in this build; full
# OKVIS2 native compile is gated on installing Ceres-solver +
# OKVIS2 vendored submodules (BRISK, DBoW2) via apt, plus
# `submodules: recursive` checkout. That CI lift is the
# tier2 task's surface, not AZ-332's.
- kind: deployment
cmake_flags: >-
-DBUILD_OKVIS2=OFF -DBUILD_VINS_MONO=OFF
-DBUILD_VPR_SALAD=OFF -DBUILD_C11_TILE_MANAGER=OFF
- kind: research
cmake_flags: >-
-DBUILD_OKVIS2=OFF -DBUILD_VINS_MONO=ON -DBUILD_VPR_SALAD=ON
steps:
- uses: actions/checkout@v4
- run: cmake -S . -B build ${{ matrix.cmake_flags }}
- run: cmake --build build --parallel
sbom-diff:
runs-on: ubuntu-22.04
needs: build
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: "3.10"
- name: SBOM diff (ADR-002 enforcement)
run: python ci/sbom_diff.py --deployment build-deployment-sbom.json --research build-research-sbom.json
security:
runs-on: ubuntu-22.04
needs: build
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: "3.10"
- run: pip install pip-audit
- run: pip-audit -r pyproject.toml || true
- name: OpenCV pin gate (D-CROSS-CVE-1)
run: python ci/opencv_pin_gate.py --pyproject pyproject.toml
push-images:
runs-on: ubuntu-22.04
if: github.event_name == 'push' && contains(fromJson('["refs/heads/dev","refs/heads/stage","refs/heads/main"]'), github.ref)
needs: [unit, integration, build, sbom-diff, security]
steps:
- uses: actions/checkout@v4
- run: echo "push images to GHCR (deployment + research) — wiring lands per release task"
.github/workflows/cve-rescan.yml
-19
View File
@@ -1,19 +0,0 @@
name: cve-rescan
on:
schedule:
- cron: "0 5 1 * *" # 05:00 UTC on the 1st of each month
workflow_dispatch:
jobs:
rescan:
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: "3.10"
- run: pip install pip-audit
- run: pip-audit -r pyproject.toml
- name: OpenCV pin gate (D-CROSS-CVE-1)
run: python ci/opencv_pin_gate.py --pyproject pyproject.toml
.github/workflows/release.yml
-24
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@@ -1,24 +0,0 @@
name: release
on:
push:
tags:
- "v*"
jobs:
jetpack-image:
runs-on: [self-hosted, jetson, orin-nano-super]
steps:
- uses: actions/checkout@v4
- name: Build JetPack image
run: echo "JetPack image build + sign + attest — concrete wiring lands per deploy task"
operator-orchestrator-tarball:
runs-on: ubuntu-22.04
needs: jetpack-image
steps:
- uses: actions/checkout@v4
- name: Bundle operator-orchestrator tarball
run: |
mkdir -p dist
tar -czf dist/operator-orchestrator.tar.gz docker-compose.yml docker/ _docs/
.gitignore
-69
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@@ -1,70 +1 @@
# Python
__pycache__/
*.py[cod]
*$py.class
*.so
*.egg
*.egg-info/
.eggs/
.pytest_cache/
.coverage
.coverage.*
coverage.xml
htmlcov/
.mypy_cache/
.ruff_cache/
.tox/
.venv/
venv/
env/
# Build artifacts
build/
dist/
_skbuild/
CMakeFiles/
CMakeCache.txt
cmake_install.cmake
Makefile
compile_commands.json
# Native engines and caches
*.engine
*.calib
*.index
*.faiss
*.onnx
*.trt
# Test fixtures — large blobs are out-of-band
tests/fixtures/large_replays/
tests/fixtures/flight_derkachi/*.mp4
tests/fixtures/flight_derkachi/*.h264
tests/fixtures/flight_derkachi/*.tlog
tests/fixtures/tiles_corpus/*.jpg
tests/fixtures/tiles_corpus/*.png
e2e/fixtures/sitl_replay/
# Editor / OS noise
.idea/
.vscode/
.DS_Store
Thumbs.db
*.swp
*~
# Logs and runtime data
*.log
/var/lib/gps-denied/
fdr_output/
tile_cache/
e2e-results/
# Secrets
.env
.env.local
*.key
!tests/fixtures/mavlink_signing/dev_key
# Deploy rollback bookmark (written by scripts/stop-services.sh)
.previous-tags.env
.gitmodules
-6
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@@ -1,6 +0,0 @@
[submodule "cpp/pybind11/upstream"]
path = cpp/pybind11/upstream
url = https://github.com/pybind/pybind11.git
[submodule "cpp/okvis2/upstream"]
path = cpp/okvis2/upstream
url = https://github.com/smartroboticslab/okvis2.git
.woodpecker/01-test.yml
-43
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@@ -1,43 +0,0 @@
# Cycle-1 trigger: manual-only.
#
# Rationale (per _docs/04_deploy/ci_cd_pipeline.md → Decision Record):
# The Tier-1 e2e harness (docker-compose.test.yml + tests/e2e/Dockerfile)
# is heavy: TensorRT-class pytorch fp16, gtsam, Postgres 16, and the
# Derkachi replay clip. It is shipped opt-in until per-run wall-clock on
# the colocated arm64 Jetson agent is characterised.
#
# Flip-back (cycle-2 polish item #1 in _docs/04_deploy/ci_cd_pipeline.md):
# 1. Replace `event: [manual]` with `event: [push, pull_request, manual]`
# below.
# 2. Add `depends_on: [01-test]` to .woodpecker/02-build-push.yml.
when:
event: [manual]
branch: [dev, stage, main]
matrix:
include:
- PLATFORM: arm64
TAG_SUFFIX: arm
# - PLATFORM: amd64
# TAG_SUFFIX: amd
labels:
platform: ${PLATFORM}
steps:
- name: e2e
image: docker
commands:
- docker compose -f docker-compose.test.yml up --build --abort-on-container-exit --exit-code-from e2e-runner
volumes:
- /var/run/docker.sock:/var/run/docker.sock
- name: down
image: docker
when:
status: [success, failure]
commands:
- docker compose -f docker-compose.test.yml down -v
volumes:
- /var/run/docker.sock:/var/run/docker.sock
.woodpecker/02-build-push.yml
-85
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@@ -1,85 +0,0 @@
# Cycle-1 trigger: push + manual on dev/stage/main, NO depends_on.
#
# Rationale (per _docs/04_deploy/ci_cd_pipeline.md → Decision Record):
# 01-test.yml runs `event: [manual]` only in cycle-1, so a `depends_on:
# [01-test]` clause here would skip every push (no preceding test run to
# succeed against). The un-gated stance mirrors the `detections` deferral
# pattern documented in `../_infra/ci/README.md` → "detections deferral".
#
# Re-gate (cycle-2 polish item #1 in _docs/04_deploy/ci_cd_pipeline.md):
# Add `depends_on: [01-test]` below once .woodpecker/01-test.yml flips to
# `event: [push, pull_request, manual]`.
#
# Images pushed in cycle-1:
# - azaion/gps-denied-onboard-companion-tier1:${BRANCH}-${TAG_SUFFIX}
# - azaion/gps-denied-onboard-operator-orchestrator:${BRANCH}-${TAG_SUFFIX}
#
# Image NOT pushed in cycle-1 (reserved for cycle-2 / companion-jetson):
# - azaion/gps-denied-onboard:${BRANCH}-${TAG_SUFFIX}
# (parent-suite Jetson compose at ../_infra/deploy/jetson/docker-compose.yml
# expects this exact tag; cycle-1 must not write to it or Watchtower
# on fielded Jetsons will pull a Tier-1 dev image.)
#
# OCI labels (suite-mandated, AZ-204 — see ../_infra/ci/README.md → "OCI
# image labels and commit provenance"):
# org.opencontainers.image.revision = $CI_COMMIT_SHA
# org.opencontainers.image.created = <UTC RFC 3339>
# org.opencontainers.image.source = $CI_REPO_URL
# Plus --build-arg CI_COMMIT_SHA so the Dockerfile can bake ENV AZAION_REVISION.
when:
event: [push, manual]
branch: [dev, stage, main]
matrix:
include:
- PLATFORM: arm64
TAG_SUFFIX: arm
# - PLATFORM: amd64
# TAG_SUFFIX: amd
labels:
platform: ${PLATFORM}
steps:
- name: build-push-companion-tier1
image: docker
environment:
REGISTRY_HOST: { from_secret: registry_host }
REGISTRY_USER: { from_secret: registry_user }
REGISTRY_TOKEN: { from_secret: registry_token }
commands:
- echo "$REGISTRY_TOKEN" | docker login "$REGISTRY_HOST" -u "$REGISTRY_USER" --password-stdin
- export TAG=${CI_COMMIT_BRANCH}-${TAG_SUFFIX}
- export BUILD_DATE=$(date -u +%Y-%m-%dT%H:%M:%SZ)
- |
docker build -f docker/companion-tier1.Dockerfile \
--build-arg CI_COMMIT_SHA=$CI_COMMIT_SHA \
--label org.opencontainers.image.revision=$CI_COMMIT_SHA \
--label org.opencontainers.image.created=$BUILD_DATE \
--label org.opencontainers.image.source=$CI_REPO_URL \
-t $REGISTRY_HOST/azaion/gps-denied-onboard-companion-tier1:$TAG .
- docker push $REGISTRY_HOST/azaion/gps-denied-onboard-companion-tier1:$TAG
volumes:
- /var/run/docker.sock:/var/run/docker.sock
- name: build-push-operator-orchestrator
image: docker
environment:
REGISTRY_HOST: { from_secret: registry_host }
REGISTRY_USER: { from_secret: registry_user }
REGISTRY_TOKEN: { from_secret: registry_token }
commands:
- echo "$REGISTRY_TOKEN" | docker login "$REGISTRY_HOST" -u "$REGISTRY_USER" --password-stdin
- export TAG=${CI_COMMIT_BRANCH}-${TAG_SUFFIX}
- export BUILD_DATE=$(date -u +%Y-%m-%dT%H:%M:%SZ)
- |
docker build -f docker/operator-orchestrator.Dockerfile \
--build-arg CI_COMMIT_SHA=$CI_COMMIT_SHA \
--label org.opencontainers.image.revision=$CI_COMMIT_SHA \
--label org.opencontainers.image.created=$BUILD_DATE \
--label org.opencontainers.image.source=$CI_REPO_URL \
-t $REGISTRY_HOST/azaion/gps-denied-onboard-operator-orchestrator:$TAG .
- docker push $REGISTRY_HOST/azaion/gps-denied-onboard-operator-orchestrator:$TAG
volumes:
- /var/run/docker.sock:/var/run/docker.sock
CMakeLists.txt
-32
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@@ -1,32 +0,0 @@
cmake_minimum_required(VERSION 3.22)
project(gps_denied_onboard LANGUAGES CXX)
# Compile options ----------------------------------------------------------
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(CMAKE_CXX_EXTENSIONS OFF)
set(CMAKE_POSITION_INDEPENDENT_CODE ON)
if(NOT CMAKE_BUILD_TYPE)
set(CMAKE_BUILD_TYPE RelWithDebInfo CACHE STRING "Build type" FORCE)
endif()
# Helper modules -----------------------------------------------------------
list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake")
include(build_options)
include(dependencies)
include(strategies)
# Native subprojects -------------------------------------------------------
add_subdirectory(cpp)
# Tests --------------------------------------------------------------------
option(BUILD_TESTING "Enable native unit tests (C++ gtest)" OFF)
if(BUILD_TESTING)
enable_testing()
add_subdirectory(cpp/tests)
endif()
README.md
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@@ -1,26 +0,0 @@
# gps-denied-onboard
Companion onboard system for GPS-denied UAV navigation. Detailed design and architecture documentation lives under [`_docs/`](_docs/).
## Quick links
- Problem statement: [`_docs/00_problem/problem.md`](_docs/00_problem/problem.md)
- Architecture: [`_docs/02_document/architecture.md`](_docs/02_document/architecture.md)
- Module layout (file ownership): [`_docs/02_document/module-layout.md`](_docs/02_document/module-layout.md)
- Component docs: [`_docs/02_document/components/`](_docs/02_document/components/)
- Test specs: [`_docs/02_document/tests/`](_docs/02_document/tests/)
- Deployment: [`_docs/02_document/deployment/`](_docs/02_document/deployment/)
## Local development
```bash
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest -q tests/unit/
```
For full Tier-1 integration via Docker, see [`_docs/02_document/deployment/containerization.md`](_docs/02_document/deployment/containerization.md).
## Build matrix
Four binaries built from this codebase: **airborne**, **research**, **operator-orchestrator**, **replay-cli**. CMake `BUILD_*` flags gate component inclusion per binary — see [`cmake/build_options.cmake`](cmake/build_options.cmake) and [`_docs/02_document/module-layout.md` § Build-Time Exclusion Map](_docs/02_document/module-layout.md#build-time-exclusion-map-adr-002).
_docs/00_problem/acceptance_criteria.md
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@@ -1,109 +1,50 @@
# Acceptance Criteria
# Position Accuracy
> Last revised 2026-05-07 (cleanup pass: stripped algorithm/library/parameter implementation details; renamed source label `vo_extrapolated` → `visual_propagated`; broadened FC scope to ArduPilot + iNav).
> Subsequent revision 2026-05-07 (post-SQ6 research): AC-4.3 reworded to acknowledge that no single message type is accepted by both ArduPilot Plane and iNav — per-FC interface is named explicitly (MAVLink `GPS_INPUT` for ArduPilot Plane, MSP2 `MSP2_SENSOR_GPS` for iNav). Rationale and L1 sources in `_docs/00_research/02_fact_cards/SQ6_fc_external_positioning.md` / `_docs/00_research/01_source_registry/SQ6_external_positioning.md` Sources #4, #9, #10, #12, #13.
> Subsequent revision 2026-05-09 (Plan Phase 2a.0 outcomes): AC-NEW-4 and AC-NEW-7 validation requirements relaxed from "≥100 flights" literal to Monte-Carlo-with-stated-CI over currently-available data corpus; multi-flight statistical headroom moved to Step 4 risk register (D-PROJ-3). AC-8.4 augmented with explicit in-air-no-upload security gate (flight-state process-level isolation; post-landing upload tool); local mid-flight tile format pinned to match `satellite-provider`'s on-disk format. AC-NEW-7 external-dependency note revised: parent-suite voting layer is not currently implemented; tracked as parent-suite design task D-PROJ-2.
> See git history for prior versions.
- The system should determine GPS coordinates of frame centers for 80% of photos within 50m error compared to real GPS
- The system should determine GPS coordinates of frame centers for 60% of photos within 20m error compared to real GPS
- Maximum cumulative VO drift between satellite correction anchors should be less than 100 meters
- System should report a confidence score per position estimate (high = satellite-anchored, low = VO-extrapolated with drift)
## Position Accuracy
- **AC-1.1** — Frame-center GPS within **50 m** of true GPS for **≥80%** of normal-flight photos.
- **AC-1.2** — Frame-center GPS within **20 m** of true GPS for **≥50%** of normal-flight photos.
- **AC-1.3** — Cumulative drift between two consecutive satellite-anchored fixes: **<100 m** visual-only / **<50 m** with IMU fused. Measured as ‖propagated centre − next anchor centre‖ at anchor fix. Every estimate carries `last_satellite_anchor_age_ms`; validation binned by anchor age. The solution must define the max anchor age beyond which estimates degrade to `visual_propagated` / `dead_reckoned` with monotonically growing covariance.
- **AC-1.4** — Each estimate reports: 95% covariance ellipse semi-major axis (m) AND a label `{satellite_anchored, visual_propagated, dead_reckoned}`.
# Image Processing Quality
## Image Processing Quality
- **AC-2.1a — Frame-to-frame registration**: succeeds for **>95%** of normal flight segments (defined: nadir ±10° bank/pitch, ≥40% prior-frame overlap, daytime, usable texture, no full visual blackout).
- **AC-2.1b — Satellite-anchor registration**: measured separately from AC-2.1a; must satisfy AC-1.1/1.2 accuracy, AC-2.2 cross-domain MRE, AC-8.2 freshness, AC-8.6 retrieval behaviour.
- **AC-2.2** — Mean Reprojection Error: **<1.0 px** frame-to-frame; **<2.5 px** satellite-anchored cross-domain.
- Image Registration Rate > 95% for normal flight segments. The system can find enough matching features to confidently calculate the camera's 6-DoF pose and stitch that image into the trajectory
- Mean Reprojection Error (MRE) < 1.0 pixels
## Resilience & Edge Cases
- **AC-3.1** — Tolerate up to **350 m** outliers between two consecutive photos (airframe tilt up to ±20°).
- **AC-3.2** — Tolerate sharp turns: <5% overlap, <200 m drift, <70° heading change. Sharp-turn frames may fail frame-to-frame registration; recovery via satellite-reference re-localization.
- **AC-3.3** — Handle **≥3 disconnected segments** per flight via satellite-reference re-localization. Core capability, not degraded mode.
- **AC-3.4** — On ≥3 consecutive frames AND ≥2 s without a position, request operator re-loc via telemetry; continue dead-reckoned propagation; FC uses last known + IMU extrapolation.
- **AC-3.5 — Visual blackout + spoofed GPS** (clouds/occlusion/whiteout while FC reports GPS denial/spoof):
- Switch label to `{dead_reckoned}` within ≤1 processed frame OR ≤400 ms.
- Reject spoofed GPS as estimator input.
- Propagate from last trusted state + FC IMU/attitude/airspeed/altitude until visual or satellite anchoring recovers.
- Covariance grows monotonically.
- `horiz_accuracy` field of the GPS message to the FC must not under-report the 95% covariance semi-major axis.
- `VISUAL_BLACKOUT_IMU_ONLY` STATUSTEXT to QGroundControl at 1–2 Hz.
# Resilience & Edge Cases
## Real-Time Onboard Performance
- **AC-4.1** — End-to-end latency (camera capture → GPS to FC) **<400 ms p95**. Up to ~10% frames may drop under sustained load.
- **AC-4.2** — Memory **<8 GB shared** on Jetson Orin Nano Super.
- **AC-4.3 — FC output contract**: WGS84 coordinates delivered to each supported FC via that FC's documented external-positioning interface — MAVLink `GPS_INPUT` for ArduPilot Plane, MSP2 `MSP2_SENSOR_GPS` for iNav. Honest covariance is carried in the field each FC uses for outlier rejection (under-reported covariance is a defect, see AC-NEW-4). Source-label semantics per AC-1.4 are emitted out-of-band via the FC-appropriate channel (e.g. MAVLink `STATUSTEXT` / `NAMED_VALUE_FLOAT` for ArduPilot; MSP equivalent for iNav). Where the FC supports it, implementation may also emit an optional auxiliary external-odometry message when the estimator delivers full 6-DoF covariance + quality above a configured threshold. Per-FC parameter wiring (EKF source-set selection on ArduPilot; GPS provider / UART role on iNav), FDR-side message variants, and out-of-band channel choice remain design decisions.
- **AC-4.4** — Estimates streamed frame-by-frame; no batching/delay.
- **AC-4.5** — System may refine prior estimates and emit corrections.
- The system should correctly continue work even in the presence of up to 350m outlier between 2 consecutive photos (due to tilt of the plane)
- System should correctly continue work during sharp turns, where the next photo doesn't overlap at all or overlaps less than 5%. The next photo should be within 200m drift and at an angle of less than 70 degrees. Sharp-turn frames are expected to fail VO and should be handled by satellite-based re-localization
- System should operate when UAV makes a sharp turn and next photos have no common points with previous route. It should figure out the location of the new route segment and connect it to the previous route. There could be more than 2 such disconnected segments, so this strategy must be core to the system
- In case the system cannot determine the position of 3 consecutive frames by any means, it should send a re-localization request to the ground station operator via telemetry link. While waiting for operator input, the system continues attempting VO/IMU dead reckoning and the flight controller uses last known position + IMU extrapolation
## Startup & Failsafe
- **AC-5.1** — Initialise from FC EKF's last valid GPS + IMU-extrapolated position at GPS denial.
- **AC-5.2** — On >3 s without estimate, FC falls back to IMU-only dead reckoning; system logs failure. Verify in production param sets of each supported FC (ArduPilot Plane SITL + iNav SITL or equivalent).
- **AC-5.3** — On companion reboot mid-flight, re-initialise from FC's current IMU-extrapolated position. Cold-start TTFF in AC-NEW-1.
# Real-Time Onboard Performance
## Ground Station & Telemetry
- **AC-6.1** — Position estimates + confidence stream to QGroundControl over MAVLink at **1–2 Hz** downsampled (high-rate stays on local FDR).
- **AC-6.2** — GCS may send commands (e.g., operator re-loc hint) via standard MAVLink (`STATUSTEXT`, `NAMED_VALUE_FLOAT`) or a custom dialect.
- **AC-6.3** — Output coordinates in WGS84.
- Less than 400ms end-to-end per frame: from camera capture to GPS coordinate output to the flight controller (camera shoots at ~3fps)
- Memory usage should stay below 8GB shared memory (Jetson Orin Nano Super — CPU and GPU share the same 8GB LPDDR5 pool)
- The system must output calculated GPS coordinates directly to the flight controller via MAVLink GPS_INPUT messages (using MAVSDK)
- Position estimates are streamed to the flight controller frame-by-frame; the system does not batch or delay output
- The system may refine previously calculated positions and send corrections to the flight controller as updated estimates
## Object Localization (AI Camera)
- **AC-7.1** — AI systems may request GPS for AI-camera-detected objects. Accuracy consistent with frame-center accuracy in level flight (bank/pitch <5°). In maneuvering flight, error bounded by `altitude × |sin(unknown_bank_or_pitch)|` and that bound is published alongside the estimate.
- **AC-7.2** — Object coordinates computed trigonometrically from current UAV position, AI-camera gimbal angle, zoom, and altitude. Flat-terrain assumption.
# Startup & Failsafe
## Satellite Reference Imagery
- **AC-8.1** — Imagery via Azaion Suite Satellite Service (offline cache interface; no direct commercial-provider calls). Cache-interface resolution ≥0.5 m/px, ideally 0.3 m/px.
- **AC-8.2** — Tile freshness: <6 mo (active-conflict sectors), <12 mo (stable rear). Older → reject or downgrade (AC-NEW-6).
- **AC-8.3** — Imagery pre-loaded onto companion before flight; offline preprocessing time not time-critical. Pre-extracted descriptors/indices count against the cache budget unless explicitly carved out.
- **AC-8.4** — Mid-flight tile generation: continuously orthorectify nav-camera frames into basemap-projected tiles, deduplicated (latest/highest-quality wins). Tiles are written **only** to the local cache while airborne — in-air outbound writes to `satellite-provider` are **forbidden** for drone-security reasons; enforced by a `flight state` process-level gate (see `architecture.md`). Upload to `satellite-provider` happens **only after landing**, triggered by a separate operator-side post-landing upload tool. Local mid-flight tile format matches `satellite-provider`'s on-disk format so post-landing upload is byte-identical. Each uploaded tile carries quality metadata sufficient for the Service's ingest pipeline (AC-NEW-7).
- **AC-8.5** — No raw nav-camera or AI-camera frames retained in normal operation; tiles are the only persistent imagery. Forensic exception: ≤0.1 Hz thumbnail log of frames that failed tile generation, within FDR budget (AC-NEW-3).
- **AC-8.6 — Satellite-anchor relocalization robustness**:
- **Scale-ratio**: any UAV-frame ground footprint at the deployment altitude band must be retrievable from the cache regardless of internal tiling/indexing.
- **Scene change in active-conflict sectors**: cratering / building destruction / road realignment must not collapse retrieval recall, measured against a labelled change-pair dataset over season-matched tiles. No `satellite_anchored` label on stale-tile match (per AC-NEW-6).
- **Compute & latency**: relocalization must remain inside AC-4.1 latency + AC-4.2 memory budgets under both steady-state and re-loc-trigger workloads.
- The system initializes using the last known valid GPS position from the flight controller before GPS denial begins
- If the system completely fails to produce any position estimate for more than N seconds (TBD), the flight controller should fall back to IMU-only dead reckoning and the system should log the failure
- On companion computer reboot mid-flight, the system should attempt to re-initialize from the flight controller's current IMU-extrapolated position
## Additional AC
# Ground Station & Telemetry
### AC-NEW-1 — Cold-start TTFF
**Statement.** From companion boot, first valid external-position MAVLink frame **<30 s p95**, given an IMU-extrapolated initial position from FC EKF.
**Why.** Mid-flight reboot is realistic on 8 h missions; FC dead-reckons during the gap, ~500 m drift max at 60 km/h.
**Validation.** Cold-boot 50× with simulated FC pose; measure boot → first frame; pass = 95th percentile <30 s.
- Position estimates and confidence scores should be streamed to the ground station via telemetry link for operator situational awareness
- The ground station can send commands to the onboard system (e.g., operator-assisted re-localization hint with approximate coordinates)
- Output coordinates in WGS84 format
### AC-NEW-2 — Spoofing-promotion latency
**Statement.** When FC signals GPS denial/spoof, promote onboard estimate to FC's primary position source within **<3 s p95**.
**Why.** Without this, FC may follow a spoofed source while a valid onboard estimate sits idle; 3 s rides out one-frame anomalies but blocks malicious heading changes.
**Validation.** SITL on each supported FC (ArduPilot Plane + iNav, production param sets): inject false GPS, measure spoof onset → promotion; pass = 95th percentile <3 s on both.
# Object Localization
### AC-NEW-3 — Flight Data Recorder
**Statement.** Per flight, retain to NVM: per-frame estimates with covariance + source-label; FC IMU traces (full rate); all emitted external-position MAVLink frames; raw MAVLink stream (tlog); system health (CPU/GPU/temp/throttle); mid-flight tiles (AC-8.4); ≤0.1 Hz thumbnail log of failed tile-gen frames. **No raw nav-cam/AI-cam frames** (AC-8.5). Cap **64 GB / flight**; oldest segment dropped first on rollover.
**Why.** Tiles + telemetry + IMU reproduce the mission, feed next mission's cache (AC-8.4), explain false-position events (AC-NEW-4). Raw frames are large + redundant once tiles exist.
**Validation.** 8 h synthetic load (3 Hz nav frames replayed); assert FDR ≤64 GB; no payload class silently dropped without a logged rollover.
- Other onboard AI systems can request GPS coordinates of objects detected by the AI camera
- The GPS-Denied system calculates object coordinates trigonometrically using: current UAV GPS position (from GPS-Denied), known AI camera angle, zoom, and current flight altitude. Flat terrain is assumed
- Accuracy is consistent with the frame-center position accuracy of the GPS-Denied system
### AC-NEW-4 — False-position safety budget
**Statement.** Per flight: **P(error >500 m) <0.1 %**, **P(error >1 km) <0.01 %**.
**Why.** A single 1-km-off frame can fly the UAV outside the geofence; covariance carried in the MAVLink message is the FC's only defense.
**Validation.** Monte Carlo over the currently-available data corpus (Derkachi flight + 60 stills + synthetic perturbations); report error CDF with stated 95% confidence interval; pass = both probabilities below budget within the CI's lower bound. Multi-flight statistical headroom (originally framed as ≥100 flights) is residual risk tracked in the Step 4 risk register; **D-PROJ-3** reopens this validation when additional multi-flight data becomes available.
# Satellite Reference Imagery
### AC-NEW-5 — Operational environmental envelope
**Statement.** Operating temp **−20 °C to +50 °C**; vibration/shock per RTCA DO-160G low-altitude UAV-class. Cooling sustains **25 W** at the upper temp for the full **8-hour duty cycle** without throttling.
**Why.** Without this, all latency/accuracy AC are conditional on a benign thermal day; +35 °C bay temps cause Jetson to throttle to 15 W, collapsing the 400 ms latency budget.
**Validation.** Hot-soak: 25 W @ +50 °C for 8 h, no throttle. Cold-soak: −20 °C cold-start within AC-NEW-1.
### AC-NEW-6 — Imagery freshness enforcement
**Statement.** System rejects (or downgrades) any tile whose capture date violates AC-8.2. Mid-flight tiles (AC-8.4) not yet uploaded are timestamped current and treated as fresh.
**Why.** Stale tiles are the dominant cross-view-matching failure mode in active-conflict sectors; a confident match on a stale tile is worse than no match.
**Validation.** Inject synthetic-age tiles; verify rejection/decay matches spec; verify stale-tile match never produces `satellite_anchored`.
### AC-NEW-7 — Cache-poisoning safety budget
**Statement.** Per flight, across all onboard tiles written (AC-8.4): **P(geo-misalign >30 m) <1 %**, **P(>100 m) <0.1 %**.
**Why.** Onboard tiles feed back into the `satellite-provider` basemap when uploaded post-landing (AC-8.4). A bad onboard pose with optimistic covariance writes a misaligned tile that becomes the next flight's anchor — cross-flight error compounding that AC-NEW-4 doesn't capture.
**External-dependency note.** The parent-suite `satellite-provider` is expected to operate a multi-flight ingest-side trust/voting layer that gates onboard-tile promotion to "trusted basemap" until multiple independent flights agree on geo-alignment. The ingest endpoint and voting layer are **not currently implemented in `satellite-provider`** and are tracked as a parent-suite design task (**D-PROJ-2**). Onboard's job (AC-8.4) is to publish per-tile quality metadata sufficient for that layer. End-to-end AC-NEW-7 evidence depends on the `satellite-provider` contract being added.
**Validation.** Onboard-only Monte Carlo replay over the currently-available data corpus + synthetic over-confidence injection (deflate covariance ×1.5–3); report error CDF with stated 95% confidence interval; pass = both probabilities below budget within the CI's lower bound for the onboard-side contribution. Multi-flight statistical headroom and the `satellite-provider` voting-side contract verification are residual risks tracked in the Step 4 risk register; **D-PROJ-3** reopens onboard validation when additional multi-flight data becomes available; **D-PROJ-2** reopens cross-suite validation once the ingest + voting layer is built.
### AC-NEW-8 — Visual blackout + GPS spoofing degraded mode
**Statement.** When the navigation camera is fully unusable AND FC reports GPS denial/spoof:
- continue emitting external-position MAVLink frames from IMU-only propagation for **≤30 s** after the last trusted anchor (or until covariance trips fail threshold);
- label every estimate `{dead_reckoned}`; degrade MAVLink fix-quality to "2D fix or worse" when 95% covariance semi-major axis **>100 m**;
- escalate to "no fix" (`horiz_accuracy=999.0`) + `VISUAL_BLACKOUT_FAILSAFE` STATUSTEXT when 95% covariance >**500 m** OR blackout >**30 s** without a trusted re-anchor;
- never promote spoofed real-GPS back into the estimator unless FC GPS health stable + non-spoofed for **≥10 s** AND a visual/satellite consistency check has succeeded.
**Why.** During cloud/whiteout + spoofing, no honest correction is available; only safe behaviour is IMU-only dead reckoning with rapidly-growing uncertainty, never pretending stale visual or spoofed GPS remains valid.
**Validation.** SITL/replay on each FC: inject 5 s / 15 s / 35 s blackouts while spoofing GPS; assert mode transition ≤400 ms, spoofed GPS ignored, covariance grows monotonically, MAVLink fields degrade at thresholds, recovery only via trusted anchor or 10-s GPS-health + visual-consistency gate.
- Satellite reference imagery resolution must be at least 0.5 m/pixel, ideally 0.3 m/pixel
- Satellite imagery for the operational area should be less than 2 years old where possible
- Satellite imagery must be pre-processed and loaded onto the companion computer before flight. Offline preprocessing time is not time-critical (can take minutes/hours)
_docs/00_problem/input_data/data_parameters.md
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- Height: 400m
- Camera: ADTi Surveyor Lite 20MP 20L V1
- Height
- 400m
- Camera:
- Name: ADTi Surveyor Lite 26S v2
- Resolution: 26MP
- Image resolution: 6252*4168
- Focal length: 25mm
- Sensor width: 23.5
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# Expected Results Mapping
# Expected Results
## Scope
Maps every input data item to its quantifiable expected result.
Tests use this mapping to compare actual system output against known-correct answers.
`coordinates.csv` is the current source of truth for the provided still-image nadir set. It gives expected WGS84 frame-center coordinates for `AD000001.jpg` through `AD000060.jpg`.
## Result Format Legend
This data is sufficient for black-box frame-center geolocation tests against still images. The Derkachi representative fixture in `input_data/flight_derkachi/` adds cropped nadir video plus synchronized `SCALED_IMU2` and `GLOBAL_POSITION_INT` telemetry. It is sufficient for fixture validation, video/telemetry synchronization, replay, latency, VIO smoke tests, and trajectory comparison against the tlog GPS path. It is not sufficient by itself for final production accuracy because raw camera calibration, lens distortion, and exact camera-to-body calibration are still pending.
| Result Type | When to Use | Example |
|-------------|-------------|---------|
| Exact value | Output must match precisely | `fix_type: 3`, `satellites_visible: 10` |
| Tolerance range | Numeric output with acceptable variance | `lat: 48.275292 ± 50m` |
| Threshold | Output must exceed or stay below a limit | `latency < 400ms`, `memory < 8GB` |
| Pattern match | Output must match a string/regex pattern | `RELOC_REQ: last_lat=.* last_lon=.* uncertainty=.*m` |
| File reference | Complex output compared against a reference file | `match expected_results/position_accuracy.csv` |
| Set/count | Output must contain specific items or counts | `registered_frames / total_frames > 0.95` |
## Pass / Fail Rules
## Comparison Methods
- **Normal frame-center geolocation**: estimated frame center is within 50 m of the expected WGS84 coordinate.
- **Stretch accuracy bin**: estimated frame center is within 20 m of the expected WGS84 coordinate.
- **Dataset aggregate**: at least 80% of mapped images pass the 50 m threshold and at least 50% pass the 20 m threshold.
- **Output shape**: each result must include image name, estimated `lat`, estimated `lon`, error in meters, source label, 95% covariance semi-major axis, and `last_satellite_anchor_age_ms`.
| Method | Description | Tolerance Syntax |
|--------|-------------|-----------------|
| `numeric_tolerance` | abs(actual - expected) ≤ tolerance | `± <value>` |
| `threshold_min` | actual ≥ threshold | `≥ <value>` |
| `threshold_max` | actual ≤ threshold | `≤ <value>` |
| `percentage` | percentage of items meeting criterion | `≥ N%` |
| `exact` | actual == expected | N/A |
| `regex` | actual matches regex pattern | regex string |
| `file_reference` | compare against reference file | file path |
## Input To Expected Output Map
## Input → Expected Result Mapping
### Still-Image Frame Centers
### Position Accuracy (60-image flight sequence)
| Input image | Expected latitude | Expected longitude | Primary threshold | Stretch threshold |
|-------------|-------------------|--------------------|-------------------|-------------------|
| AD000001.jpg | 48.275292 | 37.385220 | <= 50 m | <= 20 m |
| AD000002.jpg | 48.275001 | 37.382922 | <= 50 m | <= 20 m |
| AD000003.jpg | 48.274520 | 37.381657 | <= 50 m | <= 20 m |
| AD000004.jpg | 48.274956 | 37.379004 | <= 50 m | <= 20 m |
| AD000005.jpg | 48.273997 | 37.379828 | <= 50 m | <= 20 m |
| AD000006.jpg | 48.272538 | 37.380294 | <= 50 m | <= 20 m |
| AD000007.jpg | 48.272408 | 37.379153 | <= 50 m | <= 20 m |
| AD000008.jpg | 48.271992 | 37.377572 | <= 50 m | <= 20 m |
| AD000009.jpg | 48.271376 | 37.376671 | <= 50 m | <= 20 m |
| AD000010.jpg | 48.271233 | 37.374806 | <= 50 m | <= 20 m |
| AD000011.jpg | 48.270334 | 37.374442 | <= 50 m | <= 20 m |
| AD000012.jpg | 48.269922 | 37.373284 | <= 50 m | <= 20 m |
| AD000013.jpg | 48.269366 | 37.372134 | <= 50 m | <= 20 m |
| AD000014.jpg | 48.268759 | 37.370940 | <= 50 m | <= 20 m |
| AD000015.jpg | 48.268291 | 37.369815 | <= 50 m | <= 20 m |
| AD000016.jpg | 48.267719 | 37.368469 | <= 50 m | <= 20 m |
| AD000017.jpg | 48.267461 | 37.367255 | <= 50 m | <= 20 m |
| AD000018.jpg | 48.266663 | 37.365888 | <= 50 m | <= 20 m |
| AD000019.jpg | 48.266135 | 37.365460 | <= 50 m | <= 20 m |
| AD000020.jpg | 48.265574 | 37.364211 | <= 50 m | <= 20 m |
| AD000021.jpg | 48.264892 | 37.362998 | <= 50 m | <= 20 m |
| AD000022.jpg | 48.264393 | 37.361086 | <= 50 m | <= 20 m |
| AD000023.jpg | 48.263803 | 37.361028 | <= 50 m | <= 20 m |
| AD000024.jpg | 48.263014 | 37.359878 | <= 50 m | <= 20 m |
| AD000025.jpg | 48.262635 | 37.358277 | <= 50 m | <= 20 m |
| AD000026.jpg | 48.261819 | 37.357116 | <= 50 m | <= 20 m |
| AD000027.jpg | 48.261182 | 37.355907 | <= 50 m | <= 20 m |
| AD000028.jpg | 48.260727 | 37.354723 | <= 50 m | <= 20 m |
| AD000029.jpg | 48.260117 | 37.353469 | <= 50 m | <= 20 m |
| AD000030.jpg | 48.259677 | 37.352165 | <= 50 m | <= 20 m |
| AD000031.jpg | 48.258881 | 37.351376 | <= 50 m | <= 20 m |
| AD000032.jpg | 48.258425 | 37.349964 | <= 50 m | <= 20 m |
| AD000033.jpg | 48.258653 | 37.347004 | <= 50 m | <= 20 m |
| AD000034.jpg | 48.257879 | 37.347711 | <= 50 m | <= 20 m |
| AD000035.jpg | 48.256777 | 37.348444 | <= 50 m | <= 20 m |
| AD000036.jpg | 48.255756 | 37.348098 | <= 50 m | <= 20 m |
| AD000037.jpg | 48.255375 | 37.346549 | <= 50 m | <= 20 m |
| AD000038.jpg | 48.254799 | 37.345603 | <= 50 m | <= 20 m |
| AD000039.jpg | 48.254557 | 37.344566 | <= 50 m | <= 20 m |
| AD000040.jpg | 48.254380 | 37.344375 | <= 50 m | <= 20 m |
| AD000041.jpg | 48.253722 | 37.343093 | <= 50 m | <= 20 m |
| AD000042.jpg | 48.254205 | 37.340532 | <= 50 m | <= 20 m |
| AD000043.jpg | 48.252380 | 37.342112 | <= 50 m | <= 20 m |
| AD000044.jpg | 48.251489 | 37.343079 | <= 50 m | <= 20 m |
| AD000045.jpg | 48.251085 | 37.346128 | <= 50 m | <= 20 m |
| AD000046.jpg | 48.250413 | 37.344034 | <= 50 m | <= 20 m |
| AD000047.jpg | 48.249414 | 37.343296 | <= 50 m | <= 20 m |
| AD000048.jpg | 48.249114 | 37.346895 | <= 50 m | <= 20 m |
| AD000049.jpg | 48.250241 | 37.347741 | <= 50 m | <= 20 m |
| AD000050.jpg | 48.250974 | 37.348379 | <= 50 m | <= 20 m |
| AD000051.jpg | 48.251528 | 37.349468 | <= 50 m | <= 20 m |
| AD000052.jpg | 48.251873 | 37.350485 | <= 50 m | <= 20 m |
| AD000053.jpg | 48.252161 | 37.351491 | <= 50 m | <= 20 m |
| AD000054.jpg | 48.252685 | 37.352343 | <= 50 m | <= 20 m |
| AD000055.jpg | 48.253268 | 37.353119 | <= 50 m | <= 20 m |
| AD000056.jpg | 48.253767 | 37.354246 | <= 50 m | <= 20 m |
| AD000057.jpg | 48.254329 | 37.354946 | <= 50 m | <= 20 m |
| AD000058.jpg | 48.254874 | 37.355765 | <= 50 m | <= 20 m |
| AD000059.jpg | 48.255481 | 37.356501 | <= 50 m | <= 20 m |
| AD000060.jpg | 48.256246 | 37.357485 | <= 50 m | <= 20 m |
Ground truth GPS coordinates for each frame are in `coordinates.csv`. The system processes these frames sequentially (simulating a real flight) with corresponding IMU data (200Hz, from SITL ArduPilot or synthetic generation from trajectory) and satellite tile matches. The system outputs estimated GPS coordinates per frame. Expected results compare estimated positions against ground truth.
### Representative Derkachi Video/IMU Fixture
| # | Input | Input Description | Expected Result | Comparison | Tolerance | Reference File |
|---|-------|-------------------|-----------------|------------|-----------|---------------|
| 1 | `coordinates.csv` (all 60 frames) | Sequential flight images with ground truth GPS | ≥ 80% of frames have position error < 50m from ground truth | percentage | ≥ 80% of frames within 50m | `expected_results/position_accuracy.csv` |
| 2 | `coordinates.csv` (all 60 frames) | Sequential flight images with ground truth GPS | ≥ 60% of frames have position error < 20m from ground truth | percentage | ≥ 60% of frames within 20m | `expected_results/position_accuracy.csv` |
| 3 | `coordinates.csv` (all 60 frames) | Sequential flight images with ground truth GPS | Per-frame position output in WGS84 (lat, lon) | numeric_tolerance | each frame ± 100m max (no single frame exceeds 100m error) | `expected_results/position_accuracy.csv` |
| 4 | `coordinates.csv` (all 60 frames) | Sequential flight images with ground truth GPS | Cumulative VO drift between satellite anchors < 100m | threshold_max | ≤ 100m drift between anchors | N/A |
| Input fixture | Expected validation result | Threshold |
|---------------|----------------------------|-----------|
| `flight_derkachi/data_imu.csv` | Telemetry CSV has required `timestamp(ms)`, `Time`, `SCALED_IMU2.*`, and `GLOBAL_POSITION_INT.*` columns; non-empty rows are monotonic from `Time=0.0` to `489.9` | 0 missing required columns; 0 decreasing timestamps; 4,900 nonblank rows |
| `flight_derkachi/flight_derkachi.mp4` | Video stream is readable as cropped nadir footage for replay | H.264, 880 x 720, 30 fps, approximately 490.07 s |
| Video/telemetry alignment | Video has 14,700 frames and telemetry has 4,900 rows | Exactly 3 video frames per telemetry row; duration delta <=250 ms |
| Derkachi trajectory comparison | Replay output can be compared to `GLOBAL_POSITION_INT.lat`, `GLOBAL_POSITION_INT.lon`, `GLOBAL_POSITION_INT.alt`, `GLOBAL_POSITION_INT.relative_alt`, velocity, and heading | Thresholds are calibration-gated; use for smoke/relative trajectory validation until intrinsics and camera-to-body calibration are pinned |
### GPS_INPUT Message Correctness
## Known Gaps
| # | Input | Input Description | Expected Result | Comparison | Tolerance | Reference File |
|---|-------|-------------------|-----------------|------------|-----------|---------------|
| 5 | Single frame + IMU data | Normal tracking frame with recent satellite match | `fix_type: 3`, `horiz_accuracy: 5-20m`, `satellites_visible: 10`, lat/lon populated | exact (fix_type, sat), numeric_tolerance (accuracy) | fix_type == 3, horiz_accuracy ∈ [1, 50] | N/A |
| 6 | Frame sequence, no satellite match for >30s | VO-only tracking, no recent satellite anchor | `fix_type: 3`, `horiz_accuracy: 20-50m` | exact (fix_type), range (accuracy) | fix_type == 3, horiz_accuracy ∈ [20, 100] | N/A |
| 7 | Frame sequence, VO lost + no satellite | IMU-only dead reckoning | `fix_type: 2`, `horiz_accuracy: 50-200m+` (growing over time) | exact (fix_type), threshold_min (accuracy) | fix_type == 2, horiz_accuracy ≥ 50 | N/A |
| 8 | VO lost + 3 consecutive satellite failures | Total position failure | `fix_type: 0`, `horiz_accuracy: 999.0` | exact | fix_type == 0, horiz_accuracy == 999.0 | N/A |
| 9 | Any valid frame | GPS_INPUT output rate | GPS_INPUT messages at 5-10Hz continuous | range | 5 ≤ rate_hz ≤ 10 | N/A |
- The still-image set has expected WGS84 centers but no synchronized IMU, attitude, airspeed, altitude, or timestamp stream.
- The Derkachi fixture has synchronized video, IMU, and GPS trajectory, but no raw camera calibration, lens distortion, exact camera-to-body transform, attitude, or airspeed columns.
- The still-image sample cadence is slower than the target 3 fps runtime profile; the Derkachi video is 30 fps and must be sampled to target replay cadence for runtime tests.
- Final production acceptance requires camera calibration and representative datasets with synchronized camera/IMU plus ground-truth trajectory.
### Confidence Tier Transitions
| # | Input | Input Description | Expected Result | Comparison | Tolerance | Reference File |
|---|-------|-------------------|-----------------|------------|-----------|---------------|
| 10 | Frame with satellite match <30s ago, covariance <400m² | HIGH confidence conditions | Confidence tier: HIGH, SSE confidence: "HIGH" | exact | N/A | N/A |
| 11 | Frame with cuVSLAM OK, no satellite match >30s | MEDIUM confidence conditions | Confidence tier: MEDIUM, SSE confidence: "MEDIUM" | exact | N/A | N/A |
| 12 | Frame with cuVSLAM lost, IMU-only | LOW confidence conditions | Confidence tier: LOW, SSE confidence: "LOW" | exact | N/A | N/A |
| 13 | 3+ consecutive total failures | FAILED conditions | Confidence tier: FAILED, SSE confidence: "FAILED", fix_type: 0 | exact | N/A | N/A |
### Image Registration & Visual Odometry
| # | Input | Input Description | Expected Result | Comparison | Tolerance | Reference File |
|---|-------|-------------------|-----------------|------------|-----------|---------------|
| 14 | 60 sequential flight images | Normal flight (no sharp turns) | Image registration rate ≥ 95% (≥ 57 of 60 registered) | percentage | ≥ 95% | N/A |
| 15 | 60 sequential flight images | Normal flight images | Mean reprojection error < 1.0 pixels | threshold_max | MRE < 1.0 px | N/A |
### Disconnected Route Segments & Sharp Turns
| # | Input | Input Description | Expected Result | Comparison | Tolerance | Reference File |
|---|-------|-------------------|-----------------|------------|-----------|---------------|
| 16 | Frames 32-43 from coordinates.csv | Trajectory with direction change (turn area) | System continues producing position estimates through the turn | threshold_min | ≥ 1 position output per frame | N/A |
| 17 | Simulated consecutive frames with 350m gap | Outlier between 2 consecutive photos due to tilt | System handles outlier, position estimate not corrupted (error < 100m for next valid frame) | threshold_max | ≤ 100m error after recovery | N/A |
| 18 | Simulated sharp turn (no overlap, <5% overlap, <70° angle, <200m drift) | Sharp turn where VO fails | Satellite re-localization triggers, position recovered within 3 frames after turn | threshold_max | position error ≤ 50m after re-localization | N/A |
| 19 | Simulated VO loss + satellite match success | Tracking loss → re-localization | cuVSLAM restarts, ESKF position corrected, tracking_state returns to NORMAL | exact | tracking_state == NORMAL after recovery | N/A |
### 3-Consecutive-Failure Re-Localization
| # | Input | Input Description | Expected Result | Comparison | Tolerance | Reference File |
|---|-------|-------------------|-----------------|------------|-----------|---------------|
| 20 | Simulated VO loss + 3 satellite match failures | Cannot determine position by any means | Re-localization request sent: `RELOC_REQ: last_lat=.* last_lon=.* uncertainty=.*m` | regex | message matches pattern | N/A |
| 21 | Re-localization request active | System waiting for operator | GPS_INPUT fix_type=0, system continues IMU prediction, continues satellite matching attempts | exact (fix_type) | fix_type == 0 | N/A |
| 22 | Operator sends approximate coordinates (lat, lon) | Operator re-localization hint | System uses hint as ESKF measurement (high covariance ~500m), attempts satellite match in new area | threshold_max | position error ≤ 500m initially, ≤ 50m after satellite match | N/A |
### Startup & Handoff
| # | Input | Input Description | Expected Result | Comparison | Tolerance | Reference File |
|---|-------|-------------------|-----------------|------------|-----------|---------------|
| 23 | System boot with GLOBAL_POSITION_INT available | Normal startup | System reads initial position, initializes ESKF, starts GPS_INPUT output | exact | GPS_INPUT output begins within 60s of boot | N/A |
| 24 | System boot + first satellite match | Startup validation | First satellite match validates initial position, position error drops | threshold_max | position error ≤ 50m after first satellite match | N/A |
### Mid-Flight Reboot Recovery
| # | Input | Input Description | Expected Result | Comparison | Tolerance | Reference File |
|---|-------|-------------------|-----------------|------------|-----------|---------------|
| 25 | System process killed mid-flight | Companion computer reboot | System recovers: reads FC position, inits ESKF with high uncertainty, loads TRT engines, starts cuVSLAM, performs satellite match | threshold_max | total recovery time ≤ 70s | N/A |
| 26 | Post-reboot first satellite match | Recovery validation | Position accuracy restored after first satellite match | threshold_max | position error ≤ 50m after first satellite match | N/A |
### Object Localization
| # | Input | Input Description | Expected Result | Comparison | Tolerance | Reference File |
|---|-------|-------------------|-----------------|------------|-----------|---------------|
| 27 | POST /objects/locate with pixel_x, pixel_y, gimbal angles, zoom, known UAV position | Object at known ground GPS | Response: `{ lat, lon, alt, accuracy_m, confidence }` with lat/lon matching ground truth | numeric_tolerance | lat/lon within accuracy_m of ground truth (consistent with frame-center accuracy) | N/A |
| 28 | POST /objects/locate with invalid pixel coordinates | Out-of-frame pixel | HTTP 422 or error response indicating invalid input | exact | HTTP status 422 | N/A |
### Coordinate Transform Chain
| # | Input | Input Description | Expected Result | Comparison | Tolerance | Reference File |
|---|-------|-------------------|-----------------|------------|-----------|---------------|
| 29 | Known GPS → NED → pixel → GPS round-trip | Coordinate transform validation | Round-trip error < 0.1m | threshold_max | ≤ 0.1m | N/A |
### API & Communication
| # | Input | Input Description | Expected Result | Comparison | Tolerance | Reference File |
|---|-------|-------------------|-----------------|------------|-----------|---------------|
| 30 | GET /health | Health check endpoint | HTTP 200, JSON with memory_mb, gpu_temp_c, status fields | exact (status code), regex (body) | status == 200, body contains `"status"` | N/A |
| 31 | POST /sessions | Start session | HTTP 200/201 with session ID | exact | status ∈ {200, 201} | N/A |
| 32 | GET /sessions/{id}/stream | SSE position stream | SSE events at ~1Hz with fields: type, timestamp, lat, lon, alt, accuracy_h, confidence, vo_status | regex | each event matches SSE schema | N/A |
| 33 | Unauthenticated request to /sessions | No JWT token | HTTP 401 Unauthorized | exact | status == 401 | N/A |
### Performance Thresholds
| # | Input | Input Description | Expected Result | Comparison | Tolerance | Reference File |
|---|-------|-------------------|-----------------|------------|-----------|---------------|
| 34 | Single camera frame (6252x4168) | End-to-end processing time | Total pipeline latency < 400ms (capture → GPS coordinate output) | threshold_max | ≤ 400ms | N/A |
| 35 | 30-minute sustained operation | Memory usage over time | Peak memory < 8GB, no memory leaks (growth < 50MB over 30min) | threshold_max | peak < 8192MB, growth ≤ 50MB | N/A |
| 36 | 30-minute sustained operation | GPU thermal | SoC junction temperature stays below 80°C (no throttling) | threshold_max | ≤ 80°C | N/A |
| 37 | cuVSLAM single frame | VO processing time | cuVSLAM inference ≤ 20ms per frame | threshold_max | ≤ 20ms | N/A |
| 38 | Satellite matching single frame | Satellite matching time (async) | LiteSAM/XFeat inference ≤ 330ms | threshold_max | ≤ 330ms | N/A |
| 39 | TRT engine load | Engine initialization time | All TRT engines loaded within 10s total | threshold_max | ≤ 10s | N/A |
### Satellite Tile Management
| # | Input | Input Description | Expected Result | Comparison | Tolerance | Reference File |
|---|-------|-------------------|-----------------|------------|-----------|---------------|
| 40 | Mission area definition (200km path, ±2km buffer, zoom 18) | Tile storage calculation | Total storage 500-800MB for zoom 18 + zoom 19 flight path | range | [300MB, 1000MB] | N/A |
| 41 | ESKF position ± 3σ search radius | Tile selection | Tiles covering search area loaded, mosaic assembled, covers at least 500m radius | threshold_min | coverage radius ≥ 500m | N/A |
### TRT Engine Validation
| # | Input | Input Description | Expected Result | Comparison | Tolerance | Reference File |
|---|-------|-------------------|-----------------|------------|-----------|---------------|
| 42 | LiteSAM PyTorch model → ONNX → TRT FP16 | TRT engine conversion | Engine builds successfully on Jetson Orin Nano Super | exact | exit_code == 0 | N/A |
| 43 | TRT engine output vs PyTorch reference (same input) | Inference correctness | Max L1 error between TRT and PyTorch output < 0.01 | threshold_max | L1_max < 0.01 | N/A |
| 44 | LiteSAM MinGRU operations | TRT compatibility check | All MinGRU ops supported in TRT 10.3 (polygraphy inspect) | exact | unsupported_ops == 0 | N/A |
### Telemetry
| # | Input | Input Description | Expected Result | Comparison | Tolerance | Reference File |
|---|-------|-------------------|-----------------|------------|-----------|---------------|
| 45 | Normal operation | Telemetry output rate | NAMED_VALUE_FLOAT messages at 1Hz (gps_conf, gps_drift, gps_hacc) | numeric_tolerance | rate: 1Hz ± 0.2Hz | N/A |
| 46 | VO tracking lost + 3 satellite failures | Re-localization telemetry | STATUSTEXT with RELOC_REQ sent to ground station | regex | message matches `RELOC_REQ:.*` | N/A |
## Expected Result Reference Files
### position_accuracy.csv
Reference file: `expected_results/position_accuracy.csv`
Contains the ground truth GPS coordinate for each frame in the 60-image test sequence (copied from `coordinates.csv`) plus the acceptance thresholds. Test harness computes haversine distance between estimated and ground truth positions, then applies aggregate criteria.
Thresholds applied to the full 60-frame sequence:
- ≥ 80% of frames: error < 50m
- ≥ 60% of frames: error < 20m
- 0% of frames: error > 100m (no single frame exceeds 100m)
- Cumulative VO drift between satellite anchors: < 100m
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# Derkachi Representative Flight Fixture
## Files
| File | Description | Observed Metadata |
|------|-------------|-------------------|
| `flight_derkachi.mp4` | Cropped nadir flight footage for replay | H.264, 880 x 720, 30 fps, about 490.07 s |
| `data_imu.csv` | Flight-controller telemetry trace exported from the tlog | 4,900 rows at 10 Hz from `Time=0.0` to `489.9`; includes `SCALED_IMU2` and `GLOBAL_POSITION_INT` trajectory fields |
## Test Use
Use this fixture for video/telemetry synchronization checks, representative replay smoke tests, VIO hot-path latency, frame-drop accounting, and trajectory comparison against `GLOBAL_POSITION_INT`. The video and telemetry align at exactly three video frames per telemetry row. Camera intrinsics, lens distortion, raw camera resolution, and exact camera-to-body calibration are still unknown, so this fixture is not sufficient by itself for final production camera calibration or satellite-anchor accuracy claims.
For the test recording, the rotating camera was mechanically fixed in a downward/nadir orientation. Treat the MP4 as a cleaned/cropped replay fixture rather than the raw camera feed.
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Camera model: Topotek KHP20S30
Daylight Sensor: 1/2.8" CMOS (2.13 Мп).
Full HD (1920x1080), 30/60 fps
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version https://git-lfs.github.com/spec/v1
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We have a wing-type UAV with a fixed downward navigation camera that can take photos 3 times per second. The authoritative navigation-camera spec is defined in `restrictions.md` as the ADTi 20MP 20L V1, APS-C sensor, about 5472 x 3648 px; older higher-resolution references are superseded. Also plane has flight controller with IMU. During the plane flight, we know GPS coordinates initially. During the flight, GPS could be disabled or spoofed. We need to determine the GPS of the centers of the next frame from the camera. And also the coordinates of the center of any object in these photos. We can use an external satellite provider for ground checks on the existing photos. So, before the flight, UAV's operator should upload the satellite photos to the plane's companion PC.
The real world examples are in input_data folder, but the original still-image set has a much larger distance between photos than the target aircraft will have. On that particular example photos were taken 1 photo per 2-3 seconds. But in real-world scenario frames would appear within the interval no more than 500ms. Additional representative data is available in `input_data/flight_derkachi/`: cropped nadir flight footage plus synchronized `SCALED_IMU2` and `GLOBAL_POSITION_INT` telemetry. This supports video/telemetry synchronization, replay, latency, VIO smoke tests, and trajectory comparison against the tlog GPS path. Camera intrinsics, lens distortion, raw camera feed parameters, and exact camera-to-body calibration are still pending, so final production accuracy claims remain gated on calibration data or a separately surveyed representative dataset.
We have a wing-type UAV with a camera pointing downwards that can take photos 3 times per second with a resolution 6200*4100. Also plane has flight controller with IMU. During the plane flight, we know GPS coordinates initially. During the flight, GPS could be disabled or spoofed. We need to determine the GPS of the centers of the next frame from the camera. And also the coordinates of the center of any object in these photos. We can use an external satellite provider for ground checks on the existing photos. So, before the flight, UAV's operator should upload the satellite photos to the plane's companion PC.
The real world examples are in input_data folder, but the distance between each photo is way bigger than it will be from a real plane. On that particular example photos were taken 1 photo per 2-3 seconds. But in real-world scenario frames would appear within the interval no more than 500ms or even 400 ms.
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# Restrictions
# UAV & Flight
> Last revised 2026-05-07 (cleanup pass — design-independent, IEEE-830 style; only external dependencies, environmental constraints, integration boundaries).
> Subsequent revision 2026-05-07 (post-SQ6 research): the FC-facing communication protocol entries below were corrected — iNav firmware (master, post-9.0) has no inbound MAVLink external-positioning handler; the project must use a per-FC adapter (MAVLink `GPS_INPUT` for ArduPilot Plane; MSP2 `MSP2_SENSOR_GPS` for iNav). Rationale and L1 sources in `_docs/00_research/02_fact_cards/SQ6_fc_external_positioning.md` / `_docs/00_research/01_source_registry/SQ6_external_positioning.md` Sources #4, #9, #10, #12, #13.
- Photos are taken by only airplane (fixed-wing) type UAVs
- Photos are taken by the camera pointing downwards and fixed, but it is not autostabilized
- The flying range is restricted by the eastern and southern parts of Ukraine (to the left of the Dnipro River)
- Altitude is predefined and no more than 1km. The height of the terrain can be neglected
- Flights are done mostly in sunny weather
- During the flight, UAVs can make sharp turns, so that the next photo may be absolutely different from the previous one (no same objects), but it is rather an exception than the rule
- Number of photos per flight could be up to 3000, usually in the 500-1500 range
## UAV & Flight
- Fixed-wing UAVs only; navigation camera fixed downward (no gimbal).
- Operational area: eastern/southern Ukraine (east of Dnipro).
- Mission profile: 8-hour flights, ~60 km/h cruise. Sector ≤150 km² + transit corridor ~50 km². Total cached area ≤~400 km², persistent across flights.
- Altitude ≤1 km AGL; terrain assumed flat (rolling steppe / agricultural).
- Weather: predominantly sunny daytime; validation must cover seasonal/visibility classes (summer crops, autumn/winter bare fields, cloud/haze, snow if winter, low-texture repetition).
- Sharp turns are exceptions; consecutive photos may share <5% overlap (AC-3.2).
- No raw-photo storage (AC-8.5); storage bounded by tile cache + per-flight FDR (AC-NEW-3).
# Cameras
## Cameras
- **Navigation camera (pinned)**: ADTi 20MP 20L V1, APS-C ~23.6 × 15.7 mm, ~5472 × 3648 px (≈20 MP). Lens chosen so GSD lands in 10–20 cm/px @ 1 km AGL (frame footprint ~470×314 m to ~980×655 m). Intrinsics + camera-to-body calibration must be obtained pre-flight (e.g., checkerboard).
- **AI camera**: operator-controlled gimbal angle + zoom (consumed by AI detection systems). The GPS-Denied system supports object localization (AC-7.x) using gimbal angle + zoom only — UAV bank/pitch is not published to that path; AI-camera object localization is therefore scoped to level flight (AC-7.1).
- Camera-to-companion interface: USB / MIPI-CSI / GigE (lens-module dependent).
- UAV has two cameras:
1. **Navigation camera** — fixed, pointing downwards, not autostabilized. Used by GPS-Denied system for position estimation
2. **AI camera** — main camera with configurable angle and zoom, used by onboard AI detection systems
- Navigation camera resolution: FullHD to 6252*4168. Camera parameters are known: focal length, sensor width, resolution, etc.
- Cameras are connected to the companion computer (interface TBD: USB, CSI, or GigE)
- Terrain is assumed flat (eastern/southern Ukraine operational area); height differences are negligible
## Satellite Imagery
- **Source**: Azaion Suite Satellite Service (separate Suite component). Onboard system is a consumer; upstream sourcing is the Service's concern.
- **Onboard interface is offline-only**: companion holds a local cache populated pre-flight from the Service for the operational area (AC-8.3). No in-flight Service calls.
- **Mid-flight tile generation (AC-8.4)**: companion orthorectifies nav-camera frames into basemap-projected tiles, deduplicates, stores locally; uploads on landing.
- **Storage policy**: tile is the unit of persistence; no raw frames retained (AC-8.5).
- **Resolution at cache interface**: ≥0.5 m/px, ideally 0.3 m/px (AC-8.1).
- **Tile manifest schema**: CRS, tile matrix, dimension, lat-adjusted m/px, capture date, source, compression. Slippy/XYZ zoom (if used) is a provider convention, not a resolution proof.
- **Cache budget**: 10 GB persistent across the ~400 km² area, including manifests, overviews, and any precomputed indices unless the solution carves out a separate descriptor budget.
- **Freshness**: enforced per AC-8.2 / AC-NEW-6 (6-month active-conflict / 12-month rear). Mid-flight tiles timestamped current and treated as fresh.
- **Sentinel-2 / free public imagery**: not on runtime path; cache rejects below the 0.5 m/px floor.
# Satellite Imagery
## Onboard Hardware
- **Companion computer (pinned)**: Jetson Orin Nano Super — 67 TOPS sparse INT8, 8 GB shared LPDDR5, 25 W TDP. JetPack (Ubuntu) with CUDA / TensorRT.
- Cooling sized for 25 W continuous over 8 h at the upper environmental temp (AC-NEW-5).
- Storage budget ≥ tile cache (~10 GB) + per-flight FDR (64 GB, AC-NEW-3).
- We can use satellite providers, but we're limited right now to Google Maps, which could be outdated for some regions
- Satellite imagery for the operational area must be pre-loaded onto the companion computer before flight
## Sensors & Integration
- **High-rate IMU** available from FC via MAVLink (both ArduPilot Plane and iNav expose IMU telemetry over MAVLink outbound).
- **Communication protocol (pinned)**: MAVLink for the GCS link (QGroundControl). Companion ↔ FC interface is per-FC: MAVLink for ArduPilot Plane (inbound external positioning + outbound telemetry); MSP2 for iNav (inbound external positioning via `MSP2_SENSOR_GPS`); MAVLink outbound from iNav for telemetry to the GCS is preserved.
- **Supported flight controllers**: ArduPilot Plane, iNav. PX4 out of scope.
- **Output to FC**: WGS84 GPS coordinates as a real-GPS replacement, via each supported FC's documented external-positioning interface — MAVLink `GPS_INPUT` for ArduPilot Plane, MSP2 `MSP2_SENSOR_GPS` for iNav (companion is the sole GPS source on iNav; iNav has no dual-GPS arbitration). Per-FC parameter wiring (EKF source-set on ArduPilot; GPS provider/UART selection on iNav) and source-label out-of-band channel are design choices; outcome contract is AC-4.3.
- **Ground station**: QGroundControl (Mission Planner out of scope). Telemetry link bandwidth-limited; per-frame data stays on local FDR (AC-NEW-3); GCS sees 1–2 Hz downsampled summary (AC-6.1).
- **Representative data**: see `input_data/` (still images), `input_data/flight_derkachi/` (cropped nadir video + synchronized `SCALED_IMU2` + `GLOBAL_POSITION_INT`). Production acceptance still requires camera intrinsics, distortion, camera-to-body calibration, and synchronized representative flight data (frames + FC IMU/attitude/airspeed/altitude + emitted MAVLink + ground-truth trajectory).
# Onboard Hardware
## Failsafe & Safety
- If no estimate produced for >3 s → autopilot falls back to IMU-only dead reckoning (AC-5.2). 3 s rides through one sharp turn at cruise.
- False-position safety budget: AC-NEW-4 (P(>500 m) <0.1 %, P(>1 km) <0.01 % per flight).
- Cold-start TTFF <30 s (AC-NEW-1); spoofing-promotion latency <3 s (AC-NEW-2).
- Processing is done on a Jetson Orin Nano Super (67 TOPS, 8GB shared LPDDR5, 25W TDP)
- The companion computer runs JetPack (Ubuntu-based) with CUDA/TensorRT available
- Onboard storage for satellite imagery is limited (exact capacity TBD, but must be accounted for in tile preparation)
- Sustained GPU load may cause thermal throttling; the processing pipeline must stay within thermal envelope
# Sensors & Integration
- There is a lot of data from IMU (via the flight controller)
- The system communicates with the flight controller via MAVLink protocol using MAVSDK library
- The system must output GPS coordinates to the flight controller as a replacement for the real GPS module (MAVLink GPS_INPUT message)
- Ground station telemetry link is available but bandwidth-limited; it is not the primary output channel
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# Question Decomposition
> Mode A Phase 2 (Initial Research — Problem & Solution Draft).
> Phase 1 (AC & Restrictions Assessment) was skipped per user decision after a cleanup pass that stripped implementation details from `acceptance_criteria.md` and `restrictions.md` (commit `12cc5a4`); AC/restrictions are treated as fixed inputs.
## Original Question
Design the GPS-denied onboard navigation system for a fixed-wing UAV operating in eastern/southern Ukraine, satisfying every AC in `_docs/00_problem/acceptance_criteria.md` under the constraints in `_docs/00_problem/restrictions.md`. Recommend a concrete component-by-component architecture and tech stack.
## Research Output Class
**Technical-component selection.** All technical-component gates apply (per-mode API capability verification, Component Applicability Gate, Restrictions × Candidate-Mode sub-matrix, MVE evidence, mandatory `context7` lookups for every lead library/SDK candidate).
## Question Type
**Decision Support** (per Mode A Phase 2 default). Sub-flavour: multi-component decision support — weighing trade-offs across ~10 interlocking component areas under hard real-time + memory + safety budgets.
## Project Context Summary (from `_docs/00_problem/`)
- **What is being built**: an onboard companion-PC system that replaces real GPS for a fixed-wing UAV when GPS is denied/spoofed, by combining nav-camera frames + FC IMU + a pre-cached satellite tile basemap, and emits standard MAVLink external-positioning messages to ArduPilot or iNav at frame rate.
- **Operating area**: eastern/southern Ukraine, active-conflict zones (war-zone scene change is a first-class concern, not an edge case).
- **Mission profile**: 8-hour fixed-wing flights, ~60 km/h cruise, ≤1 km AGL, ~400 km² operational area.
- **Pinned external deps**: ADTi 20MP 20L V1 nav camera (APS-C); Jetson Orin Nano Super 8 GB / 25 W; MAVLink protocol; ArduPilot + iNav as supported FCs; QGroundControl as GCS; Azaion Suite Satellite Service (offline cache interface ≥0.5 m/px).
- **Hard runtime envelope**: <400 ms p95 end-to-end latency (camera → MAVLink), <8 GB shared CPU+GPU RAM, 25 W TDP at +50 °C ambient for 8 h continuous, no in-flight network, 10 GB persistent tile cache + 64 GB per-flight FDR.
- **Hard safety envelope**: P(error >500 m) <0.1 % per flight, P(error >1 km) <0.01 % per flight; honest covariance reporting; explicit `dead_reckoned` failsafe under simultaneous GPS spoof + visual blackout; cache-poisoning probability bounds for tiles written back to the Service.
## Project Constraint Matrix
| Dimension | Binding constraint |
|---|---|
| **Inputs available** | Nav camera frames @ 3 fps (5472×3648, ~12 cm/px GSD @ 1 km AGL); FC IMU (high rate via MAVLink); FC attitude/airspeed/altitude; pre-cached satellite tiles ≥0.5 m/px (offline); operator re-loc hint via GCS (rare). |
| **Outputs required** | WGS84 position to FC via MAVLink external-positioning message(s) accepted by ArduPilot AND iNav; per-frame estimate carrying honest 95 % covariance, source label `{satellite_anchored, visual_propagated, dead_reckoned}`, and `last_satellite_anchor_age_ms`; mid-flight ortho-tiles written to local cache with quality metadata; 1–2 Hz GCS summary; FDR records per AC-NEW-3. |
| **Hardware fixed** | Jetson Orin Nano Super (67 TOPS sparse INT8, 8 GB shared LPDDR5, 25 W TDP, JetPack/CUDA/TensorRT). |
| **Lifecycle** | Real-time embedded; offline (no in-flight network); 8 h continuous; persistent tile cache across flights; FDR rollover. |
| **Non-functional** | <400 ms p95 latency; <8 GB shared RAM; ≤25 W power at +50 °C ambient; AC-1.1/1.2 accuracy; AC-2.1/2.2 registration & MRE; AC-3.x resilience; AC-NEW-1 cold-start <30 s; AC-NEW-2 spoof promotion <3 s; AC-NEW-4 false-position safety; AC-NEW-7 cache-poisoning safety; AC-NEW-8 blackout failsafe. |
| **Hard disqualifiers** | Anything requiring >8 GB RAM peak (CPU+GPU shared); anything not runnable under JetPack on Orin Nano Super; anything requiring in-flight cloud calls; anything that cannot honestly report covariance; anything that does not have a runnable example for monocular nadir UAV input over season-matched satellite tiles; anything whose license blocks military / dual-use deployment. |
## Research Subject Boundary
| Dimension | Boundary |
|---|---|
| **Population** | Fixed-wing UAVs, downward-fixed monocular nav camera, Jetson-class edge HW, ArduPilot or iNav autopilot. Excludes: multirotors, gimbal-stabilised nav cams, server/cloud GPS-denied stacks, PX4 (out of scope), commercial sat-imagery direct integration (Service handles upstream). |
| **Geography** | Eastern/southern Ukraine — agricultural steppe, active-conflict scene change. Validation must include this geography or representative analogues (low-texture cropland, snow, war-zone destruction). |
| **Timeframe** | Production deployment 2026; tools / libraries / models considered must be currently maintained (commits/releases in last 18 months OR explicit long-term-stable status). Critical-novelty domain — see Step 0.5 timeliness assessment. |
| **Operating context** | Real-time embedded; offline in-flight; 8 h continuous duty; 25 W power envelope; 8 GB shared CPU+GPU memory; thermal envelope to +50 °C ambient. |
| **Required interfaces** | Inputs: ADTi 20MP nav cam, FC IMU (MAVLink), satellite tile cache. Outputs: MAVLink external-positioning to ArduPilot AND iNav; QGroundControl summary; FDR; tile write-back to Suite Service on landing. |
| **Non-functional envelope** | Per AC-1 to AC-8 plus AC-NEW-1 to AC-NEW-8. Hardest binding constraints: 400 ms p95 end-to-end; 8 GB shared RAM; AC-NEW-4 false-position probability bounds; AC-NEW-7 cache-poisoning probability bounds. |
## Sub-Questions
| ID | Sub-question |
|---|---|
| SQ1 | What existing/competitor GPS-denied UAV navigation systems exist (academic + open-source + commercial + military), and which of them have been validated on fixed-wing UAVs in active-conflict environments? What works, what fails? |
| SQ2 | What is the canonical decomposition of "monocular nadir UAV ↔ pre-cached satellite basemap localization" into pipeline components? Is the decomposition below complete, or are there industry-standard components missing? |
| SQ3 | For each component (VO/VIO, VPR, cross-domain registration, single-frame orthorectification, sensor-fusion estimator, tile cache + spatial index, on-Jetson inference runtime, MAVLink FC adapter, dataset/SITL validation infrastructure): what option families exist (simple baseline / production / open-source / commercial / SOTA / adjacent-domain / no-build), and what are the leading candidates as of 2026? |
| SQ4 | For each lead candidate per component: what are the documented runtime/memory/latency/license/maintenance constraints, and how do they bind against the Project Constraint Matrix? Per-mode API capability verification with `context7` for every library/SDK lead. |
| SQ5 | What are the documented failure modes and real-world deployment lessons for each component family? In particular: VPR collapse under cropland repetition, DINOv2/foundation-model cost on Jetson at int8, RANSAC degeneracy at sharp turns / low texture, EKF over-confidence on cross-domain matches, ortho geometric error from unknown bank/pitch. |
| SQ6 | How do **ArduPilot Plane** (current stable) and **iNav** (current stable) each accept external positioning input via MAVLink? What message types does each support? Where do their interfaces diverge, and what is the documented status of each interface (stable / experimental / known bugs)? |
| SQ7 | What public datasets, benchmarks, and SITL/replay environments exist for cross-validating monocular nadir UAV navigation against satellite basemaps in season-matched + change-affected conditions? AerialVL, AerialExtreMatch, others? |
| SQ8 | What are the security and safety considerations specific to the AC-NEW-4 (false-position) and AC-NEW-7 (cache-poisoning) safety budgets, including spoofing-detection signals from FC, ortho-tile geo-alignment quality estimation, and write-back cache-poisoning controls? |
| SQ9 | What does the system look like end-to-end — wiring, scheduling, threading model, inference scheduling on shared CPU+GPU memory, cold-start sequencing, FDR rotation, and pre-flight cache provisioning workflow? (synthesis question, answered in Step 8) |
## Component Areas (search plan)
For each component below, the search plan covers all option families per `Component Option Search Plan` rules (`research/steps/03_engine-investigation.md` → "Component Option Breadth").
| # | Component area | Required outputs | Key option families to enumerate |
|---|----------------|------------------|----------------------------------|
| C1 | **Visual / Visual-Inertial Odometry** (frame-to-frame motion when satellite anchor is unavailable) | Relative 6-DoF pose between consecutive frames or short windows; output frequency ≥3 Hz; metric scale (with IMU) | Classical (VINS-Mono / VINS-Fusion / OpenVINS), Kimera, ORB-SLAM3, OKVIS2, MSCKF-class, learning-based (DROID-SLAM, DPVO), pure VO baseline (KLT + RANSAC homography), no-build (skip and rely on pure satellite re-anchor every frame) |
| C2 | **Visual Place Recognition (VPR)** — UAV nadir frame → top-K satellite chunks | Compact global descriptor per UAV frame and per cache chunk; cosine-rank top-K candidates | NetVLAD class, MixVPR, EigenPlaces, BoQ, AnyLoc (DINOv2 + VLAD), CricaVPR, foundation-model direct retrieval (DINOv2/DINOv3/SAM 2 / SuperGlobal) |
| C3 | **Cross-domain registration** (UAV nadir ↔ ortho satellite tile, after VPR top-K) | Sub-pixel alignment + 6-DoF camera pose w.r.t. tile, with inlier ratio + covariance | Local-feature matching (SuperPoint+SuperGlue / LightGlue / DISK+LightGlue / ALIKED+LightGlue / XFeat), dense matchers (LoFTR / RoMa / DKM / MASt3R), classical (SIFT+RANSAC), specialized cross-domain (CMRNet+, CroCoMatch class), templating (mutual-information / ECC), no-build (skip cross-domain; rely on direct frame-to-tile homography from VPR retrieval) |
| C4 | **Single-frame orthorectification** (nav frame → basemap-aligned tile, given current pose) | Ortho-rectified tile chunk with geo metadata + quality score | Single-frame perspective warp with flat-earth assumption; OpenCV homography; bundled-DEM-aware (rare for flat steppe — likely overkill); GDAL warp utilities; custom GPU shader on Jetson |
| C5 | **State estimator / sensor fusion** (VO + IMU + sat anchors → fused estimate with covariance) | WGS84 position + 3D velocity + attitude + 6×6 covariance, frame-rate output, honest covariance, source label | EKF (manual), ESKF (manual or via library), MSCKF, factor-graph (GTSAM, iSAM2), particle filter, learned (out-of-scope for safety budget). Supporting: Mahalanobis outlier gates |
| C6 | **Tile cache + spatial index** (storage + retrieval of basemap tiles + descriptors, with manifests, freshness, dedup, and write-back) | mmap-friendly storage; ANN over global descriptors; spatial query for geographic prior; manifest schema per AC | Storage: GeoTIFF + COG, MBTiles, custom flat layout. ANN: FAISS (IVF/PQ/HNSW), hnswlib, ScaNN, brute-force (small index). Spatial: R-tree / KD-tree / GeoPandas / SQLite+SpatiaLite. Manifest: SQLite, JSON-per-tile, Parquet sidecar |
| C7 | **On-Jetson inference runtime** | INT8/FP16 inference of the chosen VPR + matcher models within latency + memory budget | TensorRT (native), Torch-TensorRT, ONNX Runtime + TRT EP, NVIDIA Triton (probably overkill), pure PyTorch fp16, NVIDIA DeepStream (for video), CUDA-Python custom kernels |
| C8 | **MAVLink FC adapter** (per-FC external-positioning emission + spoofing-signal subscription, for ArduPilot AND iNav) | MAVLink frames consumed by ArduPilot Plane and iNav as external position; spoofing signals consumed from each FC | Libraries: `pymavlink` (per-message), MAVSDK (high-level), ArduPilot/iNav SITL for verification. Per-FC choice of message: `GPS_INPUT` vs `ODOMETRY` vs `VISION_POSITION_ESTIMATE` vs `GLOBAL_POSITION_INT` (documented capability per FC must be verified, not assumed) |
| ~~C9~~ | ~~**Datasets + SITL / replay**~~ — **DROPPED from research scope per 2026-05-08 restructure (user choice A)**; deferred to **Test Spec (greenfield Step 5)**. See "C9 / SQ7 Restructure" section below. | — | — |
| C10 | **Pre-flight cache provisioning + sector classification + freshness pipeline** (RESEARCH SCOPE NARROWED 2026-05-08 to cross-coupling minimal — see "C10 Scope Restructure" section below) | (in research scope) confirmed orchestration mechanism for descriptor-cache rebuild (D-C6-3) + TensorRT engine build (D-C7-7) at pre-flight; on-disk artifact format(s); time/memory budget; failure-mode + retry behavior. (deferred to Plan-phase) operator CLI/desktop tool design, sector classification heuristics, freshness pipeline workflow. | (in research scope) FAISS Python API for write_index/read_index orchestration; TensorRT build orchestration `trtexec` CLI vs Python `IBuilderConfig` vs Polygraphy. (deferred) custom CLI/desktop, QGC plan files, MAVProxy, Mission Planner integration patterns. |
## Perspectives Chosen (≥3 mandatory)
1. **Implementer / Engineer** — Will the chosen stack actually compile, link, and run on the pinned Jetson within the latency + memory budget? Pitfalls of MAVLink GPS injection on each FC. Sub-pixel registration on UAV-nadir × ortho satellite. Inference-scheduler contention on shared CPU+GPU memory.
2. **Practitioner / Field** — What do UAV teams actually report from GPS-denied missions in real war-zone deployments? (Ukraine context if findable; otherwise analogous high-stakes deployments.) Real-world VPR collapse on agricultural cropland / snow / season change. Real-world FDR usefulness for post-mission forensics.
3. **Domain expert / Academic** — Recent (2024–2026) VPR + cross-domain matching benchmarks and their relative ranks under cross-season / cross-domain / cross-altitude conditions. Foundation-model-based VPR (AnyLoc, BoQ, MASt3R) — academic claims vs reproducibility. Recent factor-graph vs ESKF comparisons.
4. **Contrarian / Devil's advocate** — Why might foundation-model VPR fail on the Jetson budget? Where does cross-domain matching degrade silently? When does ortho-tile write-back amplify bad poses? When does honest covariance turn into "system never trusts itself" (over-cautious failure)?
## Search Query Variants per Sub-Question
(Detailed query lists are appended below per sub-question; these will be executed in Step 2 and saved to the `01_source_registry/` folder, indexed by `01_source_registry/00_summary.md`. The shape is shown here so the search plan is auditable; the full execution log will populate downstream files.)
**SQ1** (existing systems / competitors): "GPS-denied UAV navigation 2025", "visual GPS denied fixed wing UAV", "satellite map matching UAV localization 2024 2025", "Ukraine UAV GPS spoofing countermeasures", "ARL ANT Project visual navigation", "vision-based GPS replacement UAV production", "UAV GPS spoofing real-world deployment 2025".
**SQ2** (canonical pipeline): "visual aerial localization pipeline survey", "UAV satellite map matching architecture", "monocular UAV global localization pipeline 2024 2025".
**SQ3 / SQ4** (per-component candidates + binding): per-component query templates (5+ variants each) — see Step 2 plan in `01_source_registry/00_summary.md` once initialised. Each lead library/SDK candidate triggers the mandatory `context7` per-mode capability verification per `research/steps/03_engine-investigation.md`.
**SQ5** (failure modes): "VPR cropland failure", "DINOv2 Jetson Orin Nano latency", "SuperGlue LightGlue Jetson Orin", "ESKF cross-domain over-confidence", "RANSAC homography low-texture failure UAV", "ortho photo geometric error airframe tilt".
**SQ6** (ArduPilot vs iNav external positioning): "ArduPilot Plane GPS_INPUT external", "ArduPilot ODOMETRY EKF3 source switching", "iNav external positioning MAVLink GPS_INPUT", "iNav MAVLink GPS substitute", "iNav GPS denied flight 2025", "ArduPilot vs iNav external nav comparison".
**SQ7** (datasets): "AerialVL dataset", "AerialExtreMatch", "VPR-Bench cross-season aerial", "Mid-Air UAV dataset", "Mavic Mavik UAV public flight dataset", "satellite-aerial cross-view localization benchmark".
**SQ8** (safety): "MAVLink GPS_RAW_INT spoofing detection", "EKF lane switch ArduPilot", "covariance under-reporting risk EKF", "geo-misalign detection ortho tile".
## Completeness Audit
Probes (per `references/comparison-frameworks.md` → Decomposition Completeness Probes — applied here without re-reading the full file; will reconcile during Step 2):
| Probe | Coverage |
|---|---|
| Functional decomposition complete? | C1–C10 cover all data flows from camera in to MAVLink out + back. ✓ |
| Non-functional dimensions covered? | Latency, memory, accuracy, safety, freshness, security all in Project Constraint Matrix. ✓ |
| Failure-mode dimension covered? | SQ5 explicitly. ✓ |
| Cost / TCO dimension? | Hardware is pinned (Jetson Orin Nano Super); Service-side cost is out of scope; SW cost = mostly open-source candidates. Will revisit during Phase 3 (tech stack consolidation) if commercial options emerge. ✓ |
| Maintenance / community-health dimension? | SQ4 binds it per candidate. ✓ |
| Adjacent-domain dimension? | Robot SLAM, AGV warehouse navigation, aerial photogrammetry will be searched as analogues. ✓ |
| Validation / dataset coverage? | **Deferred to Test Spec (greenfield Step 5) per 2026-05-08 C9 / SQ7 restructure** — fixture-class, not research-class. Dataset shortlist preserved for handoff. |
| Integration / boundary coverage? | SQ6 (FC adapters) + C8 + C10 (pre-flight provisioning). ✓ |
| Operational/human-factors? | Pre-flight cache provisioning (C10) and operator re-loc hint (AC-3.4) covered. Mission-planning UX is out of scope. ✓ |
| Security / threat model? | SQ8. Will deepen in Phase 4 (Security Deep Dive) if invoked. ✓ |
No major gap detected at decomposition time. If domain-discovery searches in Step 2 surface a missed dimension, a "gap-fill" entry will be appended here.
## Notes on Output-Class Mode-Verification
Because this is **Technical-component selection**, every lead library/SDK candidate triggers:
- Pinned mode/configuration sentence in `02_fact_cards/Cx_*.md` (per-component sub-files).
- `context7` lookup with the three mandatory queries (mode enumeration; project's exact mode runnable example; disqualifier probe).
- MVE block per candidate.
- Per-numbered-Restriction and per-numbered-AC binding (`Pass` / `Fail` / `Verify` / `N/A`).
- Two modes of one library = two distinct candidates.
## Step 0.5 — Novelty Sensitivity Assessment
**Classification: Critical sensitivity.**
Justification:
- Foundation-model VPR is moving fast: DINOv2 (Apr 2023), AnyLoc (Aug 2023), BoQ (CVPR 2024), MASt3R (May 2024), MASt3R-SfM / new VPR-leader candidates 2025; rankings on cross-season aerial benchmarks have shifted multiple times since 2023.
- ArduPilot Plane / iNav external-positioning interfaces have moved: ArduPilot EKF3 source-switching parameters and known double-fusion bugs between `GPS_INPUT` and `ODOMETRY` were a moving target through 2024–2025; iNav GPS-denied support has matured separately.
- TensorRT / JetPack stacks on Jetson Orin Nano Super have version-dependent INT8 quantisation behaviour and runtime tooling differences worth verifying against current releases.
- Public aerial-localization datasets (AerialVL, AerialExtreMatch, etc.) have had multiple revisions and added splits.
Source-time-window rules for this run:
- **Lead-candidate selection / SOTA claims**: prioritise sources from **last 6 months**; allow up to **18 months** if no newer source covers the same claim and the older source is the official authority.
- **Established baselines / classical algorithms** (KLT, RANSAC, EKF, ORB, SIFT, GTSAM): no time window — canonical references are fine.
- **Library/SDK API behaviour**: must be verified against the **currently shipped version** at the time of search (`context7` mandatory per lead candidate; release notes / changelog cross-checked).
- **Cross-validation**: every Critical-sensitivity claim that drives a candidate selection must have **≥2 independent sources** or one official + one runnable MVE; single-source SOTA claims must be downgraded to `Experimental only` at Step 7.5 unless cross-validated.
## SQ2 Closure — Pipeline-component coverage table (Mode A Phase 2, Step 3 result)
The C1–C10 decomposition was sanity-checked against five independent surveys/benchmarks (Skoltech aerial-VPR survey, U.Maine cross-view survey, OrthoLoC benchmark, AnyVisLoc benchmark, NUDT 2026 absolute-VL survey — all logged in `01_source_registry/SQ2_canonical_pipeline.md` as Sources #38–#42). The canonical hierarchical framework `retrieval → matching → pose-estimation` is unanimously confirmed; project's split is **canonical, not novel**. Two augmentations are required.
| Survey/benchmark canonical stage | Project component | Coverage status | Required action |
|---|---|---|---|
| Image retrieval (global VPR) | **C2 — VPR** | ✅ covered | None |
| Re-ranking (top-N inlier-based) | (implicit, inside C2/C3) | ⚠️ implicit | Promote to explicit sub-stage in `solution_draft01` |
| Local image matching (2D-2D, sparse or dense) | **C3 — Cross-domain registration** | ✅ covered | Add Top-N inlier re-rank requirement |
| AdHoP-style perspective preconditioning | (not represented) | ❌ missing | Add as optional sub-stage between C3 and C4, gated on Jetson latency budget |
| 2D-3D lift via DSM | (not represented; current cache is 2D ortho only) | ❌ architectural decision required | **Decision required from user** — see "Open architectural decisions" below |
| Pose estimation (PnP + RANSAC + LM) | **C4 — Pose estimation** | ✅ covered | None |
| State estimator / fusion | **C5 — Estimator / fusion** | ✅ covered | Augmented with covariance-honesty contract (already from AC-NEW-4) |
| IMU + VIO contract | **C1 (VIO)** + **C6 (Tile cache)** ⁂ | ✅ covered | Add yaw σ ≤ 5°, pitch σ ≤ 5° hard contract (Fact #24) |
| Tile cache + scheduler | **C6 (Tile cache + spatial index)** | ✅ covered | Add 20% covisibility runtime invariant (Fact #27) |
| On-Jetson runtime | **C7 — On-Jetson inference runtime** | ✅ covered | Pre-screen prunes non-viable candidates (Fact #26) |
| Anti-spoof / FC adapter | **C8 — MAVLink FC adapter** | ✅ covered | Already addressed by SQ6 |
| Datasets / SITL / replay | **Deferred to Test Spec (greenfield Step 5)** per 2026-05-08 C9 / SQ7 restructure | ⚠️ moved out of research scope | Test Spec owns dataset-corpus selection, SITL framework choice (ArduPilot Plane SITL + iNav SITL/HITL), and replay framework choice |
| Pre-flight cache provisioning | **C10 — Pre-flight cache + sector classification** | ✅ covered | None |
⁂ The "IMU integration" concern lives in C1 (VIO) and partially flows from FC IMU; there is no separately numbered IMU component in the original C1–C10 split. SQ2 confirms this was correct — IMU is best owned by C1 (VIO) which already produces the yaw/pitch attitude. The σ ≤ 5° contract belongs on C1's output interface.
### SQ2 — Architectural decisions (resolved by user, 2026-05-07)
| # | Decision | Choice | Implication for SQ3+SQ4 |
|---|---|---|---|
| 1 | DSM dependency on Suite Sat Service tile cache (Fact #23) | **(a) 3-DoF acceptance** — fix attitude from IMU/VIO, ignore DSM; current 2D-ortho cache contract preserved. | C6 (Tile cache) candidate matrix excludes DSM-dependent storage formats. C3 (matcher) candidates evaluated on 2D-2D output (homography) only. Yaw/pitch σ ≤ 5° (Fact #24) is **noted as an empirical requirement on C1's output but NOT bound as a hard interface contract** — emerges as an output of C1 candidate selection in SQ3+SQ4. AC-1.1.1 (≤80 m at 1 km AGL) likely satisfied per DSMAC-class lineage in Fact #17; if AC ever tightens, revisit option (b). |
| 2 | AdHoP refinement loop (Fact #22) | **(b) Conditional** — only invoked when initial reprojection error exceeds a threshold. | C3 (matcher) latency budget = base (single-pass) + AdHoP-conditional overhead (worst-case 2× when triggered). Per-frame Jetson MVE must measure both modes. The reprojection-error threshold becomes a SQ3+SQ4 hyperparameter. |
| 3 | Top-N re-rank promotion (Fact #25) | **(a) Promote** to an explicit named sub-stage between C2 and C3. | SQ3+SQ4 will hyperparameter-sweep N ∈ {5, 10, 15, 20}; C2 candidates evaluated jointly with re-rank cost. Top-N re-rank by inlier-count is now a hard pipeline component, not implicit. |
### SQ2 — Component-pruning carried into SQ3+SQ4 (Jetson-pre-screen result)
Per Fact #26 (RTX-3090-measured runtime → conservative Jetson-Orin-Nano translation):
- **C2 candidates entering SQ3+SQ4 with mandatory Jetson MVE**: MixVPR, SALAD, SelaVPR, EigenPlaces, NetVLAD.
- **C2 candidates entering SQ3+SQ4 conditional on INT8 quantization path**: AnyLoc, BoQ, DINOv2-VLAD.
- **C2 candidates pruned outright**: SuperGlue-as-reranker (latency).
- **C3 candidates entering SQ3+SQ4 with mandatory Jetson MVE**: LightGlue, XFeat, XFeat*, SP+LightGlue (NGPS template confirmed).
- **C3 candidates pruned outright**: RoMa, MASt3R, DKM (dense-matcher latency on Jetson).
- **C3 candidates as "AerialExtreMatch reference points" only**: GIM+DKM, GIM+LightGlue (per Source #40 — accuracy benchmark, not for production deployment).
## C9 / SQ7 Restructure (2026-05-08, user choice A)
**Decision**: drop C9 (Datasets + SITL / replay) entirely from the research scope. Defer dataset-corpus selection, SITL framework choice (ArduPilot Plane SITL + iNav SITL/HITL), and replay framework choice (custom vs PX4-Avionics-Replay-style) to **Test Spec (greenfield Step 5)**. Pull D-C7-1 (calibration-dataset-strategy) back inside C7 batch 1 and close it there.
**Rationale**: datasets are test fixtures, not architectural commitments. They feed into Test Spec → Decompose Tests → Implement Tests, not into the deployed pipeline on the Jetson. They don't bind against the AC-4.1 / AC-4.2 / R-NEW-2 / R-NEW-4 envelope. Choosing among AerialVL S03 vs AerialExtreMatch vs VPR-Bench vs MahalNotchVPR / Mid-Air UAV vs the project's own Mavic + Derkachi flight footage is a "what evidence proves the system meets AC-X" question, not a "what gets implemented on the Orin Nano" question. SITL and replay framework choice are test-infra commitments rather than runtime commitments; SITL framework is largely deterministic at this point (ArduPilot Plane SITL + iNav SITL/HITL are the canonical paths the locked C8 closure already implies).
**Effective changes**:
- **Component Areas table**: C9 removed; remaining components are C1–C8 + C10.
- **Sub-Questions table**: SQ7 is deferred to Test Spec (Step 5) — its query variants and dataset shortlist remain documented here for handoff but are not researched in this Mode A run.
- **SQ2 closure table**: "Datasets / SITL / replay" row → "Deferred to Test Spec".
- **D-C7-1 (calibration-dataset-strategy)**: closed inside C7 batch 1. Strategy = prefer real UAV nadir flight footage at ~1 km AGL over season-matched satellite tiles as the calibration corpus distribution; specific fixture-file selection (AerialVL S03 vs project's Mavic + Derkachi clips vs other corpora) is fixture-class and delegated to Test Spec. Synthetic-tile augmentation via random homography is a documented low-data fallback, only invoked if real flight footage is insufficient for Recall@K-target calibration.
- **Cross-component gates**: D-C7-1 is no longer cross-coupled to C9; owner narrows to Plan-phase architect (closed at research time).
- **Cross-row dependencies in C7 / C8 fact cards and fit-matrix files**: every "C9 datasets / SITL / replay row when opened" reference becomes "Test Spec (Step 5) when opened".
**Carryforward to Test Spec (Step 5)** — preserved here so Test Spec's first invocation has the handoff payload ready:
- **Dataset shortlist**: AerialVL (VISTA / NTU), AerialExtreMatch, VPR-Bench, MahalNotchVPR / Mid-Air UAV, project's own Mavic + Derkachi flights.
- **SITL frameworks**: ArduPilot Plane SITL (canonical), iNav SITL/HITL (canonical); Gazebo / Webots noted-and-rejected as overkill for the spoof-promotion + visual-blackout failsafe scenarios that AC-NEW-2 and AC-NEW-8 actually exercise.
- **Replay frameworks**: PX4-Avionics-Replay-style canonical reference; custom Python harness as the lightweight default if PX4 replay's MAVLink-injection point doesn't cleanly match the C8 closure's per-FC injection cadence (5 Hz GPS_INPUT for AP / 5 Hz MSP2_SENSOR_GPS for iNav).
- **SQ7 query variants** (carried forward verbatim from above): "AerialVL dataset", "AerialExtreMatch", "VPR-Bench cross-season aerial", "Mid-Air UAV dataset", "Mavic Mavik UAV public flight dataset", "satellite-aerial cross-view localization benchmark".
- **Test-coverage obligations Test Spec must answer**:
- Which corpora exercise which AC (AC-1.1 / AC-1.2 / AC-2.1 / AC-2.2 / AC-3.1 / AC-3.2 / AC-3.3 / AC-3.4 / AC-NEW-1 / AC-NEW-2 / AC-NEW-4 / AC-NEW-7 / AC-NEW-8).
- SITL test-harness shape exercising AC-NEW-2 spoof-promotion <3 s end-to-end on **both** ArduPilot Plane SITL **and** iNav SITL/HITL (per locked C8 batch 1 closure cross-component decision D-C8-2).
- Replay-fixture format compatible with both C8 injection paths (pymavlink GPS_INPUT for AP, YAMSPy MSP2_SENSOR_GPS for iNav).
- INT8 calibration corpus pin (specific files satisfying the C7 batch 1 D-C7-1 strategy = real UAV nadir flight footage at ~1 km AGL over season-matched satellite tiles).
## C10 Scope Restructure (2026-05-08, user choice C — cross-coupling minimal)
**Decision**: narrow C10 (Pre-flight cache provisioning + sector classification + freshness pipeline) research scope to the two cross-coupling confirmation sub-areas. Defer the operator-side CLI/desktop tool, sector classification heuristics, and tile age-stamping/freshness schema to Plan-phase as `operator tooling design` out-of-research-scope.
**In-scope (C10 batch 1)**:
1. **D-C6-3 confirmation** — descriptor-cache rebuild trigger pipeline. Recommendation inherited from C6 batch 1 (Fact #92 + D-C6-3) = `periodic rebuild during C10 pre-flight provisioning + faiss.write_index serialize + load-at-takeoff in <5 s`. Confirmation work: pin the orchestration tool (FAISS Python API vs subprocess invocation), the trigger semantics (manifest hash change vs operator-manual vs new-tile-delivered), the on-disk file format, the rebuild time budget at pre-flight, and the failure-mode + retry behavior.
2. **D-C7-7 confirmation** — TensorRT engine-build pipeline. Recommendation inherited from C7 batch 1 (Fact #94 + D-C7-7) = `primary build-on-deployed-Jetson during pre-flight + reference-Jetson-built engines as fallback`. Confirmation work: pin the build-orchestration tool (`trtexec` CLI vs Python `IBuilderConfig` vs Polygraphy), the calibration-corpus shipping mechanism into the pre-flight build (per D-C7-1 closure: real UAV nadir flight footage at ~1 km AGL over season-matched satellite tiles), the per-model build-duration budget, the retry/fallback logic on build failure, and the on-disk engine cache layout.
**Out-of-research-scope (deferred to Plan-phase)**:
- Operator-side CLI/desktop tool design (mission-prep tooling shape; CLI vs GUI; integration with QGC plan files / MAVProxy / Mission Planner equivalents).
- Sector classification (active-conflict vs stable rear) heuristics + interface — used to decide AC-8.2 freshness threshold (6 mo vs 12 mo).
- Tile age-stamping + freshness schema beyond what AC-8.2 + AC-NEW-6 already mandate.
**Rationale for narrowing**:
- The C6 and C7 closures already locked architectural recommendations (`periodic rebuild during pre-flight` and `build-on-deployed-Jetson at pre-flight`). What remains is mechanism confirmation, not candidate enumeration.
- The deferred items are fixture/operator-tooling-class concerns. Their cross-coupling with the runtime architecture is mediated entirely by the descriptor-cache file and the TensorRT engine cache file — both fixed by the in-scope confirmations. Operator tool design can iterate freely at Plan-phase without touching runtime contracts.
- Aligns with the C9-restructure precedent: keep research focused on architecture-binding decisions; push fixture/tooling decisions to the phases that own them.
**Effective changes**:
- **Component Areas table**: C10 row preserved with reduced scope. Per-FC details below.
- **`Required outputs` for C10 in the table**: narrows from `Tooling (operator-side) to pull tiles from Suite Sat Service for an operational area, classify active-conflict vs stable rear, age-stamp, populate descriptor index` to `Confirmed orchestration mechanism for descriptor-cache rebuild + TensorRT engine build at pre-flight; on-disk artifact format(s); time/memory budget; failure-mode + retry behavior`.
- **Cross-component gates**: D-C6-3 and D-C7-7 remain owned jointly with C10; new C10-internal decisions D-C10-x will be added at C10 batch 1 closure.
- **SQ5 interleaving**: limited C10 SQ5 facts (failure modes during pre-flight build/rebuild) collected during this batch.
**Carryforward to Plan-phase** — operator-tooling design issues preserved here so Plan-phase has a starting list:
- Tool shape: integrate as a sub-command of Mission Planner / QGC plan-file workflow vs standalone CLI vs lightweight desktop GUI.
- Sector-classification source: operator-marked geofence polygons vs Suite Sat Service metadata vs hybrid.
- Tile age-stamping: per-tile capture date in manifest (already mandated by restrictions.md) vs additional sector-class tag vs full audit trail per AC-NEW-7.
- Freshness pipeline: when to re-pull from Suite Sat Service (every flight, weekly, on operator demand, on sector-class change).
## Next Step
SQ1 ✓ → SQ2 ✓ (with three architectural decisions resolved) → **SQ3+SQ4 per component (C1→C8)** ✓ → **C10 batch 1 in progress (cross-coupling minimal scope, 2 sub-areas: D-C6-3 + D-C7-7 confirmation)** → SQ5 interleaved → SQ8 → SQ9 synthesis at engine Step 8.
(SQ7 deferred to Test Spec per C9 restructure; C9 dropped; C10 operator-tooling-design deferred to Plan-phase per the C10 scope restructure above.)
Pipeline shape (final, post-C10-restructure): `C1 (VIO) → C2 (VPR) → Top-N re-rank by inlier count → C3 (matcher) → AdHoP-conditional refinement → C4 (PnP+RANSAC+LM) → C5 (estimator) → C8 (FC adapter)` with C6 (cache, 2D ortho) + C7 (Jetson runtime) + C10 (pre-flight orchestration: descriptor-cache rebuild + TensorRT engine build) cross-cutting.
First C1 (VIO) candidate batch: VINS-Mono / VINS-Fusion / OpenVINS / OKVIS2 / DROID-SLAM / DPVO / pure-VO baseline (RTAB-Map and ORB-SLAM3 already pruned by Fact #16). Per-mode `context7` capability verification mandatory for every lead library/SDK candidate.
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# Source Registry — Summary & Index
> Mode A Phase 2 — engine Step 2 (Source Tiering & Exhaustive Web Investigation).
> Critical-novelty sensitivity per Step 0.5 in `../00_question_decomposition.md`. Time windows applied:
> - **Lead-candidate / SOTA claims**: prefer sources within last 6 months; up to 18 months if older is the official authority.
> - **Library/SDK API behaviour**: must reflect the currently shipped version at search time (`context7` mandatory per lead candidate).
> - **Established baselines** (KLT, RANSAC, EKF, ORB, SIFT, GTSAM): no time window.
>
> Investigation order saved in `../00_question_decomposition.md` → "Next Step": SQ6 → SQ1 → SQ2 → SQ3+SQ4 per component (C1→C8) ✓ → C10 next → SQ5 interleaved → SQ8 → SQ9 synthesis at engine Step 8. **SQ7 (datasets / SITL / replay) deferred to Test Spec (greenfield Step 5) per 2026-05-08 C9 / SQ7 restructure** — see `../00_question_decomposition.md` → "C9 / SQ7 Restructure" section.
>
> This folder replaces the previous monolithic `01_source_registry.md`. The full per-source description for any source `#N` in the table below lives in the category file linked in its row.
## Category Index
| Category | File | Sources | Status |
|---|---|---|---|
| SQ6 — ArduPilot Plane vs iNav external positioning | [`SQ6_external_positioning.md`](SQ6_external_positioning.md) | #1–#24 | Saturated for protocol-level architectural decision |
| SQ1 — Existing GPS-denied UAV systems | [`SQ1_existing_systems.md`](SQ1_existing_systems.md) | #25–#37 | Saturated |
| SQ2 — Canonical pipeline decomposition | [`SQ2_canonical_pipeline.md`](SQ2_canonical_pipeline.md) | #38–#42 | Saturated |
| C1 — VIO candidates | [`C1_vio.md`](C1_vio.md) | #43–#56 | Closed at documentary level |
| C2 — VPR candidates | [`C2_vpr.md`](C2_vpr.md) | #57–#68 | Mandatory pre-screen complete (5/5) |
| C3 — Matcher candidates | [`C3_matchers.md`](C3_matchers.md) | #69–#81 | Closed at documentary level |
| C4 — Pose estimation candidates | [`C4_pose_estimation.md`](C4_pose_estimation.md) | #82–#87 | Closed at 3/N |
| C5 — State estimator / sensor fusion candidates | [`C5_state_estimator.md`](C5_state_estimator.md) | #88–#91 | Closed at 2/N (batch 1 closed) |
| C6 — Tile cache + spatial index candidates | [`C6_tile_cache_spatial_index.md`](C6_tile_cache_spatial_index.md) | #92–#98 | Closed at 2/N (batch 1 closed) — Cand 1 (mirror-suite-pattern) RECOMMENDED PRIMARY; Cand 2 (PostGIS+pgvector) DEFERRED secondary |
| C7 — On-Jetson inference runtime candidates | [`C7_inference_runtime.md`](C7_inference_runtime.md) | #99–#105 | Closed at 3/N (batch 1 closed 2026-05-08) — Cand 1 (TensorRT native) RECOMMENDED PRIMARY; Cand 2 (ONNX Runtime + TRT EP) modern-competitive-lead-cross-architecture-portability; Cand 3 (pure PyTorch FP16) mandatory simple-baseline |
| C8 — MAVLink / MSP2 FC adapter candidates | [`C8_fc_adapter.md`](C8_fc_adapter.md) | #106–#113 | Closed at 3/N (batch 1 closed 2026-05-08) — Cand 1 (pymavlink → MAVLink GPS_INPUT) RECOMMENDED PRIMARY for ArduPilot Plane; Cand 2 (MSP2_SENSOR_GPS via Python MSP V2) RECOMMENDED PRIMARY for iNav (locked SQ6 + AC-4.3 transport); Cand 3 (UBX impersonation via pyubx2 NAV-PVT) DEFERRED secondary for iNav after comparative-improvement verdict |
| C10 — Pre-flight cache provisioning (CROSS-COUPLING MINIMAL scope per 2026-05-08 user choice C; D-C6-3 + D-C7-7 confirmation pipelines only, operator tooling deferred to Plan-phase) | [`C10_preflight_provisioning.md`](C10_preflight_provisioning.md) | #114–#121 | Closed at 2/N (batch 1 closed 2026-05-08) — D-C6-3 confirmation: direct `faiss.write_index`/`faiss.read_index` Python API + `python-atomicwrites` + content-hash verification gate at takeoff (FAISS MIT, atomicwrites MIT); D-C7-7 confirmation: hybrid Polygraphy CLI primary + `trtexec` for cache-reuse fast rebuilds + direct `IBuilderConfig` Python API escape hatch (Polygraphy + TensorRT 10.x Apache-2.0 throughout) |
| **Mode B addendum (2026-05-08)** — solution_draft01 assessment | [`MODEB_addendum.md`](MODEB_addendum.md) | **#122–#131** (10 sources) | New sources gathered for Mode B findings F1–F20: VINS-Mono GPL-3.0 LICENCE confirmation (#122), MegaLoc + UltraVPR + AirZoo aerial-VPR successor candidates (#123, #124, #125), CVE-2026-1579 MAVLink no-default-auth + CVE-2025-53644 OpenCV crafted-JPEG (#126, #127), ArduPilot MAVLink2 message-signing + iNav signing-gap (#128, #129), ArduPilot `MAV_CMD_SET_EKF_SOURCE_SET` no-deployed-GCS-implementer re-verification (#130), XoFTR + 2026 SAR-optical 24-matcher benchmark (#131). |
## Investigation Status
| Sub-question | Status | Notes |
|---|---|---|
| SQ6 — ArduPilot vs iNav external positioning | **Saturated for protocol-level architectural decision** (further detail deferred to SQ8 for spoofing-side fields and to design phase for SITL parameter tuning) | Major finding: iNav has no inbound external-positioning MAVLink handler; AC-4.3 wording must be revised. See `../02_fact_cards/SQ6_fc_external_positioning.md` "SQ6 Conclusions". |
| SQ1 — Existing GPS-denied UAV systems | **Saturated.** 13 sources logged across academic / open-source / commercial / defense-program / Ukraine-practitioner. Closest peer system: Twist Robotics OSCAR (deployed in Ukraine). Closest open-source pipeline-match: snktshrma/ngps_flight (NGPS, ArduPilot GSoC 2024 — LightGlue+SuperPoint+UKF+VISION_POSITION_ESTIMATE). Closest deployed commercial: Auterion Artemis (Skynode N + Visual Navigation, Ukraine-tested, 1000-mile range). | See `../02_fact_cards/SQ1_existing_systems.md` cluster + working summary. |
| SQ2 — Canonical pipeline decomposition | **Saturated.** 5 surveys/benchmarks logged (Skoltech aerial VPR, U.Maine cross-view, OrthoLoC 2.5D geodata, AnyVisLoc low-altitude multi-view, NUDT 2026 sciopen survey). All converge on **`retrieval → matching → pose-estimation`** hierarchical framework with VIO/IMU as auxiliary. Two new architectural facts added to C1–C10: (a) **AdHoP-style perspective-refinement loop** between matching and PnP (+63% translation accuracy, method-agnostic), (b) **DSM 2.5D dependency** for full 6-DoF on aerial-to-satellite (must be resolved with the Suite Sat Service or accepted as a 3-DoF degraded mode). Practitioner runtime evidence: AnyLoc on RTX 3090 = 0.63s/descriptor, SuperGlue re-rank = 17–25s; on Jetson Orin Nano these are non-viable for our 400 ms p95 budget — must restrict to lightweight VPR (e.g., MixVPR / SALAD class) + LightGlue/XFeat-class matchers. See `../02_fact_cards/SQ2_canonical_pipeline.md` "SQ2 Conclusions". |
| SQ3+SQ4 — Per-component candidates (C1–C10) | **In progress** — C1 (VIO) **CLOSED** at documentary level (Sources #43–#56). C2 (VPR) — **mandatory pre-screen COMPLETE at documentary level (5 of 5 candidates)**: MixVPR (Sources #57+#58), SALAD (Sources #59+#60+#61), SelaVPR (Sources #62+#63), NetVLAD (Sources #64+#65+#66), **EigenPlaces (Sources #67+#68 — closure 2026-05-08)**. All five mandatory candidates have per-mode API capability verification ✅, per-numbered-Restriction × per-numbered-AC sub-matrix written, and `../06_component_fit_matrix/C2_vpr.md` rows populated. **Conditional pre-screen candidates (AnyLoc / BoQ / DINOv2-VLAD)** are GATED on a prerequisite **INT8 quantization survey** before they can be added to per-mode rows (per Fact #26 pre-screen rule). C3 closed at documentary level (Sources #69–#81). C4 closed at 3/N (Sources #82–#87). **C5 CLOSED at 2/N — batch 1 closed 2026-05-08** (mandatory simple-baseline = Manual ESKF Solà 2017 [Sources #88–#89]; modern-competitive-lead-factor-graph = GTSAM iSAM2 + ImuFactor + smart factors + Marginals [Sources #90–#91]). **C6 CLOSED at 2/N — batch 1 closed 2026-05-08** (Cand 1 RECOMMENDED PRIMARY = mirror-of-suite-satellite-provider pattern: PostgreSQL btree + bytea + FAISS HNSW + filesystem [Sources #92+#96+#97+#98]; Cand 2 DEFERRED secondary = PostGIS GiST + pgvector HNSW + filesystem [Sources #94+#95]; Source #93 = PostgreSQL btree multicolumn-indexes docs cross-cite). **C7 CLOSED at 3/N — batch 1 closed 2026-05-08** (Cand 1 RECOMMENDED PRIMARY = TensorRT native [Sources #99+#104+#105]; Cand 2 modern-competitive-lead-cross-architecture-portability = ONNX Runtime + TRT EP [Source #100 + #103]; Cand 3 mandatory simple-baseline = pure PyTorch FP16 [Source #101]; Source #102 = YOLO26 Jetson Orin Nano Super benchmark; Source #103 = LightGlue+TRT+FP8 quantization-sensitivity finding driving D-C7-6 cross-component precision policy). **C8 CLOSED at 3/N — batch 1 closed 2026-05-08** (Cand 1 RECOMMENDED PRIMARY for ArduPilot = pymavlink → MAVLink GPS_INPUT msg 232 cooperative-path [Sources #106+#107 + cross-cite SQ6 Source #4 AP_GPS_MAV.cpp ingestion-path]; Cand 2 RECOMMENDED PRIMARY for iNav = MSP2_SENSOR_GPS id 7939 / 0x1F03 via Python MSP V2 implementation [Sources #111+#112+#113 + cross-cite SQ6 Source #12+#13]; Cand 3 DEFERRED secondary for iNav = UBX impersonation via pyubx2 NAV-PVT [Sources #108+#109+#110 + cross-cite SQ6 Fact #10] with comparative-improvement verdict that does NOT clear user's "significant-improvement-only" bar over Cand 2; mid-batch correction via c8_inav_recovery=B preserved locked SQ6 + AC-4.3 + restrictions.md verdicts). **C9 DROPPED** from research scope per 2026-05-08 SQ7/C9 restructure (datasets/SITL/replay deferred to Test Spec greenfield Step 5). **C10 CLOSED at 2/N — batch 1 closed 2026-05-08** under CROSS-COUPLING MINIMAL scope per 2026-05-08 user choice C (operator CLI/desktop tooling, sector classification, freshness pipeline deferred to Plan-phase): D-C6-3 confirmation = direct `faiss.write_index`/`faiss.read_index` Python API + `python-atomicwrites` + content-hash (SHA-256) verification gate at takeoff load + `IO_FLAG_MMAP_IFC` mmap [Sources #114+#115+#116]; D-C7-7 confirmation = hybrid Polygraphy CLI primary for INT8-calibrating builds + `trtexec` for cache-reuse fast rebuilds + direct `IBuilderConfig` Python API escape hatch [Sources #117+#118+#119+#120+#121]; **no further C10 batches required at the research layer** — operator tooling design enters at Plan-phase. | See `../02_fact_cards/C1_vio.md` + `../02_fact_cards/C2_vpr.md` + `../02_fact_cards/C3_matchers.md` + `../02_fact_cards/C4_pose_estimation.md` + `../02_fact_cards/C5_state_estimator.md` + `../02_fact_cards/C6_tile_cache_spatial_index.md` + `../02_fact_cards/C7_inference_runtime.md` clusters; `../06_component_fit_matrix/C{1..7}_*.md` rows. |
| SQ5 — Failure modes / deployment lessons | Not started (interleaved with SQ3/SQ4) | |
| SQ7 — Datasets, SITL, replay environments | **Deferred to Test Spec (greenfield Step 5)** per 2026-05-08 C9 / SQ7 restructure | Fixture-class / test-infra-class — not researched in this Mode A run. Carryforward payload preserved in `../00_question_decomposition.md` → "C9 / SQ7 Restructure" section. |
| SQ8 — Safety considerations (AC-NEW-4 / AC-NEW-7) | Not started | Carries the AP_GPS spoofing-signal probe deferred from SQ6. |
| SQ9 — End-to-end synthesis | Step 8 of engine (deferred) | |
---
## Source Summary Table
Compact one-line index across all 121 sources. For full per-source description, follow the **File** link.
| # | Title | Tier | File |
|---|---|---|---|
| 1 | Non-GPS Navigation — Plane documentation | L1 | [SQ6](SQ6_external_positioning.md) |
| 2 | GPS / Non-GPS Transitions — Plane documentation | L1 | [SQ6](SQ6_external_positioning.md) |
| 3 | EKF Source Selection and Switching — Plane documentation | L1 | [SQ6](SQ6_external_positioning.md) |
| 4 | ArduPilot AP_GPS_MAV.cpp (master) | L1 | [SQ6](SQ6_external_positioning.md) |
| 5 | ArduPilot PR #28750 — AP_NavEKF3 EK3_OPTION bits (GPS-denied testing) | L2 | [SQ6](SQ6_external_positioning.md) |
| 6 | ArduPilot Issue #15859 — EKF3 source switching (GPS↔NonGPS) | L4 | [SQ6](SQ6_external_positioning.md) |
| 7 | ArduPilot Issue #27193 — EK3 Source Switching wrong frame for GUIDED | L4 | [SQ6](SQ6_external_positioning.md) |
| 8 | ArduPilot Issue #23485 — fuse only External Nav Velocities | L4 | [SQ6](SQ6_external_positioning.md) |
| 9 | iNavFlight/inav telemetry/mavlink.c (master inbound switch) | L1 | [SQ6](SQ6_external_positioning.md) |
| 10 | iNav Wiki — MAVLink (frogmane edited 2025-12-11) | L1 | [SQ6](SQ6_external_positioning.md) |
| 11 | iNav Wiki — GPS and Compass setup | L1 | [SQ6](SQ6_external_positioning.md) |
| 12 | iNavFlight/inav docs/development/msp/README.md (MSP message reference) | L1 | [SQ6](SQ6_external_positioning.md) |
| 13 | iNavFlight/inav src/main/io/gps.c + target/common.h (master) | L1 | [SQ6](SQ6_external_positioning.md) |
| 14 | iNav Issue #10141 — dual GPS support | L4 | [SQ6](SQ6_external_positioning.md) |
| 15 | iNav docs/GPS_fix_estimation.md (master) | L1 | [SQ6](SQ6_external_positioning.md) |
| 16 | iNav docs/Settings.md (master) | L1 | [SQ6](SQ6_external_positioning.md) |
| 17 | iNav Issue #10588 — DeadReckoning weird behaviour during GPS outage | L4 | [SQ6](SQ6_external_positioning.md) |
| 18 | iNav Release 8.0.0 (highlights, Dec 2024) | L1 | [SQ6](SQ6_external_positioning.md) |
| 19 | iNav Release 9.0.0 / 9.0.1 + Release Notes wiki | L1 | [SQ6](SQ6_external_positioning.md) |
| 20 | MAVLink common message set — GPS_RAW_INT (24) | L1 | [SQ6](SQ6_external_positioning.md) |
| 21 | MAVLink PR #2110 — gps: add status and integrity information | L2 | [SQ6](SQ6_external_positioning.md) |
| 22 | AirDroper — GNSS Spoofing Filter companion device | L3 | [SQ6](SQ6_external_positioning.md) |
| 23 | ArduPilot PR #24135 — EKF3 robust to bad IMU and lane-switching | L2 | [SQ6](SQ6_external_positioning.md) |
| 24 | ArduPilot AP_NavEKF3 — VehicleStatus.cpp + AP_NavEKF3.cpp (master) | L1 | [SQ6](SQ6_external_positioning.md) |
| 25 | Twist Robotics OSCAR — visual navigation system (Ukraine deployment) | L2 | [SQ1](SQ1_existing_systems.md) |
| 26 | Ukraine Drones with Vision-Based Navigation Past Heavy Jamming (TWZ) | L2 | [SQ1](SQ1_existing_systems.md) |
| 27 | Ukraine's Ruta Missile Drone EW-Immune Navigation (Defense Express) | L2 | [SQ1](SQ1_existing_systems.md) |
| 28 | Kilometer-Scale GNSS-Denied UAV Navigation via Heightmap Gradients | L1 | [SQ1](SQ1_existing_systems.md) |
| 29 | Hierarchical Image Matching for UAV Absolute Visual Localization | L1 | [SQ1](SQ1_existing_systems.md) |
| 30 | Raptor — GPS-Denied UAV Navigation & Coordinate Extraction (Vantor) | L2 | [SQ1](SQ1_existing_systems.md) |
| 31 | Auterion Artemis program — long-range deep-strike completion | L1 | [SQ1](SQ1_existing_systems.md) |
| 32 | Auterion Skynode N — AI/CV for small autonomous systems | L2 | [SQ1](SQ1_existing_systems.md) |
| 33 | snktshrma/ngps_flight — NGPS for ArduPilot (GSoC 2024) | L1 | [SQ1](SQ1_existing_systems.md) |
| 34 | AerialExtreMatch — benchmark for extreme-view image matching/localization | L1 | [SQ1](SQ1_existing_systems.md) |
| 35 | DARPA Fast Lightweight Autonomy (FLA) program page + T&E review | L1 | [SQ1](SQ1_existing_systems.md) |
| 36 | DSMAC / TERCOM lineage — DTIC ADA315439 | L1 | [SQ1](SQ1_existing_systems.md) |
| 37 | Electronic Warfare in Ukraine — Ukraine War Analytics | L3 | [SQ1](SQ1_existing_systems.md) |
| 38 | VPR for Aerial Imagery: A Survey (Skoltech, Moskalenko et al.) | L1 | [SQ2](SQ2_canonical_pipeline.md) |
| 39 | Cross-View Geo-Localization: A Survey (U. Maine) | L1 | [SQ2](SQ2_canonical_pipeline.md) |
| 40 | OrthoLoC: UAV 6-DoF Localization with Orthographic Geodata | L1 | [SQ2](SQ2_canonical_pipeline.md) |
| 41 | AnyVisLoc — UAV visual localization, low-altitude multi-view | L1 | [SQ2](SQ2_canonical_pipeline.md) |
| 42 | NUDT 2026 — survey on absolute visual localization for low-altitude UAV | L1 | [SQ2](SQ2_canonical_pipeline.md) |
| 43 | VINS-Mono — robust monocular visual-inertial state estimator | L1 | [C1](C1_vio.md) |
| 44 | VINS-Fusion — optimization-based multi-sensor state estimator | L1 | [C1](C1_vio.md) |
| 45 | OpenVINS — open-source VI navigation research platform | L1 | [C1](C1_vio.md) |
| 46 | Run VIO on NVIDIA Jetson — KAIST benchmark | L1 | [C1](C1_vio.md) |
| 47 | OKVIS2 — realtime scalable VI-SLAM with loop closure | L1 | [C1](C1_vio.md) |
| 48 | OKVIS2-X — open keyframe VI-SLAM with dense depth | L1 | [C1](C1_vio.md) |
| 49 | Kimera-VIO — VIO with SLAM + 3D mesh (MIT-SPARK, BSD) | L1 | [C1](C1_vio.md) |
| 50 | DROID-SLAM — deep visual SLAM (princeton-vl) | L1 | [C1](C1_vio.md) |
| 51 | DPVO / DPV-SLAM — deep patch visual odometry | L1 | [C1](C1_vio.md) |
| 52 | DPVO-QAT++ — heterogeneous QAT + CUDA kernel fusion for DPVO | L2 | [C1](C1_vio.md) |
| 53 | Pure-VO baseline — KLT optical flow + 5-point/homography RANSAC (OpenCV) | L1 | [C1](C1_vio.md) |
| 54 | OpenVINS — context7 per-mode capability lookup (`/rpng/open_vins`) | L1 | [C1](C1_vio.md) |
| 55 | VINS-Mono README + VINS-Fusion context7 per-mode lookup | L1 | [C1](C1_vio.md) |
| 56 | OKVIS2 — official README (`smartroboticslab/okvis2`, main) | L1 | [C1](C1_vio.md) |
| 57 | OpenVPRLab — open-source VPR framework (MixVPR / BoQ / NetVLAD / GeM) | L1 | [C2](C2_vpr.md) |
| 58 | MixVPR canonical paper (WACV 2023, arXiv:2303.02190) | L1 | [C2](C2_vpr.md) |
| 59 | SALAD canonical implementation (`serizba/salad`, GPL-3.0) | L1 | [C2](C2_vpr.md) |
| 60 | SALAD canonical paper — Optimal Transport Aggregation (CVPR 2024) | L1 | [C2](C2_vpr.md) |
| 61 | OpenVPRLab DinoV2 backbone — context7 cross-source for ViT-B/14 | L1 | [C2](C2_vpr.md) |
| 62 | SelaVPR canonical implementation (`Lu-Feng/SelaVPR`, MIT) | L1 | [C2](C2_vpr.md) |
| 63 | SelaVPR canonical paper (ICLR 2024, arXiv:2402.14505) | L1 | [C2](C2_vpr.md) |
| 64 | NetVLAD canonical implementation `Relja/netvlad` v1.03 (MIT) | L1 | [C2](C2_vpr.md) |
| 65 | NetVLAD modern PyTorch reproduction `Nanne/pytorch-NetVlad` | L2 | [C2](C2_vpr.md) |
| 66 | NetVLAD canonical paper (CVPR 2016 / TPAMI 2018, arXiv:1511.07247) | L1 | [C2](C2_vpr.md) |
| 67 | EigenPlaces canonical implementation (`gmberton/EigenPlaces`, MIT) | L1 | [C2](C2_vpr.md) |
| 68 | EigenPlaces canonical paper (ICCV 2023, arXiv:2308.10832) | L1 | [C2](C2_vpr.md) |
| 69 | LightGlue — context7 per-mode capability lookup (`/cvg/lightglue`) | L1 | [C3](C3_matchers.md) |
| 70 | LightGlue canonical implementation (`cvg/LightGlue`) | L1 | [C3](C3_matchers.md) |
| 71 | LightGlue canonical paper (ICCV 2023, arXiv:2306.13643) | L1 | [C3](C3_matchers.md) |
| 72 | LightGlue HuggingFace Transformers integration | L1 | [C3](C3_matchers.md) |
| 73 | LightGlue-ONNX — `fabio-sim/LightGlue-ONNX` (Jetson TensorRT path) | L2 | [C3](C3_matchers.md) |
| 74 | ALIKED canonical implementation (`Shiaoming/ALIKED`) | L1 | [C3](C3_matchers.md) |
| 75 | ALIKED canonical paper (TIM 2023, arXiv:2304.03608) | L1 | [C3](C3_matchers.md) |
| 76 | DISK canonical implementation (`cvlab-epfl/disk`, Apache-2.0) | L1 | [C3](C3_matchers.md) |
| 77 | DISK canonical paper — RL-trained local features (NeurIPS 2020) | L1 | [C3](C3_matchers.md) |
| 78 | SuperGlue canonical implementation (`magicleap/SuperGluePretrainedNetwork`) | L1 | [C3](C3_matchers.md) |
| 79 | SuperGlue canonical paper — graph-NN feature matching (CVPR 2020) | L1 | [C3](C3_matchers.md) |
| 80 | XFeat canonical implementation (`verlab/accelerated_features`, Apache-2.0) | L1 | [C3](C3_matchers.md) |
| 81 | XFeat canonical paper — accelerated features (CVPR 2024) | L1 | [C3](C3_matchers.md) |
| 82 | OpenCV canonical implementation — `opencv/opencv` (calib3d module) | L1 | [C4](C4_pose_estimation.md) |
| 83 | OpenCV 4.x calib3d module canonical documentation | L1 | [C4](C4_pose_estimation.md) |
| 84 | OpenGV canonical implementation (`laurentkneip/opengv`) | L1 | [C4](C4_pose_estimation.md) |
| 85 | OpenGV canonical Doxygen documentation portal | L1 | [C4](C4_pose_estimation.md) |
| 86 | GTSAM canonical implementation (`borglab/gtsam`, BSD-3) | L1 | [C4](C4_pose_estimation.md) |
| 87 | GTSAM canonical Python documentation via context7 | L1 | [C4](C4_pose_estimation.md) |
| 88 | Solà 2017 — "Quaternion kinematics for the error-state Kalman filter" (arXiv:1711.02508) | L1 | [C5](C5_state_estimator.md) |
| 89 | Reference open-source ESKF implementations (canonical-paper-derived) | L2 | [C5](C5_state_estimator.md) |
| 90 | GTSAM `ImuFactor` / `CombinedImuFactor` / `PreintegratedImuMeasurements` / `PreintegratedCombinedMeasurements` (context7 indexed) | L1 | [C5](C5_state_estimator.md) |
| 91 | GTSAM `ISAM2` / `IncrementalFixedLagSmoother` / `Marginals` with iSAM2 results (context7 indexed) | L1 | [C5](C5_state_estimator.md) |
| 92 | Parent-suite `satellite-provider` existing pattern (PostgreSQL + Dapper + filesystem tile storage; verified directly) | L1 | [C6](C6_tile_cache_spatial_index.md) |
| 93 | PostgreSQL 16 official documentation — Multicolumn Indexes + btree access method | L1 | [C6](C6_tile_cache_spatial_index.md) |
| 94 | PostGIS official documentation — GiST + KNN distance ordering + ST_DWithin | L1 | [C6](C6_tile_cache_spatial_index.md) |
| 95 | pgvector official documentation — HNSW index API (context7 + canonical README) | L1 | [C6](C6_tile_cache_spatial_index.md) |
| 96 | FAISS official documentation — IndexFlatL2 / IndexHNSWFlat / IndexIVFFlat (context7 indexed) | L1 | [C6](C6_tile_cache_spatial_index.md) |
| 97 | Postgres on NVIDIA Jetson Orin Nano — March 2026 Medium article + Coding Steve minimal-config guide | L2 | [C6](C6_tile_cache_spatial_index.md) |
| 98 | Slippy Map Tilenames — OpenStreetMap canonical specification (Web Mercator XYZ) | L1 | [C6](C6_tile_cache_spatial_index.md) |
| 99 | NVIDIA TensorRT 10.x official documentation portal (context7-indexed `/nvidia/tensorrt`) | L1 | [C7](C7_inference_runtime.md) |
| 100 | Microsoft ONNX Runtime official documentation (context7-indexed `/microsoft/onnxruntime`) + Jetson AI Lab community wheel index | L1 | [C7](C7_inference_runtime.md) |
| 101 | PyTorch official documentation (context7-indexed `/pytorch/pytorch`) + Jetson AI Lab PyTorch wheel availability for JetPack 6 | L1 | [C7](C7_inference_runtime.md) |
| 102 | Ultralytics YOLO26 benchmark suite on Jetson Orin Nano Super (April 2026) | L2 | [C7](C7_inference_runtime.md) |
| 103 | LightGlue ONNX Runtime + TensorRT acceleration + FP8 ModelOpt quantization findings (Fabio Sim's Journal) | L2 | [C7](C7_inference_runtime.md) |
| 104 | JetPack SDK release notes (NVIDIA official) — JetPack 6.0 / 6.1 / 6.2 version matrix | L1 | [C7](C7_inference_runtime.md) |
| 105 | TensorRT-on-Jetson canonical install constraints (Ultralytics issue reports + NVIDIA forum) | L2 | [C7](C7_inference_runtime.md) |
| 106 | ArduPilot Pymavlink (context7-indexed `/ardupilot/pymavlink`) — canonical Python MAVLink stack | L1 | [C8](C8_fc_adapter.md) |
| 107 | ArduPilot Plane Non-GPS Position Estimation + MAVProxy GPS Input module dev docs (`GPS1_TYPE=14`, `EK3_SRC1_POSXY=3`) | L1 | [C8](C8_fc_adapter.md) |
| 108 | pyubx2 (context7-indexed `/semuconsulting/pyubx2`) — canonical Python UBX/NMEA/RTCM3 parser | L1 | [C8](C8_fc_adapter.md) |
| 109 | u-blox NEO-M9N Integration Manual (UBX-19014286) + u-blox 8/M8 Receiver Description (UBX-13003221) — UBX-NAV-PVT canonical specification | L1 | [C8](C8_fc_adapter.md) |
| 110 | iNav `gps_ublox.c` source (master) — UBX validation gates `gpsMapFixType()` requires `flags & 0x01 = 1` AND `fixType ∈ {2,3}` | L1 | [C8](C8_fc_adapter.md) |
| 111 | iNav `docs/development/msp/README.md` (master) — `MSP2_SENSOR_GPS (7939 / 0x1F03)` canonical 36-byte payload spec | L1 | [C8](C8_fc_adapter.md) |
| 112 | Python MSP2 implementations: YAMSPy + INAV-Toolkit `inav_msp.py` (MSP V2 `msp_v2_encode` with CRC-8 DVB-S2) | L2 | [C8](C8_fc_adapter.md) |
| 113 | iNav `src/main/msp/msp_protocol_v2_sensor.h` (master) — MSP V2 sensor command-ID range (0x1F00-0x1FFF) | L1 | [C8](C8_fc_adapter.md) |
| 114 | FAISS `write_index` / `read_index` Python API + on-disk format + security warning (canonical wiki + context7) | L1 | [C10](C10_preflight_provisioning.md) |
| 115 | FAISS IndexHNSWFlat per-vector memory + on-disk file size formula (Discussions #3953 + C++ API docs) | L2 | [C10](C10_preflight_provisioning.md) |
| 116 | Python atomic file write pattern (gocept blog + python-atomicwrites docs + Python Issue 8604) | L2 | [C10](C10_preflight_provisioning.md) |
| 117 | Polygraphy `polygraphy convert` CLI for TensorRT INT8 engine build with calibration cache reuse (NVIDIA TensorRT repo + context7) | L1 | [C10](C10_preflight_provisioning.md) |
| 118 | Polygraphy `Calibrator` class API — algo defaults + dynamic-shapes calibration profile + warning behavior (NVIDIA TRT/Polygraphy SDK docs) | L1 | [C10](C10_preflight_provisioning.md) |
| 119 | `trtexec` CLI for one-off engine builds — INT8/FP16 flags + calibration cache support (NVIDIA TRT SDK docs) | L1 | [C10](C10_preflight_provisioning.md) |
| 120 | TensorRT INT8 calibration corpus size guidance (~500-1000 images) — Jetson AGX Orin (vendor engineering guide) | L2 | [C10](C10_preflight_provisioning.md) |
| 121 | Direct TensorRT `IBuilderConfig` + `IInt8EntropyCalibrator2` Python API (NVIDIA TRT Python API docs, cross-cite from C7 #105) | L1 | [C10](C10_preflight_provisioning.md) |
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# Source Registry — C10: Pre-flight cache provisioning (cross-coupling minimal scope)
> Mode A Phase 2 — engine Step 2 (Source Tiering & Exhaustive Web Investigation). Sources for C10 batch 1 (cross-coupling minimal: D-C6-3 descriptor-cache rebuild trigger pipeline + D-C7-7 TensorRT engine-build pipeline). Sibling registries: [SQ1](SQ1_existing_systems.md), [SQ2](SQ2_canonical_pipeline.md), [SQ6](SQ6_external_positioning.md), [C1](C1_vio.md), [C2](C2_vpr.md), [C3](C3_matchers.md), [C4](C4_pose_estimation.md), [C5](C5_state_estimator.md), [C6](C6_tile_cache_spatial_index.md), [C7](C7_inference_runtime.md), [C8](C8_fc_adapter.md). Index: [`00_summary.md`](00_summary.md).
>
> Source-tier definitions per `references/source-tiering.md`: L1 = official primary docs / source code / canonical specs; L2 = official blog posts, vendor SDK docs, peer-reviewed papers; L3 = community Q&A, tutorial sites, secondary commentary; L4 = forum posts, mailing-list threads, single-author blog posts.
---
## Source #114 — FAISS `write_index` / `read_index` Python API + on-disk format + security warning (L1 official)
**URL**: <https://github.com/facebookresearch/faiss/wiki/Index-IO,-cloning-and-hyper-parameter-tuning> + context7 indexed at `/facebookresearch/faiss` (Benchmark Score consistent with C6 batch 1 Source #96 lookup)
**Date accessed**: 2026-05-08
**Tier**: **L1** — canonical FAISS GitHub Wiki + canonical context7-indexed documentation
**Relevance**: Confirms `faiss.write_index(index, path)` + `faiss.read_index(path)` Python API for serializing IndexHNSWFlat to disk and loading it back; confirms `IO_FLAG_MMAP_IFC` enables memory-mapped loading for HNSW + IndexFlatCodes-derived classes (zero-copy load — important for the project's <5 s takeoff load budget); documents the explicit security warning "No attempt is made to check the correctness of loaded data. A faulty or malicious file could lead to out-of-memory errors or code execution. Users are responsible for verifying that files loaded with `read_index` have not been altered since being written by `write_index`." This warning binds directly to AC-NEW-7 (cache-poisoning safety) and motivates the project-side content-hash verification gate before takeoff load. Confirms FAISS C++ signature: `void write_index(Index* index, const char* filename)` / `Index* read_index(const char* filename)`.
**Evidence quality**: ✅ High — L1 canonical FAISS docs. Direct API verification.
---
## Source #115 — FAISS IndexHNSWFlat per-vector memory + on-disk file size formula (L2 community + L1 cross-cite)
**URL**: <https://github.com/facebookresearch/faiss/discussions/3953> + cross-cite <https://faiss.ai/cpp_api/struct/structfaiss_1_1IndexHNSWFlat.html>
**Date accessed**: 2026-05-08
**Tier**: **L2** — FAISS GitHub Discussions thread (maintainer-confirmed answer) + L1 canonical FAISS C++ API docs cross-cite
**Relevance**: Confirms IndexHNSWFlat per-vector on-disk + RAM cost formula: `(vector_dim × 4 bytes) + (M × 4 bytes × 2) + overhead from graph layers and geometric reallocation`. For project's pinned VPR descriptor candidates (per D-C2-9 / D-C2-10 / D-C2-6 / D-C6-1 = halfvec): at 2048-D float32 + M=32 → 8192 + 256 = **8448 bytes/vector** (~845 MB on disk for 100K tiles); at 2048-D halfvec (2-byte storage per descriptor element) → 4096 + 256 = **4352 bytes/vector** (~430 MB on disk for 100K tiles); at 512-D halfvec + M=32 → 1024 + 256 = **1280 bytes/vector** (~130 MB on disk for 100K tiles); at 256-D halfvec + M=32 → 512 + 256 = **768 bytes/vector** (~80 MB on disk for 100K tiles). All variants well within AC-8.3 10 GB cache budget (assuming D-C2-10 EigenPlaces 512-D path or D-C6-1 halfvec mitigation). Supplementary cross-cite to C6 Fact #92 evidence base. **Load latency**: Issue #622 confirms post-load search performance is "slightly slower initially due to memory layout and cache effects" but identical results — implies a warmup-search-pass at takeoff after `read_index` would smooth p99 latency; aligns with the <5 s takeoff load budget (pure file read at ~430 MB / SATA SSD ~500 MB/s = <1 s; mmap path eliminates the read entirely).
**Evidence quality**: ✅ High — formula matches FAISS source code in `IndexHNSW.cpp`; multiple maintainer-confirmed reproductions; conservative for project's pinned descriptor dimensions per D-C2-9/10/6 closures.
---
## Source #116 — Python atomic file write pattern: write-to-temp + fsync + atomic rename (L2 reference + L1 POSIX standard cross-cite)
**URL**: <https://blog.gocept.com/2013/07/15/reliable-file-updates-with-python/> + <https://python-atomicwrites.readthedocs.io/en/stable> + Python tracker Issue 8604 <https://bugs.python.org/issue8604>
**Date accessed**: 2026-05-08
**Tier**: **L2** — well-known engineering blog reference + canonical Python package docs + Python core developer issue tracker
**Relevance**: Documents the canonical Python crash-safe atomic file write pattern required for the project's pre-flight FAISS index file write (and TensorRT engine file write). The pattern is: (1) write to a temporary file in the same directory as target (ensures same filesystem so `os.rename` is atomic), (2) call `fsync(temp_fd)` to flush content + metadata to disk, (3) atomically rename via `os.rename(temp_path, target_path)`, (4) call `fsync` on the parent directory to flush the filename change to disk. Without this pattern, a power loss or process kill mid-write leaves a truncated/partial file that `faiss.read_index` will load successfully (no internal integrity check per Source #114 warning) and produce silently-wrong descriptor matches at takeoff — direct violation of AC-NEW-7 (cache-poisoning safety) + AC-3.3 (re-localization stability). The `python-atomicwrites` package provides this pattern with a simple API: `with atomic_write(path, overwrite=True) as f: ...`; pure-Python; trivially auditable; cross-platform (Windows + POSIX + macOS). On macOS specifically, must use `fcntl.fcntl(fd, fcntl.F_FULLFSYNC)` instead of `os.fsync()` to handle Apple's user-space write buffers — not relevant for the Jetson deployment target (Linux/JetPack). Project-side wrapper around `faiss.write_index` should use this pattern to safely write the FAISS cache file alongside content-hash verification.
**Evidence quality**: ✅ High — pattern matches POSIX `rename(2)` atomicity guarantee; extensively documented; multiple production Python packages (atomicwrites, ruamel-yaml, etc.) implement it.
---
## Source #117 — Polygraphy `polygraphy convert` CLI for TensorRT INT8 engine build with calibration cache reuse (L1 official)
**URL**: <https://github.com/NVIDIA/TensorRT/blob/main/tools/Polygraphy/examples/cli/convert/01_int8_calibration_in_tensorrt/README.md> + context7 indexed at `/websites/nvidia_deeplearning_tensorrt_static_polygraphy` (1041 code snippets, Benchmark Score 67.2, Source Reputation High)
**Date accessed**: 2026-05-08
**Tier**: **L1** — official NVIDIA TensorRT source repository documentation + canonical Polygraphy docs
**Relevance**: Confirms Polygraphy as the canonical NVIDIA-blessed orchestration wrapper around TensorRT's engine build pipeline. Documents the canonical INT8 calibration workflow: first build with `--data-loader-script ./data_loader.py --calibration-cache identity_calib.cache` (computes scales + writes cache); subsequent builds with `--calibration-cache identity_calib.cache` (skips calibration step entirely — cache contains scales). Confirms Polygraphy's `Calibrator` class API: `data_loader` parameter (generator/iterable yielding `{input_name: numpy.ndarray}` dicts), `cache` parameter (calibration cache file path), `BaseClass` parameter (defaults to `trt.IInt8EntropyCalibrator2` — matches project's D-C7-2 + D-C7-6 lock), `algo` parameter (defaults to `trt.CalibrationAlgoType.MINMAX_CALIBRATION`). CLI supports `--int8 --fp16` mixed precision flags directly per project's D-C7-2 = (b) per-family precision policy. The full CLI invocation pattern for project: `polygraphy convert <model>.onnx --int8 --fp16 --data-loader-script ./calib_data_loader.py --calibration-cache <model>_calib.cache -o <model>_sm87_jp62_trt103_int8fp16.engine`. Polygraphy is bundled inside the TensorRT distribution (no separate install on Jetson — `pip install nvidia-pyindex && pip install polygraphy` or via TensorRT installer). Production-mature and cross-referenced from canonical TensorRT documentation.
**Evidence quality**: ✅ High — official NVIDIA repository docs, multi-snippet context7 coverage, production-mature tooling.
---
## Source #118 — Polygraphy `Calibrator` class API — algo defaults + dynamic-shapes calibration profile + warning behavior (L1 official)
**URL**: <https://docs.nvidia.com/deeplearning/tensorrt/latest/_static/polygraphy/backend/trt/calibrator.html> + <https://docs.nvidia.com/deeplearning/tensorrt/latest/_static/polygraphy/backend/trt/config.html>
**Date accessed**: 2026-05-08
**Tier**: **L1** — canonical NVIDIA TensorRT/Polygraphy SDK documentation
**Relevance**: Confirms `Calibrator(data_loader, cache=None, BaseClass=IInt8EntropyCalibrator2, algo=CalibrationAlgoType.MINMAX_CALIBRATION, batch_size=None, quantile=None, regression_cutoff=None)` full signature. Documents two algorithm choices: `IInt8EntropyCalibrator2` (entropy-based; project D-C7-2 default; Polygraphy default) vs `IInt8MinMaxCalibrator` (min-max scaling). Documents dynamic-shapes behavior: "if calibration is run and the model has dynamic shapes, the last optimization profile will be used as the calibration profile" — relevant for project's matchers if any of them export with dynamic input shapes (D-C3-2 LightGlue ONNX export pathway). Documents `--data-loader-script` / `--data-loader-func-name` CLI flags for supplying custom calibration data. Documents the "Int8 Calibration is using randomly generated input data" warning that fires when `--int8` is set but neither `--data-loader-script` nor an existing `--calibration-cache` is supplied — operationalizes the D-C7-1 closure (real UAV nadir flight footage corpus) as a pre-flight build prerequisite. CLI also supports `--load-tactics` / `--save-tactics` for replaying tactic-search results across multiple builds (faster than re-running tactic profiling each build) — useful for the reference-Jetson-prebuilt-engine fallback path per D-C7-7.
**Evidence quality**: ✅ High — canonical NVIDIA documentation, directly cited from polygraphy/tools/args/backend/trt/config source code.
---
## Source #119 — `trtexec` CLI for one-off engine builds — INT8/FP16 flags + calibration cache support (L1 official)
**URL**: <https://docs.nvidia.com/deeplearning/tensorrt/latest/getting-started/quick-start-guide.html> + <https://docs.nvidia.com/deeplearning/tensorrt/latest/reference/command-line-programs.html>
**Date accessed**: 2026-05-08
**Tier**: **L1** — canonical NVIDIA TensorRT SDK documentation
**Relevance**: Confirms `trtexec` as the simpler-but-less-flexible TensorRT engine build CLI bundled with every TensorRT installation. Canonical invocation: `trtexec --onnx=model.onnx --saveEngine=model.engine --fp16 --int8 --calib=calibration.cache --shapes=input:1x3x224x224`. Supports `--int8 --fp16` mixed precision (matches project's D-C7-2). Supports `--calib=<cache_path>` for INT8 calibration cache reuse (cache file format identical to Polygraphy's; the two tools are interoperable on the calibration cache layer). **Critical limitation vs Polygraphy**: `trtexec --int8` without `--calib` causes TRT to use random data for calibration (per TRT docs warning) — this collapses INT8 accuracy by ~5-15%. **Strength**: single-binary; no Python imports; no calibration data loader script required; perfect for emergency rebuilds when an existing calibration cache is available; perfect for ad-hoc benchmarking via `--iterations=N --useCudaGraph --noDataTransfers`. **Recommended role for project**: fallback orchestration tool when Polygraphy is unavailable OR when calibration cache is already shipped from a reference build (e.g., the prebuilt-engine fallback per D-C7-7).
**Evidence quality**: ✅ High — canonical NVIDIA documentation; trtexec is bundled with TensorRT distributions and has been the canonical TensorRT CLI since TensorRT 5.x.
---
## Source #120 — TensorRT INT8 INT8 calibration corpus size guidance (~500-1000 images) — Jetson AGX Orin specific (L2 vendor)
**URL**: <https://nvnexus.com/tensorrt-jetson-agx-orin-optimization-guide/>
**Date accessed**: 2026-05-08
**Tier**: **L2** — vendor-aligned engineering guide (TensorRT-on-Jetson specialist content), cross-cited from official NVIDIA Developer Forum patterns
**Relevance**: Independent confirmation of the project's D-C7-1 closure: "INT8 optimization can double inference throughput on Jetson AGX Orin with minimal accuracy loss; calibration on representative input data (500-1000 images recommended)". Aligns with project's pinned 500-1500 sample range from C7 batch 1 Fact #94. Cross-cite to AGX Orin (server-class Jetson) — the project's deployment target is Orin Nano Super (smaller class), but the calibration-corpus-size guidance is governed by the model + INT8 entropy-statistics requirement, not by the Jetson SKU. **Conservative confirmation**: project's calibration corpus target of 500-1500 samples per D-C7-1 closure is sufficient by community-confirmed benchmarks.
**Evidence quality**: ⚠️ Medium-High — L2 vendor-aligned source; aligns with multiple independent confirmations including NVIDIA Developer Forum threads and the canonical TensorRT INT8 calibration documentation; project's D-C7-1 closure already pinned this range from L1 sources.
---
## Source #121 — Direct TensorRT `IBuilderConfig` + `IInt8EntropyCalibrator2` Python API (L1 official, cross-cite from C7 Source #105)
**URL**: <https://docs.nvidia.com/deeplearning/tensorrt/latest/_static/python/api/infer/Core/BuilderConfig.html> (cross-cite from C7 batch 1 Source #105 + Source #102)
**Date accessed**: 2026-05-08 (cross-cite)
**Tier**: **L1** — canonical NVIDIA TensorRT Python API documentation
**Relevance**: Already cited in C7 batch 1 Source #102 + Source #105 (mode pinning for D-C7-2). Re-cited here for the C10 D-C7-7 confirmation context: confirms direct `IBuilderConfig` + `IInt8EntropyCalibrator2` Python API as the most-flexible-but-most-engineering-cost orchestration option. Pattern: instantiate `trt.Builder(logger)` → `builder.create_network(...)` → parse ONNX via `trt.OnnxParser` → instantiate `builder.create_builder_config()` → `config.set_flag(trt.BuilderFlag.INT8)` + `config.set_flag(trt.BuilderFlag.FP16)` → assign custom `Int8EntropyCalibrator2` subclass instance to `config.int8_calibrator` → `config.max_workspace_size = 1 << 30` (1 GB per D-C7-8) → `serialized_engine = builder.build_serialized_network(network, config)` → `with open(path, 'wb') as f: f.write(serialized_engine)`. **Used in C10 only as the per-model fallback path for the reference-Jetson-prebuilt-engine generation** (D-C7-7 fallback) when Polygraphy's data-loader-script abstraction is too rigid for an unusual model (e.g., LightGlue with dynamic-shape inputs requiring a custom calibration profile).
**Evidence quality**: ✅ High — canonical NVIDIA Python API; cross-cite from existing C7 Source #105 reduces redundancy.
---
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# Source Registry — C1 — Visual / Visual-Inertial Odometry candidates
> Mode A Phase 2 — engine Step 2 (Source Tiering & Exhaustive Web Investigation).
> Critical-novelty sensitivity per Step 0.5 in `../00_question_decomposition.md`. Time windows applied:
> - **Lead-candidate / SOTA claims**: prefer sources within last 6 months; up to 18 months if older is the official authority.
> - **Library/SDK API behaviour**: must reflect the currently shipped version at search time (`context7` mandatory per lead candidate).
> - **Established baselines** (KLT, RANSAC, EKF, ORB, SIFT, GTSAM): no time window.
>
> This file replaces a section of the previous monolithic `01_source_registry.md`. See `00_summary.md` for the full category index. Investigation order is tracked in `../00_question_decomposition.md` and the cross-category Investigation Status table in `00_summary.md`.
---
### Source #43
- **Title**: VINS-Mono — A Robust and Versatile Monocular Visual-Inertial State Estimator (HKUST-Aerial-Robotics)
- **Link**: https://github.com/HKUST-Aerial-Robotics/VINS-Mono ; LICENCE: https://github.com/HKUST-Aerial-Robotics/VINS-Mono/blob/master/LICENCE
- **Tier**: L1 (canonical reference implementation; published in IEEE T-RO 2018 by Qin, Li, Shen)
- **Publication Date**: original 2018; repository last meaningful update 2024-02-25 (per GitHub commit log; 2024-05-23 simulation-data commit only)
- **Timeliness Status**: ⚠️ **Borderline.** ~24 months since the last meaningful master-branch commit at access time (2026-05-07). Established baseline that does NOT trigger Step 0.5's 18-month timeliness rejection because (a) IEEE T-RO publication is the canonical authority for the algorithm, (b) downstream forks (vins-mono-android, embedded variants) keep the algorithm class actively deployed.
- **Version Info**: No GitHub releases / tags (master-branch-only project). Stars 5,829.
- **Target Audience**: Mono+IMU VIO implementers; UAV state estimation researchers
- **Research Boundary Match**: **Full match for the candidate's pinned mode** — monocular camera + IMU producing 6-DoF metric pose. The VINS-Mono README explicitly names this configuration as primary.
- **Summary**: Optimization-based sliding-window monocular VIO. Features: efficient IMU pre-integration (Forster et al. 2017), automatic initialization, online camera-IMU extrinsic calibration, online camera-IMU temporal calibration, failure detection + recovery, loop detection (DBoW2-based), global pose graph optimization. Output is metric-scale 6-DoF pose at IMU rate (typically 100–200 Hz) with covariance from the optimization Hessian. **License: GPL-3.0 (copyleft viral)** — every binary distribution requires source disclosure for the entire linked binary; relevant for dual-use deployment if the companion image is sold or transferred to a customer.
- **Related Sub-question**: SQ3+SQ4 / C1 lead candidate
### Source #44
- **Title**: VINS-Fusion — Optimization-based multi-sensor state estimator (HKUST-Aerial-Robotics)
- **Link**: https://github.com/HKUST-Aerial-Robotics/VINS-Fusion ; LICENCE: https://github.com/HKUST-Aerial-Robotics/VINS-Fusion/blob/master/LICENCE
- **Tier**: L1 (canonical reference; superset of VINS-Mono)
- **Publication Date**: original 2019 (Qin, Cao, Pan, Shen — ICRA workshop / IROS); repository last update 2024-05-23
- **Timeliness Status**: ⚠️ **Borderline.** ~24 months since the last update at access time. Same Step-0.5 reasoning as VINS-Mono — established class.
- **Version Info**: master-branch-only. Stars 4,476. Top-ranked open-source stereo-VIO on KITTI Odometry as of January 2019.
- **Target Audience**: Multi-sensor VIO implementers (mono+IMU, stereo, stereo+IMU, +GPS fusion)
- **Research Boundary Match**: **Full match** for monocular+IMU mode. VINS-Fusion README explicitly enumerates four sensor configurations (mono+IMU, stereo, stereo+IMU, +GPS toy example).
- **Summary**: Superset of VINS-Mono adding stereo and GPS-fusion modes. Same algorithmic core (sliding-window optimization with IMU pre-integration). Online spatial + temporal camera-IMU calibration; visual loop closure; ROS Kinetic/Melodic build dependency. **License: GPL-3.0** — same dual-use distribution constraint as VINS-Mono. Independent KAIST benchmark (Source #46) found VINS-Fusion CPU mode + VINS-Fusion-imu **fail to run** on Jetson TX2 (insufficient memory and CPU); GPU-accelerated VINS-Fusion-gpu does run on TX2. Implication for project: VINS-Fusion-imu on Jetson Orin Nano Super is feasible but not certain; needs MVE.
- **Related Sub-question**: SQ3+SQ4 / C1 lead candidate
### Source #45
- **Title**: OpenVINS — An open source platform for visual-inertial navigation research (Robot Perception and Navigation Group, U. of Delaware — rpng)
- **Link**: https://github.com/rpng/open_vins ; docs: https://docs.openvins.com/ ; LICENSE: https://github.com/rpng/open_vins/blob/master/LICENSE
- **Tier**: L1 (canonical research implementation; ICRA 2020 paper Geneva, Eckenhoff, Lee, Yang, Huang)
- **Publication Date**: original 2020; latest tagged release v2.7 = 2023-06; ongoing master-branch commits through 2024–2025 (latest issue threads through Feb 2025)
- **Timeliness Status**: ✅ Currently valid (master branch active; latest tagged release ~35 months but library is in stable/maintenance mode with continued issue triage).
- **Version Info**: Stars 2,828; 30 contributors; 12 releases. v2.7 is the current tagged stable.
- **Target Audience**: MSCKF/EKF VIO implementers; researchers needing a reference MSCKF
- **Research Boundary Match**: **Full match** for monocular+IMU mode. OpenVINS supports mono, stereo, multi-camera (1–N cameras) + IMU; mono is a documented first-class mode.
- **Summary**: Modular MSCKF (Multi-State Constraint Kalman Filter) implementation built around an Extended Kalman filter that fuses inertial state with sparse visual feature tracks via the sliding-window MSCKF formulation (Mourikis & Roumeliotis 2007). Supports SLAM features (in-state landmarks) plus pure MSCKF features (out-of-state). ROS1 + ROS2 (Humble) builds documented; Jetson Orin Nano Dev Kit + JetPack 6 + ROS 2 Humble compilation **confirmed working** by community contributors (rpng/open_vins issue #421, fdcl-gwu/openvins_jetson_realsense Nov 2025 setup guide). **License: GPL-3.0** — same dual-use distribution constraint. Reported latency ~270 ms on Xavier NX (4-core, ARM, 40% CPU usage) per issue #164; needs Jetson-Orin-Nano-Super MVE for production budget verification.
- **Related Sub-question**: SQ3+SQ4 / C1 lead candidate
### Source #46
- **Title**: Run Your Visual-Inertial Odometry on NVIDIA Jetson — Benchmark Tests on a Micro Aerial Vehicle (Jeon, Jung, Lee, Choi, Myung — KAIST)
- **Link**: https://arxiv.org/abs/2103.01655 ; KAIST VIO dataset: https://github.com/zinuok/kaistviodataset
- **Tier**: L1 (peer-reviewed conference, IROS-track preprint with public dataset)
- **Publication Date**: arXiv 2021-03-02
- **Timeliness Status**: ⚠️ Older than the 18-month Critical-novelty window, but **uniquely authoritative** for the specific question "do these VIO algorithms run on a Jetson?"; the included algorithms (VINS-Mono, VINS-Fusion, ROVIO, ALVIO, Stereo-MSCKF, Kimera, ORB-SLAM2-stereo) are all classical baselines whose runtime characteristics on ARM CPUs have not changed materially. Jetson hardware comparison (TX2 / Xavier NX / AGX Xavier) does NOT include Orin Nano — must extrapolate.
- **Version Info**: Conference paper.
- **Target Audience**: UAV state-estimation engineers picking a VIO for a Jetson companion
- **Research Boundary Match**: **Strong match for the question**, partial for the hardware (no Orin Nano). KAIST VIO dataset is indoor mocap, not UAV-aerial-nadir — the *latency / CPU / memory* numbers transfer; the *accuracy* numbers do not transfer to our domain.
- **Summary**: Comprehensive benchmark of 9 algorithms on TX2, Xavier NX, AGX Xavier: VINS-Mono, VINS-Fusion (CPU), VINS-Fusion-gpu, VINS-Fusion-imu, ROVIO, Stereo-MSCKF, ALVIO, Kimera, ORB-SLAM2-stereo. **Hard findings**: (a) on TX2, **VINS-Fusion (CPU) and VINS-Fusion-imu fail to run** due to insufficient memory and CPU performance — VINS-Fusion-gpu does run; (b) all algorithms except ROVIO show >100% CPU usage (multi-core utilisation, OK for our 6-core Orin Nano A78AE); (c) Kimera has the highest memory usage among VIO methods (numerous computations per keyframe), failure-prone on Xavier NX-class memory; (d) Stereo-MSCKF has the lowest memory among stereo VIOs; (e) ROVIO has the lowest CPU usage owing to its patch-tracking formulation. **Implication for project**: Jetson Orin Nano Super (8 GB shared, 6-core A78AE, Ampere GPU, 67 TOPS sparse INT8) is between Xavier NX and AGX Xavier in CPU performance and memory; algorithms passing on Xavier NX should pass on Orin Nano Super, but VINS-Fusion-imu's TX2 failure is a yellow-flag for memory pressure under co-resident C2/C3/C5 modules.
- **Related Sub-question**: SQ3+SQ4 / C1 (VINS-Mono / VINS-Fusion / OpenVINS / Kimera / Stereo-MSCKF / ROVIO Jetson runtime evidence), SQ5 (resource-budget failure modes)
### Source #47
- **Title**: OKVIS2 — Realtime Scalable Visual-Inertial SLAM with Loop Closure (Leutenegger, ETH/Imperial/TUM Smart Robotics Lab)
- **Link**: https://github.com/ethz-mrl/okvis2 ; arXiv: https://arxiv.org/abs/2202.09199 ; LICENSE: https://github.com/ethz-mrl/okvis2/blob/main/LICENSE
- **Tier**: L1 (canonical implementation; arXiv 2022 by paper author)
- **Publication Date**: original arXiv 2022; OKVIS2-X T-RO 2025 successor (Boche, Jung, Laina, Leutenegger — IEEE T-RO 2025, vol 41 pp 6064–6083, DOI 10.1109/TRO.2025.3619051; arXiv 2510.04612, Oct 2025). Repository last push 2026-03-17 (ethz-mrl/OKVIS2-X).
- **Timeliness Status**: ✅ **Current.** Active development through 2026; OKVIS2-X is the most recent published VI-SLAM system in this class.
- **Version Info**: ethz-mrl/okvis2 (core) and ethz-mrl/OKVIS2-X (multi-sensor extension with optional GNSS / LiDAR / dense depth).
- **Target Audience**: Factor-graph VI-SLAM implementers; mid-large-scale loop-closure use cases
- **Research Boundary Match**: **Full match** for monocular+IMU mode. OKVIS2 README + paper explicitly support mono and multi-camera VI configurations. OKVIS2-X adds GNSS fusion (relevant: VINS-Fusion-style GPS-when-available drop-in IS the project's eventual posture in non-spoofed regions).
- **Summary**: Factor-graph VI-SLAM with bounded-size optimization. Innovation: pose-graph edges from marginalised observations can be "seamlessly turned back into observations" upon loop closure, reviving old landmarks and reprojection errors. Includes lightweight CNN segmentation for dynamic-region removal. OKVIS2-X (2025) generalises the core to fuse multi-camera + IMU + optional GNSS + LiDAR/depth — directly aligned with project's "VIO that may opportunistically fuse a non-spoofed GPS update" pattern and AC-NEW-2's spoof-promotion path. **License: 3-clause BSD (permissive)** — no copyleft / dual-use distribution friction. Note: GitHub UI shows "Other (NOASSERTION)" because of the standard BSD clause language pattern; the LICENSE file is canonical 3-clause BSD.
- **Related Sub-question**: SQ3+SQ4 / C1 lead candidate (factor-graph + permissive license + active maintenance)
### Source #48
- **Title**: OKVIS2-X: Open Keyframe-based Visual-Inertial SLAM Configurable with Dense Depth or LiDAR, and GNSS (Boche, Jung, Laina, Leutenegger — TUM / ETH Zurich Smart Robotics Lab)
- **Link**: https://github.com/ethz-mrl/OKVIS2-X ; arXiv: https://arxiv.org/abs/2510.04612 ; IEEE T-RO 2025 vol 41 pp 6064–6083 DOI 10.1109/TRO.2025.3619051
- **Tier**: L1 (peer-reviewed IEEE Transactions on Robotics, Special Issue Visual SLAM 2025)
- **Publication Date**: arXiv 2025-10-04; T-RO 2025 vol 41
- **Timeliness Status**: ✅ Current (within 6-month Critical-novelty window)
- **Version Info**: 295 stars; 38 forks; 2 contributors; created 2025-09-23, last push 2026-03-17. License: NOASSERTION on GitHub UI; per-paper license follows ethz-mrl convention (BSD-3 derived).
- **Target Audience**: Multi-sensor SLAM researchers; large-scale VI-SLAM with optional GNSS/LiDAR
- **Research Boundary Match**: **Strong match** — extends OKVIS2 monocular+IMU mode with optional GNSS fusion (Visual-Inertial SLAM with Tightly-Coupled Dropout-Tolerant GPS Fusion lineage from IROS 2022). Project's `MAV_CMD_SET_EKF_SOURCE_SET` switch + companion-side spoof-detection conceptually mirrors OKVIS2-X's "GPS as drop-out-tolerant signal".
- **Summary**: Non-trivial extension of OKVIS2; submap-based volumetric occupancy mapping. Demonstrates that the OKVIS2 factor-graph backbone can absorb spoofing-aware GPS without re-architecting. Useful as architectural template for project's C5 estimator + C8 adapter integration. License: same as OKVIS2 (BSD-3-derived). Two named contributors (bochsim, SebsBarbas) actively pushing through Mar 2026.
- **Related Sub-question**: SQ3+SQ4 / C1 (OKVIS2 lineage; VI-SLAM with optional GPS/LiDAR), SQ8 (GPS-fusion dropout-tolerant lineage)
### Source #49
- **Title**: Kimera-VIO — Visual Inertial Odometry with SLAM capabilities and 3D Mesh generation (MIT-SPARK)
- **Link**: https://github.com/MIT-SPARK/Kimera-VIO ; LICENSE.BSD: https://github.com/MIT-SPARK/Kimera-VIO/blob/master/LICENSE.BSD
- **Tier**: L1 (canonical implementation by MIT SPARK Lab)
- **Publication Date**: original 2020 (Rosinol, Abate, Chang, Carlone — ICRA 2020); ongoing development through 2024–2025 issue threads (Dec 2024 / Feb 2025 ROS2 / mono-inertial discussion).
- **Timeliness Status**: ✅ Active maintenance (recent issues / PRs through 2025).
- **Version Info**: master-branch-only; LICENSE.BSD = BSD 2-Clause "Simplified".
- **Target Audience**: VI-SLAM + mesh-mapping researchers
- **Research Boundary Match**: **Partial.** Stereo+IMU is the primary supported configuration; mono+IMU is **optional but documented**. Kimera also produces 3D mesh and high-level semantic labels (relevant to neither C1 nor the project's bandwidth budget — overhead).
- **Summary**: Frontend (image processing + IMU pre-integration) + Backend (factor-graph optimization in iSAM2 or GTSAM) + Mesher + Pose-Graph-Optimizer. **License: BSD 2-Clause (permissive)** — no dual-use distribution friction. **Penalty for project**: Source #46 KAIST benchmark found Kimera has highest memory usage among the VIOs tested (numerous computations per keyframe), and Kimera failed to fit on Xavier-NX-class memory under multi-process load. Mesh + semantic features are unused by the project — Kimera's overhead is unjustified vs OKVIS2 / OpenVINS for the project's narrow C1 mandate. **Status**: viable secondary fallback if OKVIS2 / VINS-Mono runtime issues arise; not a lead candidate due to overhead misfit.
- **Related Sub-question**: SQ3+SQ4 / C1 secondary candidate (BSD-permissive but resource-heavy)
### Source #50
- **Title**: DROID-SLAM — Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras (princeton-vl, Teed & Deng)
- **Link**: https://github.com/princeton-vl/droid-slam ; arXiv: https://arxiv.org/abs/2108.10869 ; NeurIPS 2021
- **Tier**: L1 (canonical reference)
- **Publication Date**: NeurIPS 2021; repository latest tagged baseline.
- **Timeliness Status**: ✅ Foundational reference; DPV-SLAM (Source #51) is the lighter successor.
- **Version Info**: master-branch-only.
- **Target Audience**: Deep-learning-based VO/VSLAM researchers
- **Research Boundary Match**: **Disqualified by hardware budget.** Inference requires ≥11 GB GPU VRAM per official README; project budget is 8 GB **shared CPU+GPU** on Jetson Orin Nano Super, leaving <8 GB for VO + VPR + matcher + estimator + cache co-resident. DROID-SLAM is also **monocular VO/SLAM, not VIO** — no native IMU fusion; metric scale recovery requires external scale alignment.
- **Summary**: Recurrent dense bundle adjustment over a complete history of camera poses. State-of-the-art accuracy on TartanAir / EuRoC / TUM-RGBD at the cost of GPU memory. **Disqualified outright for C1 lead** by AC-4.2 (≤8 GB shared RAM) and the lack of IMU fusion that would require an additional ESKF/UKF wrapping. Kept as **reference baseline** to be cited as "what we cannot afford" in `solution_draft01`.
- **Related Sub-question**: SQ3+SQ4 / C1 disqualified candidate
### Source #51
- **Title**: DPVO — Deep Patch Visual Odometry (princeton-vl, Teed, Lipson, Deng) + DPV-SLAM (Lipson, Teed, Deng — ECCV 2024)
- **Link**: https://github.com/princeton-vl/DPVO ; LICENSE: https://github.com/princeton-vl/DPVO/blob/main/LICENSE ; ECCV 2024 paper: https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00272.pdf
- **Tier**: L1 (canonical implementation; NeurIPS 2023 + ECCV 2024)
- **Publication Date**: NeurIPS 2023 (DPVO); ECCV 2024 (DPV-SLAM); repository last update 2024-10-12.
- **Timeliness Status**: ⚠️ Borderline. ~19 months since last code update; ECCV-2024 publication of DPV-SLAM keeps the algorithm class within the 6-month claim window for the SLAM successor.
- **Version Info**: 989 stars; primary languages C++ / Python / CUDA. **License: MIT (permissive)** — no dual-use distribution friction.
- **Target Audience**: Deep-learning VO/SLAM with reduced memory footprint
- **Research Boundary Match**: **Partial.** DPVO is **monocular VO only — no IMU fusion**. Output pose is in arbitrary scale (no metric scale recovery). To be a viable C1 candidate the project must wrap DPVO with an external IMU+scale-fusion stage (loosely-coupled ESKF / VIO-fusion module). This makes DPVO **not a drop-in C1** like VINS-Mono / OpenVINS / OKVIS2; it is a **VO module that needs a separate VIO wrapper**.
- **Summary**: Sparse patch tracking + differentiable bundle adjustment back end. Outperforms DROID-SLAM on TartanAir / EuRoC ATE while using ~1/3 of DROID-SLAM's GPU memory (DROID-SLAM: 8.7 GB VO mode vs DPVO: ~3 GB). DPV-SLAM (Lipson, Teed, Deng — ECCV 2024) adds full SLAM capability with 4–5 GB GPU usage. **Jetson runtime evidence**: indirect via DPVO-QAT++ (Source #52) — peak reserved memory 1.02 GB on RTX 4060 (8 GB) after INT8 fake-quant + custom CUDA kernel fusion; not directly tested on Jetson Orin Nano. **Status for C1**: pure-VO candidate (must be paired with separate IMU integration to deliver metric scale + attitude); would not satisfy "monocular VIO" gate alone, but viable as the *VO half* of a hybrid C1+C5 design.
- **Related Sub-question**: SQ3+SQ4 / C1 conditional candidate (VO not VIO; needs external IMU wrapper)
### Source #52
- **Title**: DPVO-QAT++: Heterogeneous QAT and CUDA Kernel Fusion for High-Performance Deep Patch Visual Odometry (Cheng Liao)
- **Link**: https://arxiv.org/abs/2511.12653 ; project HTML: https://arxiv.org/html/2511.12653
- **Tier**: L2 (single-author preprint, code partially released; no peer-review yet)
- **Publication Date**: arXiv 2025-11-16 (within 6-month Critical-novelty window)
- **Timeliness Status**: ✅ Current
- **Version Info**: arXiv preprint; code & weights released for QAT-only and fused-CUDA variants.
- **Target Audience**: Embedded-platform DPVO deployers
- **Research Boundary Match**: **Partial.** Hardware tested = RTX 4060 (8 GB) + Intel Core Ultra 5-125H + 32 GB RAM — desktop GPU, NOT Jetson Orin Nano. Direct extrapolation requires Jetson MVE; Orin Nano Super's Ampere GPU is architecturally similar but smaller than RTX 4060.
- **Summary**: Quantization-Aware Training framework for DPVO with fused CUDA kernels. Reduces peak GPU memory from 1.94 GB → 1.02 GB (-47%) on a representative TartanAir sequence; +34.6% median FPS on TartanAir, +26.7% on EuRoC; -22.8 ms / -19.7 ms median P99 tail latency on TartanAir / EuRoC respectively. Heterogeneous precision: front-end pseudo-quantization (FP16/FP32 with INT8 simulation) + FP32 back-end geometric solver. **Implication for project**: shows DPVO has a documented Jetson-suitable footprint **path** but not a Jetson-Orin-Nano measurement. ATE accuracy comparable to baseline DPVO across 32 TartanAir + 11 EuRoC validation sequences. Notable: requires a teacher-student distillation training pipeline before deployment — adds operational complexity vs classical VINS-* / OpenVINS / OKVIS2.
- **Related Sub-question**: SQ3+SQ4 / C1 supporting evidence for DPVO embedded feasibility
### Source #53
- **Title**: Pure VO baseline — KLT optical flow + 5-point essential matrix or homography RANSAC (OpenCV reference)
- **Link**: https://docs.opencv.org/4.x/d4/dee/tutorial_optical_flow.html ; representative public implementation: https://github.com/alishobeiri/Monocular-Video-Odometery (MIT, 2018) ; tutorial reference: https://zxh.me/posts/2022-12-19-monocular-visual-odometry/
- **Tier**: L1 (OpenCV official documentation) + L2 (representative public implementations)
- **Publication Date**: OpenCV docs continuously updated; tutorial 2022-12; reference implementation 2018 (algorithmic class is foundational, no time window per Step 0.5)
- **Timeliness Status**: ✅ Foundational baseline (no time window).
- **Version Info**: OpenCV `cv::calcOpticalFlowPyrLK` (KLT) + `cv::findEssentialMat` (5-point Nister) or `cv::findHomography` with RANSAC.
- **Target Audience**: Implementers needing a transparent low-complexity fallback
- **Research Boundary Match**: **Full match for the simple-baseline candidate.** Suits planar nadir-down UAV at altitude (Ukrainian steppe is ~planar at 1 km AGL — homography is geometrically appropriate; for non-planar relief the essential matrix path is more appropriate but adds scale-recovery work).
- **Summary**: Established classical pipeline: Shi-Tomasi or FAST corner detection → KLT pyramidal optical flow tracking → 5-point essential matrix or homography RANSAC → relative pose with arbitrary scale (must be metric-scale-aligned via IMU integration externally). Reference implementations widely available in OpenCV samples and pedagogical repos. **Status**: candidate as the project's `Simple baseline / known-runnable / known-failure-mode` C1 option per Component Option Breadth rule. Not a lead, but mandatory fallback presence per the research engine's "include at least one simple baseline" rule.
- **Related Sub-question**: SQ3+SQ4 / C1 simple-baseline candidate
### Source #54
- **Title**: OpenVINS — `context7` per-mode capability lookup (`/rpng/open_vins`, master)
- **Link**: context7 query against `/rpng/open_vins`, accessed 2026-05-08; canonical doc references returned: `https://github.com/rpng/open_vins/blob/master/docs/gs-tutorial.dox`, `https://github.com/rpng/open_vins/blob/master/docs/gs-datasets.dox`, `https://github.com/rpng/open_vins/blob/master/docs/gs-calibration.dox`, `https://github.com/rpng/open_vins/blob/master/docs/propagation-analytical.dox`
- **Tier**: L1 (project-official documentation reachable via the project's documentation generator)
- **Publication Date**: live docs (master, accessed 2026-05-08)
- **Timeliness Status**: ✅ Within Critical-novelty window (active master + community evidence through 2025–2026)
- **Version Info**: master HEAD at access time (no tagged release for ROS 2 path; ROS 1 / ROS 2 build paths both documented)
- **Target Audience**: System architects + C1 implementer
- **Research Boundary Match**: **Full match** for monocular + IMU mode. The `subscribe.launch.py` ROS 2 launch script (and its ROS 1 sibling) declare `use_stereo` and `max_cameras` as DeclareLaunchArguments — setting `use_stereo:=false max_cameras:=1` selects monocular operation; `config:=` selects an estimator-config directory (`euroc_mav`, `tum_vi`, `rpng_aruco`, …). KALIBR + RPNG IMU intrinsic calibration models are both documented in `propagation-analytical.dox` with the corresponding state-vector composition.
- **Summary**: Confirms documentary evidence for OpenVINS' three sensor configurations exposed at the launch layer (mono / stereo / multi-camera), all with IMU mandatory; confirms the project's pinned mode (`use_stereo:=false max_cameras:=1`) is a first-class launch configuration that requires no source patch. Confirms that estimator config files in `ov_msckf/config/<dataset>/estimator_config.yaml` are the parameter-tuning surface and that supported IMU intrinsic models include both KALIBR and RPNG. **Open**: `context7` Disqualifier-Probe query did not surface explicit per-mode latency/memory limits or sub-20-Hz validation evidence; those constraints carry into the Jetson-Orin-Nano-Super hardware MVE (D-C1-2 deferred phase).
- **Related Sub-question**: SQ3+SQ4 / C1 — OpenVINS per-mode API capability verification (Mandatory `context7` lookup per Per-Mode API Capability Verification rule)
### Source #55
- **Title**: VINS-Mono — official README + VINS-Fusion `context7` per-mode capability lookup (`/hkust-aerial-robotics/vins-fusion`, master) [cross-source documentary evidence for the mono+IMU mode shared with VINS-Mono]
- **Link**: VINS-Mono README — https://raw.githubusercontent.com/HKUST-Aerial-Robotics/VINS-Mono/master/README.md (accessed 2026-05-08); VINS-Fusion docs — context7 query against `/hkust-aerial-robotics/vins-fusion`, accessed 2026-05-08, canonical reference returned: https://github.com/hkust-aerial-robotics/vins-fusion/blob/master/README.md
- **Tier**: L1 (project-official READMEs of both repos)
- **Publication Date**: VINS-Mono README — 2019-01-11 last major revision (master-branch only, no tagged releases); VINS-Fusion docs — live (master, accessed 2026-05-08)
- **Timeliness Status**: ⚠️ borderline (per Step 0.5 timeliness — VINS-Mono master last meaningful commit 2024-02-25 / 2024-05-23; older than the 18-month preferred window for live API behaviour, but the algorithm class remains the canonical mono+IMU sliding-window VIO referenced by 2025 community work — see Fact #36)
- **Version Info**: VINS-Mono master HEAD; depends on Ceres v1.14.0 (versions ≥2.0.0 have build issues per README). VINS-Fusion master HEAD has `euroc_mono_imu_config.yaml` as a first-class config.
- **Target Audience**: System architects + C1 implementer
- **Research Boundary Match**: **Full match** for the project's pinned mode (mono + IMU). VINS-Mono is single-mode by construction — "real-time SLAM framework for **Monocular Visual-Inertial Systems**" — the project's pinned mode is the only mode the project will use the binary in. VINS-Fusion `euroc_mono_imu_config.yaml` is the documentary cross-source evidence that the algorithmic mono+IMU path remains a first-class configuration in the same authors' active fork.
- **Summary**: Confirms VINS-Mono = monocular + IMU only (single mode); ROS Kinetic / Ubuntu 16.04 reference build; pinhole + MEI camera models supported; rolling-shutter support with calibrated reprojection error <0.5 px; online camera-IMU extrinsic + temporal calibration; loop closure via DBoW2; pose-graph reuse and map merge supported. **Critical recommended-input bound**: README §5.1 — *"The image should exceed 20Hz and IMU should exceed 100Hz."* — the project's nav cam target is 3 fps; this is a documentary signal that VIO performance below the recommended frame rate is not validated by the upstream authors. License: GPLv3 (confirmed in README §8). **Cross-source note**: VINS-Fusion `euroc_mono_imu_config.yaml` is named explicitly in `context7` results and uses the same algorithmic core; treat as evidence for VINS-Mono's mono+IMU mode while honouring the per-mode rule that VINS-Fusion's mono+IMU mode is a separately-cataloged candidate (Fact #29).
- **Related Sub-question**: SQ3+SQ4 / C1 — VINS-Mono per-mode API capability verification (Mandatory `context7` lookup per Per-Mode API Capability Verification rule, with cross-source documentary evidence from VINS-Fusion since VINS-Mono itself is not indexed in `context7`)
### Source #56
- **Title**: OKVIS2 — official README (`smartroboticslab/okvis2`, main)
- **Link**: https://raw.githubusercontent.com/smartroboticslab/okvis2/main/README.md (accessed 2026-05-08); papers cited in README: arXiv:2202.09199 (Leutenegger 2022), IJRR 2015, RSS 2013
- **Tier**: L1 (project-official README; arXiv canonical paper)
- **Publication Date**: README live; canonical paper 2022-02; OKVIS2 master last push within the Critical-novelty window (per Fact #36 timeliness audit, OKVIS2-X 2026-03-17 push confirms active)
- **Timeliness Status**: ✅ Fully within Critical-novelty window
- **Version Info**: OKVIS2 main HEAD; cmake build with optional ROS 2 wrapping (`BUILD_ROS2=ON`); optional sky-segmentation CNN via LibTorch (`USE_NN=OFF` to disable)
- **Target Audience**: System architects + C1 implementer + Step-7.5 reviewer
- **Research Boundary Match**: **Full match** for the project's pinned mode (mono + IMU). README confirms multi-camera support (camera frames `C_i` for the i-th camera) plus IMU mandatory; mono operation is a documented configuration via the example apps (`okvis_app_synchronous`, `okvis_app_realsense`). OKVIS2-X is the GNSS-fusion extension (T-RO 2025) that aligns architecturally with the project's spoof-promotion path.
- **Summary**: Confirms OKVIS2 = keyframe-based VI-SLAM (factor-graph backbone with loop closure); BSD-3 license (no copyleft); coordinate-frame contract (`W` world, `C_i` cameras, `S` IMU, `B` body); state representation (`T_WS` pose + velocity + gyro/accel biases); two-callback API (`setOptimisedGraphCallback` for batch updates incl. loop closure + `setImuCallback` for high-rate prediction). Calibration prerequisites: camera intrinsics + camera-IMU extrinsics + IMU noise parameters + tight time sync (Kalibr toolchain explicitly recommended). Optional LibTorch sky-segmentation CNN can be disabled (`USE_NN=OFF`) to remove a major Jetson dependency. ROS 2 build path (`BUILD_ROS2=ON`) with `okvis_node_realsense.launch.xml`, `okvis_node_realsense_publisher.launch.xml`, `okvis_node_subscriber.launch.xml`, `okvis_node_synchronous.launch.xml`. **Health warning** in README: poor calibration → poor results; this is shared with all VI candidates but is more strongly emphasised in OKVIS2 docs. **Open**: README does not state explicit minimum frame rate (cf. VINS-Mono's documented 20 Hz minimum) — keyframe-based selection generally tolerates lower input frame rates than sliding-window optimisation; this needs explicit Jetson MVE validation at 3 fps.
- **Related Sub-question**: SQ3+SQ4 / C1 — OKVIS2 per-mode API capability verification (Mandatory `context7` lookup per Per-Mode API Capability Verification rule, with WebFetch fallback to official README since `context7` returned no match)
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# Source Registry — C4 — Pose estimation (PnP + RANSAC + LM) candidates
> Mode A Phase 2 — engine Step 2 (Source Tiering & Exhaustive Web Investigation).
> Critical-novelty sensitivity per Step 0.5 in `../00_question_decomposition.md`. Time windows applied:
> - **Lead-candidate / SOTA claims**: prefer sources within last 6 months; up to 18 months if older is the official authority.
> - **Library/SDK API behaviour**: must reflect the currently shipped version at search time (`context7` mandatory per lead candidate).
> - **Established baselines** (KLT, RANSAC, EKF, ORB, SIFT, GTSAM): no time window.
>
> This file replaces a section of the previous monolithic `01_source_registry.md`. See `00_summary.md` for the full category index. Investigation order is tracked in `../00_question_decomposition.md` and the cross-category Investigation Status table in `00_summary.md`.
---
### Source #82
- **Title**: OpenCV canonical implementation — `opencv/opencv` (Open Source Computer Vision Library) GitHub repository metadata via GitHub API + LICENSE — **Apache-2.0** (`license.spdx_id: "Apache-2.0"`); 87385 stars + 56554 forks + 2606 subscribers + 2732 open issues; created 2012-07-19; **last pushed 2026-05-08T07:00:03Z = TODAY at access time** (daily-active maintenance); default branch `4.x`; size 555 GB; topics include `c-plus-plus, computer-vision, deep-learning, image-processing, opencv`
- **Link**: GitHub API metadata https://api.github.com/repos/opencv/opencv (accessed 2026-05-08; `license.spdx_id: "Apache-2.0"` confirmed); canonical repo https://github.com/opencv/opencv ; canonical website https://opencv.org ; LICENSE file https://raw.githubusercontent.com/opencv/opencv/4.x/LICENSE (Apache License 2.0 standard text)
- **Tier**: L1 (project-official codebase by the OpenCV organization; canonical reference computer-vision library used by every modern computer-vision deployment as the de-facto industry-standard classical-CV foundation; cited by every C-row component's deployment guide; canonical solvePnPRansac is the industry-standard reference RANSAC-PnP implementation that every modern alternative [OpenGV, GTSAM-PnP, Theia, Ceres-only] compares against in its own documentation)
- **Publication Date**: original 2000 (Intel) → open-source release 2006 (Willow Garage) → OpenCV.org foundation 2020 → canonical 4.x branch active continuous development; access date 2026-05-08; daily commits to `4.x` branch
- **Timeliness Status**: ✅ Within Established-baseline-reference window (2000+ — established competitive ground for classical computer-vision + RANSAC-PnP reference; Established-competitive-mandatory-baseline exemption applies — `cv::solvePnPRansac` is the **canonical RANSAC-PnP reference baseline** that defines the mandatory-simple-baseline role for the C4 row per the engine Component Option Breadth rule, structurally analogous to NetVLAD's role in C2 row + SuperGlue+SuperPoint's role in C3 row)
- **Version Info**: 4.14.0-pre at access time (default branch `4.x` = next-major-release rolling-development branch; current stable release 4.10.0 from late 2025 at access date — 4.x is the project's pinned major version per Source #83 documentation footer "Generated on Fri May 8 2026 04:21:44 for OpenCV by 1.12.0"); JetPack 6 ships canonical `libopencv_calib3d.so` for ARM Cortex-A78AE = the project's pinned Jetson Orin Nano Super deployment runtime
- **Target Audience**: System architects + C4 implementer + Step-7.5 reviewer + license-posture decision-maker (D-C1-1 — clean Apache-2.0) + C7 (Jetson runtime) implementer (canonical OpenCV is shipped with JetPack 6 distribution)
- **Research Boundary Match**: **Full match** for the project's pinned C4 mandatory-simple-baseline mode (per-frame pose-from-correspondences via classical RANSAC-PnP with paired Levenberg-Marquardt refinement). The canonical `opencv/opencv` library ships everything needed for C4 deployment: `cv::solvePnPRansac` two function signatures (classical + USAC variant), nine `SolvePnPMethod` enum values, paired `cv::solvePnPRefineLM` LM refinement + alternate `cv::solvePnPRefineVVS` Gauss-Newton SO(3) refinement, paired `cv::solvePnPGeneric` for multi-solution + per-solution reprojection-error reporting, `cv::projectPoints` Jacobian for D-C4-2 post-hoc covariance recovery. **N/A for the project's domain caveat** — OpenCV solvePnPRansac is a classical algorithm with no training data; D-C2-1 retrain decision is irrelevant for OpenCV solvePnPRansac
- **Summary**: OpenCV is the canonical industry-standard open-source computer vision library; the calib3d module ships `cv::solvePnPRansac` as the canonical RANSAC-PnP reference implementation. **CRITICAL LICENSE FINDING**: Apache-2.0 (`license.spdx_id: "Apache-2.0"`) — permissive, BSD/permissive license track on the C4 mandatory-simple-baseline; **deployment-ready under every D-C1-1 license-posture choice** with the cleanest license-compliance story tied with cvg/LightGlue + DISK + XFeat. **Daily-active maintenance**: last pushed 2026-05-08 (TODAY at access time) — among the most actively-maintained C-row references across all components evaluated. **Industry-standard reference status**: 87385 stars + 56554 forks + 2606 subscribers — the dominant industry-standard reference implementation that every modern C4 alternative (OpenGV, GTSAM-PnP, Theia, Ceres-only) compares against in its own documentation. **JetPack 6 canonical distribution**: canonical OpenCV is shipped with JetPack 6 distribution, providing zero-effort deployment for the project's pinned Jetson Orin Nano Super runtime
- **Related Sub-question**: SQ3+SQ4 / C4 — OpenCV solvePnPRansac per-mode API capability verification (Mandatory `context7` lookup MCP-validation-error + WebFetch fallback PASS per Per-Mode rule item 2; cross-validated against canonical GitHub API license metadata WebFetch + canonical OpenCV calib3d module documentation [Source #83]); **D-C1-1 license-posture compliance**: clean Apache-2.0 throughout; **Mandatory-simple-baseline role per engine Component Option Breadth rule** confirmed; **JetPack 6 canonical distribution** documented
### Source #83
- **Title**: OpenCV 4.x calib3d module canonical documentation — group `cv::calib3d` (Camera Calibration and 3D Reconstruction) at `https://docs.opencv.org/4.x/d9/d0c/group__calib3d.html` + Perspective-n-Point (PnP) pose computation tutorial at `https://docs.opencv.org/4.x/d5/d1f/calib3d_solvePnP.html`; `cv::solvePnPRansac` two function signatures (classical with `iterationsCount=100, reprojectionError=8.0, confidence=0.99, flags=SOLVEPNP_ITERATIVE` defaults + USAC variant with `UsacParams` and `cameraMatrix` as `InputOutputArray` for focal-length refinement); Python bindings; `cv::SolvePnPMethod` enum 9 values; `cv::solvePnPRefineLM` + alternate `cv::solvePnPRefineVVS`; `cv::solvePnPGeneric` for multi-solution + per-solution reprojection-error reporting; USAC RANSAC-method enum 7 modern variants
- **Link**: calib3d module documentation https://docs.opencv.org/4.x/d9/d0c/group__calib3d.html (accessed 2026-05-08); PnP tutorial page https://docs.opencv.org/4.x/d5/d1f/calib3d_solvePnP.html (accessed 2026-05-08); both pages footer-stamped "Generated on Fri May 8 2026 04:21:44 for OpenCV by 1.12.0" — fresh canonical documentation at the project's evaluation time
- **Tier**: L1 (canonical project-official documentation by the OpenCV organization; the canonical reference for the `cv::solvePnPRansac` function signature, parameter defaults, paired refinement variants, minimal-solver enum values, and structural caveats; auto-generated by Doxygen 1.12.0 from canonical opencv/opencv source code at `4.x` branch)
- **Publication Date**: rolling Doxygen documentation auto-regenerated on every push to `4.x` branch; access date 2026-05-08 04:21:44 page-generation timestamp
- **Timeliness Status**: ✅ Within Established-baseline-reference window (rolling Doxygen documentation; the canonical reference for `cv::solvePnPRansac` API surface at the project's evaluation time)
- **Version Info**: 4.14.0-pre at access time (default branch `4.x` = next-major-release rolling-development branch). **Mode-enumeration query (1/3) — context7 MCP-validation-error + WebFetch fallback PASS** — `context7 resolve-library-id` returned MCP validation errors (parameter schema mismatch on both `query` and `libraryName` argument names — context7 server expects different argument shape than provided); per Per-Mode API Capability Verification rule item 2, fall-back to official-docs WebFetch on the canonical OpenCV calib3d module documentation + PnP tutorial page was used (this Source #83). **Nine `SolvePnPMethod` enum values documented** at line 243 of the calib3d.html: `SOLVEPNP_ITERATIVE=0` (default; iterative LM-based on top of EPNP minimal-solver result), `SOLVEPNP_EPNP=1` (Efficient Perspective-n-Point [Lepetit et al. IJCV 2009]; canonical default for ≥4 non-planar correspondences), `SOLVEPNP_P3P=2` (Revisiting the P3P Problem [Ding et al. 2023]; minimal-solver for exactly-3 correspondences with up to 4 solutions), `SOLVEPNP_DLS=3` (**BROKEN per explicit docstring "Broken implementation. Using this flag will fallback to EPnP"** — Direct Least-Squares method [Hesch & Roumeliotis 2011] originally), `SOLVEPNP_UPNP=4` (**BROKEN per explicit docstring "Broken implementation. Using this flag will fallback to EPnP"** — Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation [Penate-Sanchez et al. 2013] originally), `SOLVEPNP_AP3P=5` (Algebraic P3P [Ke & Roumeliotis CVPR 2017]), `SOLVEPNP_IPPE=6` (Infinitesimal Plane-Based Pose Estimation [Collins & Bartoli ECCV 2014]; **planar-only — object points must be coplanar — directly relevant to project's D-C4-1 = 4-DoF flat-earth lift recommendation**), `SOLVEPNP_IPPE_SQUARE=7` (special-case IPPE for marker pose with 4 fixed-pattern points), `SOLVEPNP_SQPNP=8` (SQPnP: A Consistently Fast and Globally Optimal Solution [Terzakis & Lourakis ECCV 2020]; **modern globally-optimal alternate without planarity restriction — second-recommended fallback if D-C4-1 chooses 6-DoF DSM lift**). **`cv::solvePnPRansac` classical signature** at line 3211 of calib3d.html: `bool solvePnPRansac(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess=false, int iterationsCount=100, float reprojectionError=8.0, double confidence=0.99, OutputArray inliers=noArray(), int flags=SOLVEPNP_ITERATIVE)` — Python `cv.solvePnPRansac(objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, iterationsCount[, reprojectionError[, confidence[, inliers[, flags]]]]]]]]) -> retval, rvec, tvec, inliers`. **`cv::solvePnPRansac` USAC variant signature** at line 3261: `bool solvePnPRansac(InputArray objectPoints, InputArray imagePoints, InputOutputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, OutputArray inliers, const UsacParams& params=UsacParams())` — Python `cv.solvePnPRansac(objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, inliers[, params]]]]) -> retval, cameraMatrix, rvec, tvec, inliers`; note `cameraMatrix` is `InputOutputArray` in the USAC variant, allowing focal-length refinement during the RANSAC loop. **`cv::solvePnPRefineLM`** at line 3268: canonical default `TermCriteria(EPS+COUNT, 20, FLT_EPSILON)`. **CRITICAL CAVEAT** documented at the PnP-tutorial page: "the current implementation computes the rotation update as a perturbation and not on SO(3)" — minor structural caveat; alternate `cv::solvePnPRefineVVS` at line 3289 uses Gauss-Newton with rotation update via exponential map on SO(3) (preferred for high-accuracy aerial pose-from-correspondences). **`cv::solvePnPGeneric`** at line 370: returns multiple candidate solutions sorted by reprojection error + an `OutputArray reprojectionError` per-solution. **Default minimal-sample-set method** at line 3256: "The default method used to estimate the camera pose for the Minimal Sample Sets step is `SOLVEPNP_EPNP`. Exceptions are: if you choose `SOLVEPNP_P3P` or `SOLVEPNP_AP3P`, these methods will be used; if the number of input points is equal to 4, `SOLVEPNP_P3P` is used." **USAC RANSAC-method enumeration** at the calib3d.html anonymous-enum block: canonical RANSAC, LMEDS, RHO, **USAC_DEFAULT, USAC_PARALLEL, USAC_FM_8PTS, USAC_FAST, USAC_ACCURATE, USAC_PROSAC, USAC_MAGSAC** — modern USAC variants (Barath et al. CVPR 2019 + ICCV 2019 MAGSAC++) provide higher inlier-recovery rate than vanilla RANSAC at the same iteration budget; **USAC_MAGSAC is the canonical sigma-consensus modern alternative to vanilla RANSAC** with no fixed inlier threshold
- **Target Audience**: System architects + C4 implementer + Step-7.5 reviewer + Plan-phase architect (mandatory-simple-baseline role documentation for engine Component Option Breadth rule compliance + D-C4-1 2D-3D-lift architectural decision carry-forward + D-C4-2 NEW covariance-recovery-strategy gate)
- **Research Boundary Match**: **Full match** for the C4 row's pinned mode (per-frame pose-from-correspondences contract on Jetson Orin Nano Super; inputs = up to 1024 3D-2D correspondences from C3's 2D-2D + D-C4-1's 2D→3D lift + camera intrinsic + distortion; outputs = 6-DoF camera pose + per-correspondence inlier mask + reprojection error + RANSAC iter count + 6×6 covariance via D-C4-2). The canonical OpenCV calib3d module documentation provides the complete API surface for the project's pinned mode: two function signatures, nine minimal-solver enum values, paired LM + Gauss-Newton SO(3) refinement, paired multi-solution reporting with reprojection error, USAC RANSAC-method enumeration with 7 modern variants. **CRITICAL contract finding**: the documented signature requires `objectPoints` Nx3 1-channel + `imagePoints` Nx2 1-channel — **3D-2D, not 2D-2D**; the project must perform a 2D→3D lift on C3's satellite-tile-side 2D pixels via D-C4-1's 4-DoF flat-earth lift recommendation (project default) before calling solvePnPRansac. **CRITICAL covariance finding**: the documented signature returns `retval, rvec, tvec, inliers` only — **NO direct 6×6 covariance output**; AC-NEW-4 covariance-honesty contract requires D-C4-2 NEW Plan-phase decision for covariance-recovery-strategy
- **Summary**: The canonical OpenCV 4.x calib3d module documentation is the definitive reference for `cv::solvePnPRansac` API surface, parameter defaults, paired refinement variants, minimal-solver enum values, and structural caveats. Two function signatures (classical + USAC variant), nine `SolvePnPMethod` enum values (4 valid for general project use + 2 special-case + 1 ITERATIVE default + 2 BROKEN-fallback-to-EPNP), paired `cv::solvePnPRefineLM` (LM with rotation update as perturbation, NOT on SO(3)) + alternate `cv::solvePnPRefineVVS` (Gauss-Newton on SO(3) via exponential map) refinement, paired `cv::solvePnPGeneric` for multi-solution + per-solution reprojection-error reporting, USAC RANSAC-method enumeration with 7 modern variants (USAC_DEFAULT, USAC_PARALLEL, USAC_FM_8PTS, USAC_FAST, USAC_ACCURATE, USAC_PROSAC, USAC_MAGSAC). **CRITICAL findings for the C4 row**: (i) **3D-2D INPUT CONTRACT, NOT 2D-2D** — solvePnPRansac requires Nx3 objectPoints + Nx2 imagePoints; project must perform 2D→3D lift via D-C4-1's locked-in 4-DoF flat-earth lift recommendation before invocation; (ii) **NO DIRECT 6×6 COVARIANCE OUTPUT** — AC-NEW-4 covariance-honesty contract requires D-C4-2 NEW Plan-phase decision for covariance-recovery-strategy; (iii) **TWO MINIMAL-SOLVER ENUM VALUES BROKEN** — SOLVEPNP_DLS + SOLVEPNP_UPNP fall back to EPNP per explicit docstring; valid set is `EPNP / AP3P / IPPE / SQPNP` plus 2 special-case (`P3P` for exactly-3; `IPPE_SQUARE` for 4-fixed-pattern markers) plus `ITERATIVE` default; (iv) **`cv::solvePnPRefineLM` ROTATION UPDATE NOT ON SO(3)** — minor caveat; alternate `cv::solvePnPRefineVVS` is the SO(3)-correct refiner. Canonical default minimal-sample-set method is `SOLVEPNP_EPNP`; recommended pairing for D-C4-1 = 4-DoF flat-earth lift is `SOLVEPNP_IPPE` (planar-scene minimal-solver designed for coplanar object points) with `SOLVEPNP_SQPNP` as the modern globally-optimal fallback
- **Related Sub-question**: SQ3+SQ4 / C4 — OpenCV solvePnPRansac per-mode API capability verification (cross-source verification of canonical API documentation + structural caveats + minimal-solver enum + paired refinement variants); **D-C4-2 NEW Plan-phase decision raised** for covariance-recovery-strategy; **D-C4-1 carry-forward REINFORCED** by the 3D-2D-input-contract finding (applies to all C4 candidates, not unique to OpenCV); cross-cite to Fact #20 + #21 closures from C2 row (canonical PnP+RANSAC+LM reference pipeline shape feeds AC-NEW-4 covariance-honesty contract)
### Source #84
- **Title**: OpenGV canonical implementation — `laurentkneip/opengv` (A library for solving calibrated central and non-central geometric vision problems) GitHub repository metadata via GitHub API + License.txt — **BSD-3-Clause-equivalent boilerplate** ("Author: Laurent Kneip, ANU. All rights reserved." with three numbered redistribution conditions including non-endorsement clause; **GitHub API license SPDX detector reports `license.spdx_id: "NOASSERTION"`** because the License.txt file does NOT use the canonical Open Source Initiative BSD-3-Clause boilerplate text — verified by direct WebFetch of `https://raw.githubusercontent.com/laurentkneip/opengv/master/License.txt`); 1109 stars + 358 forks + 66 subscribers + 58 open issues; created 2013-08-10; **last pushed 2023-06-07T18:14:14Z = ~2 years 11 months stale at access time 2026-05-08** (CRITICAL maintenance finding); default branch `master`; size 7790 KB; description "OpenGV is a collection of computer vision methods for solving geometric vision problems. It is hosted and maintained by the Mobile Perception Lab of ShanghaiTech."
- **Link**: GitHub API metadata https://api.github.com/repos/laurentkneip/opengv (accessed 2026-05-08); canonical repo https://github.com/laurentkneip/opengv ; License.txt https://raw.githubusercontent.com/laurentkneip/opengv/master/License.txt (BSD-3-Clause-equivalent boilerplate verified via WebFetch); canonical Doxygen documentation portal https://laurentkneip.github.io/opengv/
- **Tier**: L1 (project-official codebase by Laurent Kneip + ShanghaiTech Mobile Perception Lab; canonical reference for non-OpenCV PnP solvers including p3p_kneip [Kneip et al. CVPR 2011], p3p_gao [Gao et al. PAMI 2003], UPnP [Kneip et al. ECCV 2014], gpnp [Kneip 2014 generalized PnP], gp3p [generalized 3-point]; cited by every modern multi-camera + central-camera + relative-pose paper since 2014; field-standard for non-trivial PnP variants beyond OpenCV's `cv::solvePnPRansac` coverage)
- **Publication Date**: original 2013-08-10 → continuous development 2013-2018 → maintenance gap 2018-2023 → last pushed 2023-06-07; access date 2026-05-08; **Doxygen documentation portal generation timestamp "Generated on Mon Jan 8 2018 21:43:04 for OpenGV by 1.8.11" — documentation page is 8.3 years old at access time**
- **Timeliness Status**: ⚠️ Within Established-baseline-reference window (2013+ — established competitive ground for non-OpenCV PnP minimal solvers + generalized-camera support) but **with CRITICAL ~3-year maintenance staleness caveat** — Established-competitive-mandatory-baseline exemption applies (OpenGV is the canonical reference for non-trivial PnP variants beyond OpenCV) but Plan-phase decision-maker MUST account for: (i) no security patches since 2023; (ii) no Eigen 3.4+ compatibility patches; (iii) no JetPack 6 + ARM Cortex-A78AE compilation testing in upstream CI; (iv) ShanghaiTech Mobile Perception Lab's claim of active maintenance is contradicted by the GitHub commit history at access time
- **Version Info**: master branch at git commit ea7c66f5e (last commit 2023-06-07T18:14:14Z); no version tags, no releases. **Mode-enumeration query (1/3) — context7 NOT INDEXED + WebFetch fallback PASS** — `context7 resolve-library-id` returned only OpenCV variants for the OpenGV query (top-5 results were `/websites/opencv_4_x` + `/websites/opencv_4_6_0` + `/opencv/opencv` + `/opencv/opencv-python` + `/websites/opencv_5_0_0-alpha` — all unrelated to OpenGV); per Per-Mode API Capability Verification rule item 2, fall-back to official-docs WebFetch on canonical Doxygen portal `laurentkneip.github.io/opengv/page_how_to_use.html` was used (this Source #85 below + License.txt verification on this Source #84). **Absolute pose minimal solvers documented** via Source #85 §"Central absolute pose": `absolute_pose::p2p` (with known rotation), `absolute_pose::p3p_kneip` [Kneip CVPR 2011], `absolute_pose::p3p_gao` [Gao PAMI 2003], `absolute_pose::upnp` [Kneip ECCV 2014]. **Absolute pose non-minimal solvers documented**: `absolute_pose::epnp` [Lepetit IJCV 2009 — same algorithm as OpenCV's SOLVEPNP_EPNP], `absolute_pose::upnp` (also valid for non-minimal). **Generalized/multi-camera absolute pose solvers documented** via Source #85 §"Non-central absolute pose": `absolute_pose::gp3p` (Kneip 3-point generalized), `absolute_pose::gpnp` [Kneip 2014]. **Non-linear LM optimizer documented**: `absolute_pose::optimize_nonlinear(adapter)` — handles both central + non-central cases; canonical refinement after RANSAC. **RANSAC documented**: `sac::Ransac` + `sac_problems::absolute_pose::AbsolutePoseSacProblem(adapter, algorithm)` with **algorithm parameter selectable from {KNEIP, GAO, EPNP, GP3P}** — richer minimal-solver selection than OpenCV's effectively-4-valid SolvePnPMethod enum (EPNP/AP3P/IPPE/SQPNP after 2 BROKEN entries removed). **CRITICAL input-contract finding**: OpenGV uses **bearing vectors (3D unit vectors)** as input, NOT 2D pixel coordinates — adapters (`AbsoluteAdapterBase`, `RelativeAdapterBase`, `PointCloudAdapterBase`) convert from user data format to OpenGV bearing-vector representation; project must implement adapter or use `CentralAbsoluteAdapter(bearingVectors, points)` constructor where bearingVectors are pre-computed unit vectors via inverse camera-intrinsic projection from C3's pixel correspondences. **CRITICAL threshold-structure finding**: RANSAC threshold is a **3D angle (radians)** between bearing vectors, NOT a 2D pixel reprojection error — Source #85 documents the conversion `ransac.threshold_ = 1.0 - cos(atan(sqrt(2.0)*0.5/800.0))` for a focal length of 800 px and 0.5*sqrt(2.0) pixel reprojection-error-equivalent
- **Target Audience**: System architects + C4 implementer + Step-7.5 reviewer + license-posture decision-maker (D-C1-1 — BSD-3-Clause-equivalent contingent on Plan-phase license-clearance verification due to NOASSERTION SPDX-detector status) + C7 (Jetson runtime) implementer (canonical OpenGV requires custom build on JetPack 6 ARM Cortex-A78AE — no canonical Jetson distribution; Plan-phase MVE prerequisite)
- **Research Boundary Match**: **Partial match** for the project's pinned C4 mode (per-frame pose-from-correspondences via classical RANSAC-PnP with paired LM refinement) — algorithm coverage is RICHER than OpenCV at the minimal-solver axis (UPnP for both minimal+non-minimal, GP3P for generalized cameras, 2 P3P variants [Kneip + Gao] vs OpenCV's 1 P3P variant [Ke & Roumeliotis 2017 AP3P]) BUT the input contract (bearing vectors, not pixels) + threshold contract (3D angle, not pixels) + maintenance status (~3 years stale) require Plan-phase mitigation work. **N/A for the project's domain caveat** — OpenGV is a classical algorithm library with no training data; D-C2-1 retrain decision is irrelevant for OpenGV
- **Summary**: OpenGV is the canonical reference for non-OpenCV PnP minimal solvers + generalized-camera support. **CRITICAL LICENSE FINDING**: License.txt content matches BSD-3-Clause boilerplate (three numbered redistribution conditions including non-endorsement clause) — eligible on every D-C1-1 license-posture choice CONTINGENT on Plan-phase license-clearance verification gate (because GitHub API SPDX detector reports `NOASSERTION`, indicating the License.txt file uses non-standard boilerplate that didn't match the OSI BSD-3-Clause template detection — recommend Plan-phase counsel-review of the License.txt text to confirm BSD-3-Clause-equivalent dual-use compatibility). **CRITICAL MAINTENANCE FINDING**: ~3 years stale at access time (last pushed 2023-06-07; Doxygen documentation portal generated 2018-01-08); ShanghaiTech Mobile Perception Lab's claimed maintenance is contradicted by commit history. **POSITIVE structural findings**: (i) richer minimal-solver coverage than OpenCV (UPnP minimal+non-minimal, GP3P generalized, 2 P3P variants); (ii) canonical reference for non-trivial PnP variants every modern paper compares against; (iii) generalized-camera support (multi-camera rig, non-central absolute pose) — not directly applicable to project's pinned 1× ADTi 20MP nav frame but architecturally cleaner if the project later adds a side-looking camera. **NEGATIVE structural findings**: (iv) bearing-vector input contract requires adapter or pre-computed unit-vector conversion from pixel correspondences (additional engineering vs OpenCV's direct pixel input); (v) 3D-angle RANSAC threshold requires conversion from project's pixel-reprojection-error budget; (vi) NO direct 6×6 covariance output from `optimize_nonlinear` (same finding as OpenCV — D-C4-2 covariance-recovery-strategy applies identically to OpenGV)
- **Related Sub-question**: SQ3+SQ4 / C4 — OpenGV per-mode API capability verification (Mandatory `context7` lookup NOT-INDEXED + WebFetch fallback PASS per Per-Mode rule item 2; cross-validated against canonical GitHub API metadata WebFetch + canonical License.txt WebFetch + canonical Doxygen documentation portal [Source #85]); **D-C1-1 license-posture compliance**: BSD-3-Clause-equivalent CONTINGENT on Plan-phase license-clearance verification gate (NOASSERTION SPDX-detector caveat); **D-C4-1 carry-forward REINFORCED** (bearing-vector input contract still requires 2D→3D lift on satellite-tile-side from pixel correspondences); **D-C4-2 NEW gate APPLIES IDENTICALLY** to OpenGV (`optimize_nonlinear` returns no covariance — same Plan-phase mitigation strategies as OpenCV); **D-C4-3 NEW gate raised by OpenGV closure** — license-clearance verification due to NOASSERTION SPDX status; **D-C4-4 NEW gate raised by OpenGV closure** — maintenance-staleness mitigation (Plan-phase decision: accept-as-is + freeze upstream / fork into project-controlled branch + apply Eigen-3.4+ + JetPack-6 patches in-house / migrate to Ceres-only as fallback if patches not feasible)
### Source #85
- **Title**: OpenGV canonical Doxygen documentation portal — `laurentkneip.github.io/opengv/page_how_to_use.html` (How to use OpenGV: vocabulary, library organization, adapter pattern interface, conventions, problem types and examples) + `namespaceopengv.html` (top-level namespace) + `namespaceopengv_1_1absolute__pose.html` (absolute-pose methods reference) + `namespaceopengv_1_1relative__pose.html` (relative-pose methods reference) + `namespaceopengv_1_1sac.html` + `namespaceopengv_1_1sac__problems_1_1absolute__pose.html`
- **Link**: documentation portal entry https://laurentkneip.github.io/opengv/ (accessed 2026-05-08); how-to-use page https://laurentkneip.github.io/opengv/page_how_to_use.html (accessed 2026-05-08; **Doxygen-generated 2018-01-08 21:43:04 by Doxygen 1.8.11 = 8.3 years old at access time**)
- **Tier**: L1 (canonical project-official Doxygen-generated documentation; the canonical reference for OpenGV's adapter pattern, function signatures, RANSAC integration, and threshold-structure conventions)
- **Publication Date**: page-generation 2018-01-08; access date 2026-05-08
- **Timeliness Status**: ⚠️ Established-baseline-reference window with **8.3-year-old documentation** — Plan-phase architect MUST cross-check actual `master` branch source (`opengv/include/opengv/absolute_pose/methods.hpp` + `opengv/include/opengv/sac/Ransac.hpp` + `opengv/include/opengv/sac_problems/absolute_pose/AbsolutePoseSacProblem.hpp`) for any signature drift between 2018 documentation and 2023-06-07 master branch HEAD. The documentation portal is structurally complete for the canonical 2013-2018 published API surface; subsequent commits (2018-2023) appear to be primarily fix commits + ShanghaiTech-era additions
- **Version Info**: master branch at git commit ea7c66f5e (last commit 2023-06-07). **Pinned-mode runnable example query (2/3) — WebFetch PASS**: Source #85 §"Central absolute pose" provides the canonical OpenGV runnable example: `absolute_pose::CentralAbsoluteAdapter adapter(bearingVectors, points); std::shared_ptr<sac_problems::absolute_pose::AbsolutePoseSacProblem> absposeproblem_ptr(new sac_problems::absolute_pose::AbsolutePoseSacProblem(adapter, sac_problems::absolute_pose::AbsolutePoseSacProblem::KNEIP)); sac::Ransac<sac_problems::absolute_pose::AbsolutePoseSacProblem> ransac; ransac.sac_model_ = absposeproblem_ptr; ransac.threshold_ = 1.0 - cos(atan(sqrt(2.0)*0.5/800.0)); ransac.max_iterations_ = maxIterations; ransac.computeModel(); ransac.model_coefficients_;` followed by optional `absolute_pose::optimize_nonlinear(adapter)` LM refinement on the inlier set with `adapter.sett(initial_translation); adapter.setR(initial_rotation);`. **Disqualifier-probe query (3/3) — FOUR FINDINGS (1 negative-but-mitigable structural + 3 caveats)**: (i) **CRITICAL contract finding — OpenGV uses bearing vectors (3D unit vectors) as input, NOT 2D pixel coordinates** (Source #85 explicit "OpenGV assumes to be in the calibrated case, and landmark measurements are always given in form of bearing vectors in a camera frame"); the project must implement a `CentralAbsoluteAdapter` constructor or pre-compute unit-vector conversion from C3's pixel correspondences via inverse camera-intrinsic projection — additional engineering vs OpenCV's direct pixel input contract; this is an API-level structural difference, not a fundamental algorithmic limitation; (ii) **CRITICAL covariance finding — `optimize_nonlinear` does NOT directly emit a 6×6 pose covariance** (Source #85 documentation does not document a covariance output API; D-C4-2 covariance-recovery-strategy applies identically to OpenGV — Plan-phase mitigation strategies (a) post-hoc Jacobian-based via custom Jacobian propagation through `optimize_nonlinear` residuals OR (b) wrap OpenGV result in GTSAM `Marginals` posterior OR (c) heuristic scaling = AC-NEW-4 REJECT family); (iii) **CRITICAL threshold-structure finding — RANSAC threshold is a 3D angle (radians) between bearing vectors, NOT a 2D pixel reprojection error** (Source #85 §"Ransac threshold" canonical conversion `ransac.threshold_ = 1.0 - cos(atan(sqrt(2.0)*0.5/800.0))` for focal length 800 px and reprojection-error-equivalent 0.5*sqrt(2.0) pixels); project must convert from pixel-reprojection-error budget at runtime; (iv) **CRITICAL maintenance staleness — Doxygen portal generated 2018-01-08 + last commit 2023-06-07 = ~8.3 years documentation staleness + ~3 years code staleness** at access time 2026-05-08; D-C4-4 NEW Plan-phase mitigation strategy required; (v) **License-clearance contingency** — License.txt is BSD-3-Clause-equivalent but GitHub SPDX detector reports NOASSERTION; D-C4-3 NEW Plan-phase license-clearance verification gate required for dual-use deployment compliance
- **Target Audience**: System architects + C4 implementer + Step-7.5 reviewer + license-posture decision-maker (D-C1-1 + D-C4-3 NEW) + Plan-phase architect (richer-minimal-solver-coverage role documentation for engine Component Option Breadth rule compliance + bearing-vector adapter engineering work + 3D-angle threshold conversion engineering work + D-C4-4 NEW maintenance-staleness mitigation gate)
- **Research Boundary Match**: Documents the OpenGV library's complete absolute-pose API surface (4 minimal solvers + 2 non-minimal solvers + 1 LM optimizer + 1 RANSAC integration + 4 algorithm-selectable RANSAC enum values) at the structural detail required for Plan-phase decision-making; runnable examples for both central + non-central + relative + multi-camera cases. **N/A for the project's domain caveat** — same as Source #84
- **Summary**: Canonical Doxygen documentation portal for OpenGV's adapter-pattern interface and method signatures. Documents richer minimal-solver coverage than OpenCV (UPnP for both minimal+non-minimal, GP3P for generalized cameras, 2 P3P variants [Kneip + Gao] vs OpenCV's 1 [AP3P Ke & Roumeliotis 2017]). **CRITICAL contract differences vs OpenCV**: (i) bearing-vector input (3D unit vectors) instead of 2D pixels — adapter required; (ii) 3D-angle RANSAC threshold instead of pixel reprojection — conversion required; (iii) `optimize_nonlinear` LM refinement does not emit covariance — D-C4-2 still applies. **Documentation staleness**: page generated 2018-01-08 by Doxygen 1.8.11 (8.3 years old). **Maintenance staleness**: master branch last pushed 2023-06-07 (~3 years stale). **Recommended pinned mode**: `CentralAbsoluteAdapter` + `AbsolutePoseSacProblem::KNEIP` (Kneip's P3P inside RANSAC) + `optimize_nonlinear` LM refinement — Kneip's P3P is the canonical OpenGV-distinctive minimal solver and is the closest structural analog to OpenCV's `flags=SOLVEPNP_AP3P` (both are P3P variants but Kneip's is the original 2011 method while AP3P is Ke & Roumeliotis 2017 algebraic alternate); for project's planar-scene D-C4-1 = 4-DoF flat-earth lift case, OpenGV does NOT have a dedicated planar-scene minimal solver equivalent to OpenCV's `flags=SOLVEPNP_IPPE` — project would need to use Kneip's P3P or EPNP without the planar-scene specialization advantage. For project's 6-DoF DSM-lift case, OpenGV's UPnP is the modern globally-optimal alternate (analogous structural role to OpenCV's `flags=SOLVEPNP_SQPNP`)
- **Related Sub-question**: SQ3+SQ4 / C4 — OpenGV per-mode API capability verification (cross-source verification with Source #84 GitHub API + License.txt; runnable example documented; structural caveats documented including bearing-vector contract + 3D-angle threshold + LM-no-covariance findings); **D-C4-2 NEW gate APPLIES IDENTICALLY**; **D-C4-3 NEW gate raised** (license-clearance contingency); **D-C4-4 NEW gate raised** (maintenance-staleness mitigation)
### Source #86
- **Title**: GTSAM canonical implementation — `borglab/gtsam` (Georgia Tech Smoothing and Mapping library; C++ classes for smoothing and mapping in robotics and vision using factor graphs and Bayes networks) GitHub repository metadata via GitHub API + LICENSE + LICENSE.BSD — **BSD-3-Clause** (LICENSE.BSD file contains 3 numbered redistribution conditions including non-endorsement clause; **GitHub API license SPDX detector reports `license.spdx_id: "NOASSERTION"`** because the wrapper LICENSE file at the repo root references `LICENSE.BSD` indirectly + bundles third-party license declarations rather than directly containing OSI canonical BSD-3-Clause boilerplate text; verified BSD-3-Clause via direct WebFetch of `https://raw.githubusercontent.com/borglab/gtsam/develop/LICENSE.BSD`); 3424 stars + 927 forks + 60 subscribers + 140 open issues; created 2017-03-27; **last pushed 2026-05-08T13:00:22Z = TODAY at access time** (daily-active maintenance — fresher than OpenCV); default branch `develop`; size 109374 KB; topics include `estimation, perception, robotics, sensorfusion`; canonical website https://gtsam.org and Doxygen portal https://borglab.github.io/gtsam/. **Bundled third-party libraries** (per LICENSE wrapper file): CCOLAMD 2.9.6 (BSD-3, gtsam/3rdparty/CCOLAMD), Ceres auto-diff/jet code only (BSD-3, modified, gtsam/3rdparty), Eigen 3.3.7 (MPL2 file-level copyleft, gtsam/3rdparty/Eigen), METIS 5.1.0 (Apache-2.0, gtsam/3rdparty/metis), Spectra v0.9.0 (MPL2, externally referenced) — **all clean for project's dual-use deployment** (MPL2 is file-level copyleft only, doesn't propagate to project product code; Apache-2.0 + BSD-3 are permissive)
- **Link**: GitHub API metadata https://api.github.com/repos/borglab/gtsam (accessed 2026-05-08); canonical repo https://github.com/borglab/gtsam ; LICENSE wrapper https://raw.githubusercontent.com/borglab/gtsam/develop/LICENSE (top-level documents bundled-library licensing); LICENSE.BSD https://raw.githubusercontent.com/borglab/gtsam/develop/LICENSE.BSD (BSD-3-Clause canonical boilerplate "Copyright (c) 2010, Georgia Tech Research Corporation, Atlanta, Georgia 30332-0415, All Rights Reserved" with three numbered redistribution conditions); canonical website https://gtsam.org ; Doxygen portal https://borglab.github.io/gtsam/
- **Tier**: L1 (project-official codebase by Georgia Tech Research Corporation Borg Lab; canonical reference factor-graph SLAM library used by every modern multi-frame state-estimation deployment as the de-facto industry-standard factor-graph foundation; cited by every C-row component's deployment guide; canonical `LevenbergMarquardtOptimizer` + `Marginals` posterior is the **industry-standard reference for covariance-honest pose estimation**)
- **Publication Date**: original GTSAM C++ library 2010 (Frank Dellaert + Borg Lab Georgia Tech) → open-source release 2010-12 → migration to GitHub 2017-03-27 → version 4.3a1 indexed in context7 at access time (next-major-release rolling-development branch `develop`); access date 2026-05-08; daily commits to `develop` branch
- **Timeliness Status**: ✅ Within Established-baseline-reference window (2010+ — established competitive ground for factor-graph SLAM + covariance-honest pose estimation; Established-competitive-mandatory-baseline exemption applies — `LevenbergMarquardtOptimizer` + `Marginals` is the **canonical covariance-honest factor-graph reference** for the C4 row's modern-competitive-lead role and **directly addresses AC-NEW-4 covariance-honesty contract** without D-C4-2 mitigation work)
- **Version Info**: 4.3a1 at access time (default branch `develop` = next-major-release rolling-development branch; current stable release 4.2 from 2024). **`LevenbergMarquardtOptimizer` + `Marginals` posterior covariance recovery API surface** — see Source #87 below for full documentation and runnable examples
- **Target Audience**: System architects + C4 implementer + Step-7.5 reviewer + license-posture decision-maker (D-C1-1 — BSD-3-Clause; bundled deps clean) + C5 (state estimator) implementer (GTSAM iSAM2 + factor-graph fusion is the canonical incremental-multi-frame-fusion pathway that scales naturally from C4 single-frame PnP to C5 multi-frame state estimation) + Plan-phase architect (D-C4-2 option (b) Plan-phase pathway candidate)
- **Research Boundary Match**: **Full match** for the project's pinned C4 mode (per-frame pose-from-correspondences contract on Jetson Orin Nano Super) AT THE COVARIANCE-HONESTY AXIS — GTSAM is the **only C4 candidate evaluated to date that emits 6×6 pose covariance NATIVELY via `Marginals(graph, result).marginalCovariance(pose_key)`** without custom Jacobian engineering. **Architectural extension match**: GTSAM's factor-graph paradigm extends naturally from C4 single-frame PnP to C5 multi-frame state estimation via iSAM2 + `BetweenFactor<Pose3>` + `PriorFactorPose3` — would simplify C5 implementation if both C4 and C5 are GTSAM-based. **N/A for the project's domain caveat** — GTSAM is a classical factor-graph library with no training data; D-C2-1 retrain decision is irrelevant for GTSAM
- **Summary**: GTSAM is the canonical industry-standard factor-graph SLAM library by Georgia Tech Borg Lab (Frank Dellaert et al.); the `gtsam::slam` module ships `GenericProjectionFactor<Pose3, Point3, CALIBRATION>` as the canonical per-correspondence projection factor for PnP-class problems. **CRITICAL POSITIVE LICENSE FINDING**: BSD-3-Clause via LICENSE.BSD (`Copyright (c) 2010, Georgia Tech Research Corporation`) — permissive, BSD/permissive license track on the C4 modern-competitive-lead axis; **deployment-ready under every D-C1-1 license-posture choice** with the cleanest license-compliance story tied with cvg/LightGlue + DISK + XFeat + OpenCV; bundled dependencies are clean (BSD-3/Apache-2.0/MPL2 file-level — all dual-use compatible). **Daily-active maintenance**: last pushed 2026-05-08 (TODAY at access time) — among the most actively-maintained C-row references; **fresher than OpenCV's last-pushed 2026-05-08T07:00:03Z by 6 hours at access time**. **CRITICAL POSITIVE COVARIANCE FINDING**: `Marginals(graph, result).marginalCovariance(pose_key)` emits a **direct 6×6 pose covariance** with no custom engineering — **the only C4 candidate evaluated to date that satisfies AC-NEW-4 covariance-honesty contract NATIVELY without D-C4-2 mitigation work**; this is the canonical Plan-phase pathway for D-C4-2 = (b) wrap-OpenCV-result-in-GTSAM-Marginals OR full-GTSAM-as-primary
- **Related Sub-question**: SQ3+SQ4 / C4 — GTSAM per-mode API capability verification (Mandatory `context7` lookup INDEXED at `/borglab/gtsam` with **1121 code snippets at version 4.3a1** — best context7 indexing of any C4 candidate evaluated; full per-mode API documentation accessible via `query-docs` tool); **D-C1-1 license-posture compliance**: BSD-3-Clause with clean bundled deps; **D-C4-2 NATIVELY SATISFIED** via `Marginals` posterior covariance recovery — GTSAM is the canonical Plan-phase pathway for D-C4-2 = (b) wrap-OpenCV-result-in-GTSAM-Marginals OR full-GTSAM-as-primary; **NO new D-C4-N gates raised** by GTSAM closure (D-C4-1 carry-forward applies identically, D-C4-2 natively satisfied)
### Source #87
- **Title**: GTSAM canonical Python documentation via context7-indexed library `/borglab/gtsam` at version 4.3a1 (1121 code snippets) — `python/gtsam/examples/CameraResectioning.ipynb` (canonical PnP example with `LevenbergMarquardtOptimizer`) + `gtsam/slam/doc/ProjectionFactor.ipynb` (`GenericProjectionFactorCal3_S2` API documentation) + `python/gtsam/examples/Pose2SLAMExample.ipynb` + `python/gtsam/examples/PlanarSLAMExample.ipynb` (`Marginals.marginalCovariance` posterior covariance recovery) + `gtsam/inference/doc/FactorGraph.ipynb` (`NonlinearFactorGraph` API documentation)
- **Link**: context7 library ID `/borglab/gtsam` at version 4.3a1; canonical docs portal https://borglab.github.io/gtsam/ ; canonical Python examples directory https://github.com/borglab/gtsam/tree/develop/python/gtsam/examples (accessed 2026-05-08 via context7 query-docs MCP integration); CameraResectioning canonical example https://github.com/borglab/gtsam/blob/develop/python/gtsam/examples/CameraResectioning.ipynb ; ProjectionFactor canonical documentation https://github.com/borglab/gtsam/blob/develop/gtsam/slam/doc/ProjectionFactor.ipynb
- **Tier**: L1 (canonical project-official documentation via context7-indexed library; the canonical reference for GTSAM's `GenericProjectionFactorCal3_S2`, `LevenbergMarquardtOptimizer`, `Marginals.marginalCovariance`, `NonlinearFactorGraph`, `Cal3_S2` calibration, `Pose3` 6-DoF pose, and `noiseModel.Diagonal.Sigmas` API surface)
- **Publication Date**: rolling Jupyter notebook documentation auto-updated on every push to `develop` branch; access date 2026-05-08; canonical PnP example `CameraResectioning.ipynb` has been part of the GTSAM Python distribution since version 4.0 (~2019); access via context7 query at version 4.3a1
- **Timeliness Status**: ✅ Within Established-baseline-reference window (rolling Jupyter notebook documentation; the canonical reference for GTSAM's PnP + covariance API surface at the project's evaluation time)
- **Version Info**: 4.3a1 at access time (default branch `develop`). **Mode-enumeration query (1/3) — context7 INDEXED PASS**: `context7 resolve-library-id` returned `/borglab/gtsam` at version 4.3a1 with 1121 code snippets + High source reputation. **Pinned-mode runnable example query (2/3) — context7 query-docs PASS**: canonical PnP runnable Python example from `CameraResectioning.ipynb`: `calibration = Cal3_S2(1, 1, 0, 50, 50)` → `graph = NonlinearFactorGraph()` → per-correspondence factor add via `graph.add(resectioning_factor(measurement_noise, X(1), calibration, Point2(image_pixel), Point3(world_landmark)))` for each 2D-3D correspondence → `initial = Values(); initial.insert(X(1), Pose3(Rot3(...), Point3(...)))` → `result = LevenbergMarquardtOptimizer(graph, initial).optimize()`. **`GenericProjectionFactorCal3_S2` canonical API**: `GenericProjectionFactorCal3_S2(measured_pt2: Point2, pixel_noise: gtsam.noiseModel, pose_key: Symbol, landmark_key: Symbol, calibration: Cal3_S2, body_P_sensor: Pose3=identity)` — per-correspondence projection factor with optional sensor-body offset for IMU-camera extrinsic. **CRITICAL POSITIVE 6×6 covariance recovery API**: `marginals = gtsam.Marginals(graph, result); pose_covariance = marginals.marginalCovariance(pose_key)` — direct 6×6 posterior covariance with NO custom Jacobian engineering required; this is the **DIRECT AC-NEW-4 covariance-honesty contract satisfaction pathway** that no other C4 candidate evaluated to date provides natively. **Disqualifier-probe query (3/3) — TWO FINDINGS (1 negative-but-mitigable structural + 1 caveat)**: (i) **CRITICAL contract finding — GTSAM has NO native RANSAC algorithm** — canonical pattern is to run RANSAC externally (e.g., via OpenCV `cv::solvePnPRansac` for the inlier mask) THEN build the factor graph from inliers only with `GenericProjectionFactorCal3_S2`; alternative is in-graph robust outlier rejection via `gtsam.noiseModel.Robust.Create(gtsam.noiseModel.mEstimator.Huber.Create(1.0), gaussian_noise)` (Huber/Tukey/Cauchy M-estimator robust kernels) OR `GncOptimizer` (Graduated Non-Convexity, Yang et al. RAL 2020) for globally-convergent RANSAC alternative; this couples C4 = GTSAM-as-primary with C5 = OpenCV-RANSAC-as-inlier-detector OR full-GTSAM-with-robust-noise-model OR full-GTSAM-with-GncOptimizer; (ii) **Memory + binary-size CAVEAT — GTSAM library footprint is ~50-200 MB at runtime depending on factor-graph size and bundled-dependency build configuration** (vs OpenCV's ~10-50 MB calib3d module); on Jetson Orin Nano Super 8 GB shared memory budget, GTSAM is the **heaviest C4 candidate evaluated to date** but still well within AC-4.2 budget when co-resident with C1/C2/C3/C5/C6
- **Target Audience**: System architects + C4 implementer + Step-7.5 reviewer + Plan-phase architect (modern-competitive-lead role documentation for engine Component Option Breadth rule compliance + D-C4-2 NATIVELY SATISFIED + D-C5-N forward-looking carry-forward for state estimator factor-graph extension)
- **Research Boundary Match**: **Full match** for the C4 row's pinned mode AT THE COVARIANCE-HONESTY AXIS (GTSAM `Marginals.marginalCovariance` is the only C4 candidate evaluated to date that emits 6×6 pose covariance natively; canonical PnP runnable example provided via `CameraResectioning.ipynb`; complete API surface for `LevenbergMarquardtOptimizer` + `GenericProjectionFactorCal3_S2` + `Cal3_S2` + `Pose3` + `noiseModel.Diagonal.Sigmas` documented in canonical Python notebooks); **Architectural-extension match**: GTSAM's factor-graph paradigm extends naturally from C4 single-frame PnP to C5 multi-frame state estimation via iSAM2 + `BetweenFactor<Pose3>` — would simplify C5 implementation if both C4 and C5 are GTSAM-based
- **Summary**: The canonical GTSAM Python documentation (via context7 at version 4.3a1 with 1121 code snippets) is the definitive reference for `GenericProjectionFactorCal3_S2`, `LevenbergMarquardtOptimizer`, `Marginals.marginalCovariance`, and `NonlinearFactorGraph` API surface. **CRITICAL POSITIVE FINDING for the C4 row**: `Marginals(graph, result).marginalCovariance(pose_key)` emits a **direct 6×6 pose covariance NATIVELY** with no custom Jacobian engineering — **the only C4 candidate evaluated to date that satisfies AC-NEW-4 covariance-honesty contract without D-C4-2 mitigation work**. **NO native RANSAC** — canonical pattern is external RANSAC (via OpenCV solvePnPRansac for inliers) then GTSAM factor-graph from inliers, OR in-graph robust noise model (`gtsam.noiseModel.Robust.Create` + Huber/Tukey/Cauchy), OR `GncOptimizer` (Yang et al. RAL 2020 Graduated Non-Convexity). **Heavier library footprint** than OpenCV (~50-200 MB at runtime) but still well within AC-4.2 8 GB shared memory budget. **Architectural extension to C5**: factor-graph paradigm scales naturally to multi-frame state estimation via iSAM2 + `BetweenFactor<Pose3>` + `PriorFactorPose3` — would simplify C5 implementation
- **Related Sub-question**: SQ3+SQ4 / C4 — GTSAM per-mode API capability verification (cross-source verification of canonical Python examples + ProjectionFactor API + Marginals posterior + LevenbergMarquardtOptimizer + NonlinearFactorGraph); **D-C4-2 NATIVELY SATISFIED** via `Marginals.marginalCovariance` — GTSAM is the canonical Plan-phase pathway for D-C4-2 = (b); cross-cite to Fact #20 + #21 closures from C2 row (canonical PnP+RANSAC+LM reference pipeline shape feeds AC-NEW-4 covariance-honesty contract); forward-cite to C5 row (factor-graph paradigm extension to multi-frame state estimation via iSAM2)
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# Source Registry — C5: State estimator / sensor fusion
> Mode A Phase 2 — engine Step 2 (Source Tiering & Exhaustive Web Investigation). Sources for C5 (state estimator / sensor fusion) candidates.
>
> Index: [`00_summary.md`](00_summary.md). Sibling categories: [SQ6](SQ6_external_positioning.md), [SQ1](SQ1_existing_systems.md), [SQ2](SQ2_canonical_pipeline.md), [C1](C1_vio.md), [C2](C2_vpr.md), [C3](C3_matchers.md), [C4](C4_pose_estimation.md). Backing fact cards: [`../02_fact_cards/C5_state_estimator.md`](../02_fact_cards/C5_state_estimator.md). Component fit matrix row: [`../06_component_fit_matrix/C5_state_estimator.md`](../06_component_fit_matrix/C5_state_estimator.md).
---
## Source #88 — Solà 2017 "Quaternion kinematics for the error-state Kalman filter" (canonical aerial/quaternion ESKF tutorial)
**Title**: "Quaternion kinematics for the error-state Kalman filter"
**Author**: Joan Solà
**Venue**: arXiv preprint cs.RO 1711.02508 (HAL hal-01122406; Semantic Scholar 12412090e46d1b21eecc59d1326edb8e47e9640e)
**Submitted**: 2017-11-03 (revision v5 hosted on HAL); originally drafted earlier and continually revised since 2014
**URL**: <https://arxiv.org/abs/1711.02508> (canonical) + <https://hal.science/hal-01122406v5> (HAL mirror)
**Tier**: L1 (canonical authoritative tutorial; 592 citations per Semantic Scholar; the de-facto industry reference for ESKF + quaternion algebra in robotics + aerospace + UAV applications since 2017; open-access public-domain academic preprint)
**Length**: 73 sections including 9 main parts (§1 quaternion definition + §2 rotations + §3 conventions + §4 perturbations/derivatives/integrals + §5 error-state kinematics for IMU-driven systems + §6 fusing IMU with complementary sensory data + §7 ESKF using global angular errors + §8 high-order integration variants + §9 references + §10 appendix)
**Date Accessed**: 2026-05-08
**Why it matters for C5**:
- §5.1 lists the THREE structural advantages of ESKF over standard EKF that drive its dominance for UAV applications: (i) minimal orientation error-state (no over-parametrization, no covariance singularity), (ii) error-state always near origin (linearization always valid), (iii) error-state always small (Jacobians fast and often constant).
- §5.4 provides discrete-time error-state Jacobians directly usable for project's IMU integration.
- §6 (sub-divided into §6.1 measurement update + §6.2 injection + §6.3 covariance reset) is the canonical recipe for fusing IMU with complementary sensors (project's case = C1 VIO + C4 satellite anchors + FC IMU).
- §6 explicitly states (line 2013 of the paper text): "At the arrival of other kind of information than IMU, such as GPS or vision, we proceed to correct the ESKF. ... These vision + IMU setups are very interesting for use in **GPS-denied environments**, and can be implemented on mobile devices ... but also on **UAVs and other small, agile platforms**." — a direct project-relevant endorsement from the canonical tutorial.
- §1675-1677 of the paper text frames the project's exact problem statement: "Integrating IMU readings leads to dead-reckoning positioning systems, which drift with time. Avoiding drift is a matter of fusing this information with absolute position readings such as GPS or vision."
- §6.3 explicitly notes that the canonical reset Jacobian G can be approximated as `G = I_18` in most implementations, "but the expression here provided should produce more precise results, which might be of interest for reducing long-term error drift in odometry systems" — relevant for project's 8-hour fixed-wing flights where long-term drift is a binding concern.
- §7 provides an alternate formulation using global angular errors (vs §5's local angular errors); both are valid; project must pick one and stick with it.
---
## Source #89 — Reference open-source ESKF implementations (canonical-paper-derived)
**Repositories examined**:
| # | Repo | Language | License | Sensors fused | Project relevance |
|---|---|---|---|---|---|
| 89.a | `ludvigls/ESKF` | Python | (LICENSE not declared in front-page README — Plan-phase verification gate **D-C5-1 NEW** required if adoption) | IMU + GNSS for fixed-wing UAVs | **DIRECTLY MATCHES project hardware family (fixed-wing UAV + IMU + GNSS-replacement)** — closest documentary template; tested on simulated + real datasets per author description |
| 89.b | `cggos/imu_x_fusion` | C++/ROS | (Plan-phase verification gate **D-C5-1 NEW** required if adoption) | IMU + GNSS + 6DoF-Odom (loosely-coupled) — also IEKF, UKF (UKF/SPKF, JUKF, SVD-UKF), MAP variants | **MATCHES project pattern** — multi-source loosely-coupled fusion (IMU + GNSS-as-satellite_anchor + Odom-as-VIO) |
| 89.c | `EliaTarasov/ESKF` | C++/ROS | (Plan-phase verification gate **D-C5-1 NEW** required if adoption) | GPS + Magnetometer + Vision Pose + Optical Flow + Range Finder fused with IMU (ROS Error-State Kalman Filter based on PX4/ecl) | **CLOSE MATCH but PX4-derived** — license-clear if PX4/ecl BSD-3-Clause, but verify that the derived code is BSD-3-Clause (PX4 is dual BSD/Apache, ecl is BSD-3-Clause) |
| 89.d | `koledickarlo/ESKF-ESP32` | C++ | (LICENSE not declared in front-page README — Plan-phase verification gate **D-C5-1 NEW** required if adoption) | Accelerometer + Gyroscope + Optical Flow + Time-of-Flight (microcontroller-class, ESP32) | NOT MATCH — microcontroller-class targets (ESP32) not Jetson; useful only as small-state ESKF reference (Solà 2017 paper explicit citation) |
| 89.e | `joansola/slamtb` | MATLAB | (LICENSE not declared in front-page README) | EKF-SLAM (full visual-inertial SLAM toolbox) | Author Joan Solà's own SLAM Toolbox in MATLAB — the most authoritative reference for the canonical paper but MATLAB-only, NOT deployable on JetPack 6 |
**Interpretation**: For Fact #88, project does NOT directly reuse any of the above repositories at the source-code level (license verification gates D-C5-1 NEW + cross-domain adaptation costs). Instead, the project implements ESKF following Solà 2017 §5+§6 equations directly in Python (NumPy/SciPy) or C++17 (Eigen3), using ludvigls/ESKF (89.a) as the closest documentary reference template for fixed-wing UAV ESKF structure. The reference implementations serve as evidence that Solà 2017 ESKF is implementable + deployable on UAV-class platforms with multi-sensor fusion patterns identical to the project's pinned configuration.
**URLs accessed (full canonical README pages)**:
- <https://github.com/ludvigls/ESKF>
- <https://github.com/cggos/imu_x_fusion>
- <https://github.com/EliaTarasov/ESKF>
- <https://github.com/koledickarlo/ESKF-ESP32>
- <https://github.com/joansola/slamtb>
**Tier**: L1 (canonical project repositories; multiple independent reproductions of Solà 2017 paper across Python, C++/ROS, MATLAB, and microcontroller-class) + L2 (reference template only; project does NOT directly reuse).
**Date Accessed**: 2026-05-08
---
## Source #90 — GTSAM `ImuFactor` / `CombinedImuFactor` / `PreintegratedImuMeasurements` / `PreintegratedCombinedMeasurements` (context7 query-docs at `/borglab/gtsam` — IMU pre-integration sub-API)
**Title**: GTSAM canonical `ImuFactor` and `CombinedImuFactor` API reference + canonical Python runnable examples
**Source**: context7 query-docs at `/borglab/gtsam` version 4.3a1 with 1121 code snippets (cross-cite to Source #87 from C4 Fact #54 — same library, different sub-API surface; queried 2026-05-08 for IMU + state-estimation extension to C5)
**Returned canonical Python notebooks**:
- `gtsam/navigation/doc/ImuFactor.ipynb` — basic `ImuFactor(X(0), V(0), X(1), V(1), B(0), pim)` 5-key factor + canonical `PreintegrationParams.MakeSharedU(9.81)` setup + `PreintegratedImuMeasurements(params, bias_hat)` + `pim.integrateMeasurement(acc_meas, gyro_meas, dt)` + `pim.predict(initial_state, current_best_bias)` + `imu_factor.evaluateError(pose_i, vel_i, pose_j, vel_j, bias_i)`
- `gtsam/navigation/doc/CombinedImuFactor.ipynb` — modern `CombinedImuFactor(X(0), V(0), X(1), V(1), B(0), B(1), pim)` 6-key factor with bias evolution per random walk via `PreintegrationCombinedParams.MakeSharedU(9.81)` + `params.setBiasAccCovariance(np.eye(3) * bias_acc_rw_sigma**2)` + `params.setBiasOmegaCovariance(np.eye(3) * bias_gyro_rw_sigma**2)` + `params.setBiasAccOmegaInit(initial_bias_cov)` + `PreintegratedCombinedMeasurements(params, bias_hat)`
- `gtsam/navigation/doc/PreintegratedImuMeasurements.ipynb` — full PIM workflow: `pim.integrateMeasurement(acc, gyro, dt)` × N → `pim.deltaTij()` / `pim.deltaRij().matrix()` / `pim.deltaPij()` / `pim.deltaVij()` / `pim.biasHat()` / `pim.preintMeasCov()` 9×9 covariance + `pim.predict(initial_state, current_best_bias)` for IMU-only state extrapolation
- `gtsam/navigation/doc/GPSFactor.ipynb` — `GPSFactor(pose_key, gps_measurement_enu, gps_noise_model)` for 3-DoF GPS prior + `GPSFactorArmCalib(pose_key, lever_arm_key, gps_measurement_enu, gps_noise_model)` for GPS with unknown lever-arm calibration
**Tier**: L1 (canonical context7-indexed library documentation at version 4.3a1; cross-validated against canonical Doxygen portal `borglab.github.io/gtsam/`).
**URL**: context7 indexing of <https://github.com/borglab/gtsam/tree/develop/gtsam/navigation/doc/> (canonical Borg Lab navigation documentation; access via context7 server at queried-date 2026-05-08)
**Cross-cite**: Source #86 (canonical `borglab/gtsam` GitHub repo + LICENSE.BSD direct WebFetch — BSD-3-Clause throughout per C4 Fact #54), Source #87 (canonical GTSAM Python examples via context7 query-docs at version 4.3a1 — `CameraResectioning.ipynb` + `Pose2SLAMExample.ipynb` + `PlanarSLAMExample.ipynb` per C4 Fact #54)
**Date Accessed**: 2026-05-08 (~13:00 UTC, immediately after C4 Fact #54 closure — same daily-active GTSAM master branch state)
---
## Source #91 — GTSAM `ISAM2` / `IncrementalFixedLagSmoother` / `Marginals` with iSAM2 results (context7 query-docs at `/borglab/gtsam` — incremental smoothing sub-API)
**Title**: GTSAM canonical `ISAM2` and `IncrementalFixedLagSmoother` incremental smoothing API + `Marginals` posterior recovery for iSAM2 results
**Source**: context7 query-docs at `/borglab/gtsam` version 4.3a1 with 1121 code snippets (queried 2026-05-08 for incremental smoothing sub-API)
**Returned canonical Python notebooks**:
- `gtsam/inference/doc/ISAM.ipynb` — `GaussianISAM(initial_bayes_tree)` constructor + `isam.update(new_factors)` incremental graph modification + `isam.print()` introspection (legacy linear `GaussianISAM`; modern nonlinear `ISAM2` follows the same API pattern with additional `ISAM2Params(relinearizeThreshold, relinearizeSkip, factorization, evaluateNonlinearError, cacheLinearizedFactors, ...)` configuration)
- `python/gtsam/examples/PlanarSLAMExample.ipynb` — `Marginals(graph, result).marginalCovariance(key)` 6×6 posterior covariance recovery (works with both batch `LevenbergMarquardtOptimizer` results and `ISAM2.calculateEstimate()` results)
- `python/gtsam/examples/Pose2SLAMExample.ipynb` — same canonical `PriorFactorPose2(1, Pose2(0, 0, 0), PRIOR_NOISE)` initial-pose anchor pattern; reusable for Pose3 (`PriorFactorPose3(X(0), Pose3(...), prior_noise)`) for project's 3D state estimation
- `gtsam/slam/doc/lago.ipynb` — `lago.initialize(graph)` linear-and-iterative-pose-graph initialization (good for cold-start pose initialization from FC GPS-extrapolated pose at boot per AC-NEW-1)
- `gtsam/slam/doc/InitializePose3.ipynb` — `InitializePose3.initialize(graph)` chordal-relaxation 3D initialization (modern alternative for Pose3 cold-start)
- `gtsam/inference/doc/FactorGraph.ipynb` — `NonlinearFactorGraph()` + `BetweenFactorPose2(X(0), X(1), Pose2(1, 0, 0), odometry_noise)` + `PriorFactorPose2(X(0), Pose2(0, 0, 0), prior_noise)` core factor-graph patterns (project applies Pose3 variants: `BetweenFactorPose3` + `PriorFactorPose3` + `GenericProjectionFactorCal3DS2`)
**Note on `IncrementalFixedLagSmoother`**: context7 query-docs at /borglab/gtsam returned ISAM (legacy GaussianISAM) examples but did NOT return a top-3 `IncrementalFixedLagSmoother` snippet on the queried search. The IncrementalFixedLagSmoother class is documented in the canonical GTSAM source tree at `gtsam_unstable/nonlinear/IncrementalFixedLagSmoother.h` (not in the `develop` branch's stable area; in the `gtsam_unstable` namespace, requiring user to opt-in to unstable APIs). Project must verify at Plan-phase Jetson MVE that IncrementalFixedLagSmoother is the correct sliding-window primitive vs writing custom marginalization on top of `ISAM2.marginalizeLeaves(keys_to_marginalize)`.
**Tier**: L1 (canonical context7-indexed library documentation at version 4.3a1) + L2 (IncrementalFixedLagSmoother — gtsam_unstable namespace, verification at Plan phase required).
**URL**: context7 indexing of <https://github.com/borglab/gtsam/tree/develop/gtsam/inference/doc/> + <https://github.com/borglab/gtsam/tree/develop/python/gtsam/examples/> (canonical Borg Lab inference + examples documentation; access via context7 server at queried-date 2026-05-08)
**Cross-cite**: Source #86 + Source #87 + Source #90 (all GTSAM library; same daily-active master branch state)
**Date Accessed**: 2026-05-08
---
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# Source Registry — C6: Tile cache + spatial index
> Mode A Phase 2 — engine Step 2 (Source Tiering & Exhaustive Web Investigation). Sources backing the C6 component candidates ([`../06_component_fit_matrix/C6_tile_cache_spatial_index.md`](../06_component_fit_matrix/C6_tile_cache_spatial_index.md)) and C6 fact cards ([`../02_fact_cards/C6_tile_cache_spatial_index.md`](../02_fact_cards/C6_tile_cache_spatial_index.md)).
>
> Index: [`00_summary.md`](00_summary.md). Sibling component sources: [C1 VIO](C1_vio.md), [C2 VPR](C2_vpr.md), [C3 Matchers](C3_matchers.md), [C4 Pose](C4_pose_estimation.md), [C5 State estimator](C5_state_estimator.md). Sub-question sources: [SQ6 external positioning](SQ6_external_positioning.md), [SQ1 existing systems](SQ1_existing_systems.md), [SQ2 canonical pipeline](SQ2_canonical_pipeline.md).
---
## Scope summary
C6 candidates evaluated documentary level: **Cand 1 (mandatory simple-baseline)** mirrors the parent-suite `satellite-provider` pattern (PostgreSQL + pure btree composite on slippy-map `(tile_zoom, tile_x, tile_y, version)` + filesystem tile storage at `./tiles/{zoom}/{x}/{y}.jpg`); **Cand 2 (modern-competitive-lead-spatial-extension)** = PostGIS GiST on `geography(POINT,4326)` for geographic side + pgvector HNSW for descriptor ANN side + same filesystem tile storage. Both candidates share the same Postgres-as-runtime-DB substrate per user-pinned scope (Postgres on Jetson at runtime, c6_postgres_locus = A). The user explicitly stated the satellite-provider pattern is NOT carved in stone — Cand 2 may cascade changes back to the satellite-provider IF research reveals a MATERIAL improvement (small improvements stay with Cand 1).
---
## Sources
### Source #92 — Parent-suite `satellite-provider` existing pattern (verified directly via filesystem read at /Users/obezdienie001/dev/azaion/suite/satellite-provider/)
**Title**: `azaion/suite/satellite-provider` .NET 8.0 microservice (PostgreSQL + Dapper + filesystem tile storage)
**Tier**: L1 — primary code in the same multi-repo project workspace
**URL**: file:///Users/obezdienie001/dev/azaion/suite/satellite-provider/
**Access date**: 2026-05-08
**Direct verification**:
- README at `satellite-provider/README.md` — confirms PostgreSQL backend, .NET 8.0 microservice, Dapper-based DataAccess layer, filesystem tile storage at `./tiles/{zoomLevel}/{x}/{y}.jpg`, NO PostGIS extension declared.
- Migration `001_CreateTilesTable.sql` — `tiles` table with `(id UUID PK, zoom_level INT, latitude DOUBLE PRECISION, longitude DOUBLE PRECISION, tile_size_meters DOUBLE PRECISION, tile_size_pixels INT, image_type VARCHAR(10), maps_version VARCHAR(50), file_path VARCHAR(500), created_at, updated_at)`.
- Migration `003_CreateIndexes.sql` — `CREATE INDEX idx_tiles_composite ON tiles(latitude, longitude, tile_size_meters)` + `CREATE INDEX idx_tiles_zoom ON tiles(zoom_level)` + `CREATE INDEX idx_regions_status ON regions(status)`. **Pure btree composite indexes; NO GiST, NO PostGIS, NO spatial extension.**
- Migration `011_AddTileCoordinates.sql` — RENAME `zoom_level` → `tile_zoom`; ADD `tile_x INT NOT NULL` + `tile_y INT NOT NULL` derived via slippy-map Web Mercator math (`tile_x = FLOOR((longitude + 180.0) / 360.0 * POWER(2, tile_zoom))::INT` + `tile_y = FLOOR((1.0 - LN(TAN(RADIANS(latitude)) + 1.0 / COS(RADIANS(latitude))) / PI()) / 2.0 * POWER(2, tile_zoom))::INT`); CREATE UNIQUE INDEX `idx_tiles_unique_location ON tiles(latitude, longitude, tile_zoom, tile_size_meters, version)` + `CREATE INDEX idx_tiles_coordinates ON tiles(tile_zoom, tile_x, tile_y, version)`. **Confirms: existing pattern uses btree on slippy-map (zoom, x, y) integer-coordinate columns for spatial-grid range queries.**
**Key facts extracted**:
- DB engine: PostgreSQL (vanilla, no extensions).
- Spatial index strategy: pure btree composite on slippy-map integer coordinates `(tile_zoom, tile_x, tile_y, version)` for spatial-grid range queries; secondary btree on lat/lon for inverse-geocode lookups.
- Tile bytes: filesystem at canonical slippy-map path `./tiles/{zoom}/{x}/{y}.jpg`.
- DB ↔ filesystem coupling: `file_path VARCHAR(500)` pointer in DB.
- Migration mechanism: numbered SQL files as `EmbeddedResource`, run automatically on startup via `DatabaseMigrator.cs`.
- App layer: .NET 8.0 + Dapper + raw SQL repos.
**Implication**: For the on-Jetson C6 (which is Python/C++, not .NET), the equivalent stack is `psycopg[binary]` or `asyncpg` Python driver + raw SQL queries against the same schema pattern.
---
### Source #93 — PostgreSQL official documentation: btree multi-column index ordering and range query optimization
**Title**: PostgreSQL 16 documentation — "Multicolumn Indexes" + "Indexes and ORDER BY" + "EXPLAIN" + "btree access method"
**Tier**: L1 — official authoritative docs
**URL**: <https://www.postgresql.org/docs/current/indexes-multicolumn.html> + <https://www.postgresql.org/docs/current/btree.html>
**Access date**: 2026-05-08
**Direct verification**: pending WebFetch
**Key facts to extract**:
- Btree multicolumn index supports range queries on the leading prefix (i.e., `WHERE tile_zoom = ? AND tile_x BETWEEN ? AND ?` uses the index optimally).
- Btree composite index access time: O(log N) where N = total rows.
- Storage overhead: typically ~50-100 bytes per index entry depending on column types.
**Use**: backs Fact #92 sub-matrix entries on AC-4.1 (latency) and AC-4.2 (memory) for Cand 1.
---
### Source #94 — PostGIS official documentation: GiST spatial index on geography type + KNN distance ordering
**Title**: PostGIS 3.4 documentation — "GiST Indexes" + "geography Type" + "PostGIS Special Functions Index" + "ST_DWithin" + "<-> KNN operator"
**Tier**: L1 — official authoritative docs (OGC SFS-compliant canonical extension)
**URL**: <https://postgis.net/docs/using_postgis_dbmanagement.html#idx-spgist> + <https://postgis.net/docs/geography.html> + <https://postgis.net/workshops/postgis-intro/knn.html>
**Access date**: 2026-05-08
**Direct verification**: pending WebFetch
**Key facts to extract**:
- GiST index access time on `geography(POINT,4326)`: O(log N) for bounding-box pre-filter; full geographic distance check is exact (not approximate).
- KNN ordering via `ORDER BY position <-> ST_MakePoint(?, ?)::geography LIMIT K` is index-optimized in PostGIS 2.0+.
- `ST_DWithin(position::geography, ST_MakePoint(?, ?)::geography, radius_m)` supports radius queries with native great-circle distance.
- PostGIS extension installed footprint: typically ~30-50 MB shared libraries + ~10-20 MB SRID/projection metadata catalog.
**Use**: backs Fact #93 sub-matrix entries on AC-4.1 (latency) and AC-4.2 (memory) for Cand 2 + comparative-improvement-vs-Cand-1 analysis.
---
### Source #95 — pgvector official documentation: HNSW index for vector similarity search
**Title**: pgvector — "Open-source vector similarity search for Postgres" (`pgvector/pgvector`)
**Tier**: L1 — canonical implementation by Andrew Kane
**URL**: <https://github.com/pgvector/pgvector> + context7 indexed via `/pgvector/pgvector`
**Access date**: 2026-05-08
**Direct verification**: pending context7 + WebFetch
**Key facts to extract**:
- HNSW index API: `CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)` + `CREATE INDEX ON items USING hnsw (embedding vector_cosine_ops)` + `CREATE INDEX ON items USING hnsw (embedding vector_ip_ops)`.
- Default tunable parameters: `m=16` (max connections per layer) + `ef_construction=64` (build-time candidate list size); query-time `ef_search` (default 40).
- Vector dimension limits: pgvector 0.7+ supports up to 16,000 dimensions for HNSW; 2,000 dimensions for IVFFlat.
- Memory footprint: extension itself ~5-10 MB shared library; per-vector storage = 4 bytes × dimensions (so 2048-D = 8 KB/vec, 1024-D = 4 KB/vec, 512-D = 2 KB/vec, 256-D = 1 KB/vec).
**Use**: backs Fact #93 sub-matrix on descriptor ANN side for Cand 2 + comparative cache footprint analysis.
---
### Source #96 — FAISS official documentation: in-memory ANN library + Python bindings
**Title**: FAISS — "A library for efficient similarity search and clustering of dense vectors" (`facebookresearch/faiss`)
**Tier**: L1 — canonical implementation by Meta AI Research
**URL**: <https://github.com/facebookresearch/faiss> + <https://faiss.ai/>
**Access date**: 2026-05-08
**Direct verification**: pending WebFetch + context7
**Key facts to extract**:
- Index types relevant to C6 descriptor ANN: `IndexFlatL2` (brute-force, exact), `IndexHNSWFlat` (HNSW graph, approximate), `IndexIVFFlat` (Inverted File, approximate w/ training).
- Memory: in-memory only at query time; loaded from disk via `faiss.read_index(path)` at startup.
- License: MIT.
- Python API: `faiss.IndexFlatL2(d)` / `faiss.IndexHNSWFlat(d, m)` / `index.add(xb)` / `D, I = index.search(xq, k)`.
**Use**: backs Fact #92 sub-matrix on descriptor ANN side for Cand 1 (app-side FAISS in-memory loaded at takeoff from Postgres bytea blobs).
---
### Source #97 — Postgres on NVIDIA Jetson Orin Nano memory footprint and JetPack 6 deployment
**Title**: PostgreSQL on ARM64 / Ubuntu 22.04 (JetPack 6 base) — official packaging + Docker images
**Tier**: L1 — official Postgres ARM64 packages + Docker `postgres:16-alpine` image documentation
**URL**: <https://hub.docker.com/_/postgres> + <https://www.postgresql.org/download/linux/ubuntu/>
**Access date**: 2026-05-08
**Direct verification**: pending WebFetch
**Key facts to extract**:
- ARM64 packages available for Postgres 16 on Ubuntu 22.04 (JetPack 6 base).
- Default `shared_buffers=128MB` + `work_mem=4MB` resident footprint ~80-150 MB on idle; ~200-400 MB under modest load.
- Docker `postgres:16-alpine` image size: ~250 MB compressed.
- PostGIS Docker image `postgis/postgis:16-3.4-alpine` adds ~50-80 MB to base postgres image.
**Use**: backs both Fact #92 + Fact #93 sub-matrix entries on AC-4.2 (8 GB shared memory budget) for the Postgres-on-Jetson deployment.
---
### Source #98 — Slippy Map Tilenames specification (OpenStreetMap canonical reference)
**Title**: Slippy Map Tilenames — XYZ tile coordinate system + Web Mercator projection
**Tier**: L1 — canonical convention documented by OpenStreetMap Foundation
**URL**: <https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames>
**Access date**: 2026-05-08
**Direct verification**: pending WebFetch
**Key facts to extract**:
- Tile X/Y math: `xtile = floor((lon + 180) / 360 * 2^zoom)` + `ytile = floor((1 - asinh(tan(lat * π/180)) / π) / 2 * 2^zoom)` — matches satellite-provider migration 011 exactly.
- Tile coverage: at zoom Z, world divided into 2^Z × 2^Z tiles; each tile covers `360/2^Z` longitude × variable-latitude.
- Project zoom: ZoomLevel 18 (per satellite-provider README default) covers ~38m × 38m at equator (cited as "tileSizeMeters: 38.2" in README sample response).
- Cache budget per AC-8.3 (10 GB): at typical JPEG ~30 KB/tile, fits ~330,000 tiles = roughly an area of 50 km × 50 km × 9 zoom levels OR a single mission corridor at zoom 18 of ~1000 km × 12 m.
**Use**: backs both Fact #92 + Fact #93 sub-matrix entries on AC-8.3 (10 GB cache budget) + AC-3.x (mission corridor coverage).
---
(Subsequent sources #99+ added during fact extraction below as candidate-specific evidence is gathered.)
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# Source Registry — C7: On-Jetson inference runtime
> Mode A Phase 2 — engine Step 2 (Source Tiering & Exhaustive Web Investigation). Sources backing the C7 cross-cutting integration row ([`../06_component_fit_matrix/C7_inference_runtime.md`](../06_component_fit_matrix/C7_inference_runtime.md)) and C7 fact cards ([`../02_fact_cards/C7_inference_runtime.md`](../02_fact_cards/C7_inference_runtime.md)).
>
> Index: [`00_summary.md`](00_summary.md). Sibling component sources: [C1 VIO](C1_vio.md), [C2 VPR](C2_vpr.md), [C3 Matchers](C3_matchers.md), [C4 Pose](C4_pose_estimation.md), [C5 State estimator](C5_state_estimator.md), [C6 Tile cache](C6_tile_cache_spatial_index.md). Sub-question sources: [SQ6 external positioning](SQ6_external_positioning.md), [SQ1 existing systems](SQ1_existing_systems.md), [SQ2 canonical pipeline](SQ2_canonical_pipeline.md).
---
## Scope summary
C7 is a **cross-cutting integration row** rather than a per-component candidate row: it pins how the C1 VIO learned-frontend (if any), C2 VPR backbone, and C3 matcher actually run on the Jetson Orin Nano Super under JetPack 6 — TensorRT vs ONNX Runtime+TRT EP vs pure PyTorch FP16. Per the user-pinned scope (locked via `/autodev` AskQuestion 2026-05-08 — see `_docs/_autodev_state.md` `c7_breadth=B`, `c7_quantization=A`, `c7_overkill_options=A`), three documentary candidate rows are evaluated: **TensorRT native primary** + **ONNX Runtime + TensorRT EP interop alternate** + **pure PyTorch FP16 mandatory simple-baseline**. INT8 primary + FP16 fallback per candidate; INT8-only candidates Experimental until calibration data exists. Triton / DeepStream / CUDA-Python custom kernels noted-and-rejected in one sentence (server/video-pipeline class or out-of-budget for embedded 8 h mission). Cand-row candidates inherit and propagate Plan-phase gates already opened by C2 (D-C2-5 DINOv2 ViT-export to TensorRT FP16/INT8) and C3 (D-C3-2 LightGlue inference runtime path).
---
## Sources
### Source #99 — NVIDIA TensorRT 10.x official documentation portal (context7-indexed)
**Title**: NVIDIA TensorRT — SDK for optimizing and accelerating deep learning inference on NVIDIA GPUs (mixed precision, dynamic shapes, transformer optimizations)
**Tier**: L1 — official authoritative SDK documentation (NVIDIA primary)
**URL**: <https://docs.nvidia.com/deeplearning/tensorrt/latest/> + context7 indexed at `/websites/nvidia_deeplearning_tensorrt`
**Access date**: 2026-05-08
**Direct verification**: ✅ context7 query "INT8 calibration EntropyCalibrator2 ICudaEngine deserialize Jetson Orin Nano FP16 mixed precision deployment workflow Python builder" returned 9371 code snippets at Source Reputation High + Benchmark Score 75.25.
**Key APIs verified**:
- **INT8 calibrator hierarchy**: `nvinfer1::IInt8Calibrator` (abstract base) + `nvinfer1::IInt8EntropyCalibrator` (deprecated) + `nvinfer1::IInt8EntropyCalibrator2` (current canonical) + `nvinfer1::IInt8MinMaxCalibrator`. Each defines `getBatchSize()` + `getBatch(void* bindings[], const char* names[], int32_t nbBindings)` + `readCalibrationCache(size_t& length)` + `writeCalibrationCache(const void* ptr, size_t length)` + `getAlgorithm()` returning `kENTROPY_CALIBRATION_2` for the canonical path.
- **Python builder INT8 enable pattern** (canonical TensorRT 10.x):
```python
config.set_flag(trt.BuilderFlag.INT8)
config.int8_calibrator = Int8_calibrator
Int8_calibrator = EntropyCalibrator(["input_node_name"], batchstream)
```
- **Mixed-precision flag pattern**: `config.set_flag(trt.BuilderFlag.FP16)` + `config.set_flag(trt.BuilderFlag.INT8)` for combined FP16+INT8 mixed precision (TensorRT auto-selects per-layer precision based on calibration data).
**Use**: backs Fact #94 (TensorRT native primary candidate) per-mode API verification block + Plan-phase D-C7-1 calibration-dataset-strategy + D-C7-2 mixed-precision flag matrix.
---
### Source #100 — Microsoft ONNX Runtime official documentation (context7-indexed) + Jetson AI Lab community wheel index
**Title**: Microsoft ONNX Runtime — cross-platform ML inference and training accelerator with TensorRT execution provider; Jetson-specific install path via Jetson AI Lab community PyPI index
**Tier**: L1 — official authoritative SDK documentation (Microsoft primary) + L2 community-maintained Jetson wheel index
**URL**: <https://onnxruntime.ai/> + context7 indexed at `/microsoft/onnxruntime` (v1.25.0) + <https://pypi.jetson-ai-lab.io/jp6/cu126/> + <https://github.com/dusty-nv/jetson-containers/issues/1283> + <https://github.com/microsoft/onnxruntime/issues/20503> + <https://github.com/microsoft/onnxruntime/issues/27562>
**Access date**: 2026-05-08
**Direct verification**: ✅ context7 query "TensorRT execution provider TrtFp16Enable TrtInt8Enable TrtCachePath onnxruntime-gpu Jetson ARM64 inference session options" returned 1462 code snippets at Source Reputation High + Benchmark Score 82.23 (highest of the 3 C7 candidate context7 lookups).
**Key APIs verified**:
- **Provider enumeration + config pattern** (canonical Python API):
```python
import onnxruntime as ort
print(ort.get_available_providers())
tensorrt_options = {'device_id': 0, 'trt_max_workspace_size': 2147483648, 'trt_fp16_enable': True}
cuda_options = {'device_id': 0, 'arena_extend_strategy': 'kNextPowerOfTwo', 'gpu_mem_limit': 2 * 1024 * 1024 * 1024}
session_trt = ort.InferenceSession(
"model.onnx",
providers=[('TensorrtExecutionProvider', tensorrt_options), ('CUDAExecutionProvider', cuda_options), 'CPUExecutionProvider']
)
```
- **Provider-cascade behavior**: ORT TRT EP attempts to optimize each subgraph via TensorRT; falls back to CUDA EP for unsupported ops; falls back to CPU EP if neither GPU EP applies. Subgraph fallback is automatic and per-op transparent.
**Jetson install constraints (CRITICAL)**:
- **Standard `pip install onnxruntime-gpu` does NOT work on Jetson Tegra** — Microsoft does not publish prebuilt aarch64 wheels with CUDA/TensorRT EPs (per Issue #20503: "NVIDIA does not have CI infrastructure to publish them").
- **Canonical install path (JetPack 6 + CUDA 12.6 + Ubuntu 22.04)**: `pip3 install onnxruntime-gpu --index-url https://pypi.jetson-ai-lab.io/jp6/cu126`.
- **Alternate index (CUDA 12.9 + Ubuntu 24.04)**: `pip3 install onnxruntime-gpu --index-url https://pypi.jetson-ai-lab.io/jp6/cu129`.
- **Known incompatibility**: onnxruntime-gpu v1.23.0 wheels for JetPack 6 were built against `numpy<2.0.0`; importing under `numpy>=2.0.0` raises a compatibility error per Issue #27562. Pin numpy<2 in project requirements until upstream rebuild is published.
- **Standard pip install `onnxruntime` (CPU-only) succeeds but exposes only `CPUExecutionProvider` and `AzureExecutionProvider`** — does NOT include CUDA EP or TensorRT EP.
**Use**: backs Fact #95 (ONNX Runtime + TensorRT EP interop alternate candidate) per-mode API verification block + Plan-phase D-C7-3 ORT-Jetson-wheel-pin + D-C7-4 numpy-version-pin.
---
### Source #101 — PyTorch official documentation (context7-indexed) + Jetson AI Lab PyTorch wheel availability for JetPack 6
**Title**: PyTorch — open-source machine learning framework (tensor computation with strong GPU acceleration; tape-based autograd); Jetson-specific wheels available via Jetson AI Lab + NVIDIA forums
**Tier**: L1 — official authoritative SDK documentation (PyTorch Foundation primary) + L1 NVIDIA Developer Forums (canonical Jetson PyTorch distribution channel)
**URL**: <https://pytorch.org/docs/stable/amp.html> + context7 indexed at `/pytorch/pytorch` (v2.5.1, v2.8.0, v2.9.1, v2.11.0) + <https://forums.developer.nvidia.com/t/installing-pytorch-for-jetpack-6-2/349519> + <https://forums.developer.nvidia.com/t/jetpack-6-2-and-pytorch-2-6-0-on-jetson-nano-orin/331972>
**Access date**: 2026-05-08
**Direct verification**: ✅ context7 query "torch.cuda.amp.autocast half precision FP16 inference mode no_grad CUDA Jetson Orin ARM64 model.half() torch.compile inference deployment" returned 4866 code snippets at Source Reputation High + Benchmark Score 76.69.
**Key APIs verified**:
- **`torch.amp.autocast(device_type, dtype, enabled, cache_enabled)`** — canonical AMP context manager (since PyTorch 1.10). Replaces deprecated `torch.cuda.amp.autocast`. Inference pattern:
```python
with torch.no_grad():
with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=True):
output = model(input)
```
- **`torch.compile(model, backend='inductor')`** — graph-mode optimization for further speedup; tradeoff is cold-start compile cost (~10-60 sec depending on model complexity).
- **`model.half()`** — eager-mode FP16 weight conversion (full-precision FP16 throughout, vs autocast's per-op precision selection).
**Jetson install constraints**:
- **Standard `pip install torch` does NOT include CUDA support on Jetson** — must use NVIDIA-published or Jetson AI Lab community wheels.
- **JetPack 6.2 + CUDA 12.6 + Ubuntu 22.04 + Python 3.10 canonical wheel**: `torch-2.9.0-cp310-cp310-linux_aarch64.whl` from Jetson AI Lab (per NVIDIA forum recommendation). Earlier stable combination: PyTorch 2.5 + torchvision 0.20.
- **Known dependency issues**: missing `libcudss.so.0` and `libnvdla_runtime.so` on PyTorch 2.9 cu129 wheel under JetPack 6.2 (CUDA 12.6) — version mismatch between wheel build target and installed JetPack CUDA. Mitigation: prefer the cu126 variant for JetPack 6.2.
- **CUDA capability**: Jetson Orin Nano Super GPU = compute capability **SM 87** (Ampere class).
**Use**: backs Fact #96 (pure PyTorch FP16 mandatory simple-baseline candidate) per-mode API verification block + D-C7-5 PyTorch-Jetson-wheel-pin.
---
### Source #102 — Ultralytics YOLO26 benchmark suite on Jetson Orin Nano Super (April 2026)
**Title**: Update NVIDIA Jetson Orin Nano Super benchmarks with YOLO26 (Ultralytics 8.4.33; commit 8d4e6e8 April 2026)
**Tier**: L1 — official authoritative benchmark suite (Ultralytics is the canonical YOLO maintainer)
**URL**: <https://github.com/ultralytics/ultralytics/pull/24097> + <https://github.com/ultralytics/ultralytics/commit/8d4e6e841c89f6598b322695cb2bc816eeba8b93>
**Access date**: 2026-05-08
**Direct verification**: ✅ Web search results explicitly cite the per-export-format inference times measured on Jetson Orin Nano Super.
**Key data extracted (YOLO26n on Jetson Orin Nano Super, April 2026 measurement)**:
| Export format | Inference time (ms) | mAP50-95 | Speedup vs FP32 | Accuracy delta vs FP16 |
|---|---|---|---|---|
| TensorRT FP32 | 7.53 | 0.4770 | 1.00× | — |
| TensorRT FP16 | 4.57 | 0.4800 | 1.65× | baseline (slightly higher than FP32 due to noise) |
| TensorRT INT8 | 3.80 | 0.4490 | 1.98× | **-6.5% mAP50-95** |
**Key data extracted (YOLOv8s on Jetson Orin Nano, NVIDIA forum)**:
- **INT8**: ~157 QPS (~6.4 ms/inference)
- **FP16**: ~103 QPS (~9.7 ms/inference)
- **INT8 vs FP16 speedup**: ~1.5× (vs ~1.20× on YOLO26n — model architecture and memory bandwidth dependent)
**Use**: backs Fact #94 (TensorRT) latency claims for object-detection-class CNN backbones on Jetson Orin Nano Super; provides empirical anchor for the engine's "INT8 primary + FP16 fallback" precision strategy. Caveat: YOLO is a detection network; feature-matching networks (LightGlue / DISK / XFeat) are known to be more quantization-sensitive (see Source #103).
---
### Source #103 — LightGlue ONNX Runtime + TensorRT acceleration (canonical reference) + FP8 ModelOpt quantization findings (Fabio Sim's Journal)
**Title**: Accelerating LightGlue Inference with ONNX Runtime and TensorRT (Fabio Sim's Journal, canonical author of `fabio-sim/LightGlue-ONNX`) + FP8 Quantized LightGlue in TensorRT with NVIDIA Model Optimizer (subsequent post)
**Tier**: L1 — canonical author of the canonical LightGlue ONNX/TensorRT export pathway (already cited as Source #73 in C3 row)
**URL**: <https://fabio-sim.github.io/blog/accelerating-lightglue-inference-onnx-runtime-tensorrt/> + <https://fabio-sim.github.io/blog/fp8-quantized-lightglue-tensorrt-nvidia-model-optimizer/> + <https://github.com/qdLMF/LightGlue-with-FlashAttentionV2-TensorRT> (community Jetson Orin NX TensorRT 8.5.2 + FlashAttentionV2 plugin reference implementation)
**Access date**: 2026-05-08
**Direct verification**: ✅ Web search results explicitly cite the 2-4× ONNX Runtime + TensorRT speedup over compiled PyTorch and the FP8 5.97× / 0.32× engine-size results.
**Key data extracted**:
- **LightGlue (transformer-based feature matcher) — ONNX Runtime + TensorRT inference**: 2-4× speedup over compiled PyTorch across various batch sizes and sequence lengths.
- **FP8 quantized LightGlue (NVIDIA ModelOpt) on Hopper/Ada/Blackwell**:
- Engine size ~0.32× of FP32 (~68% smaller).
- Up to 5.97× speedup vs FP32.
- **Material accuracy degradation**: "match counts dropped. Sometimes they dropped hard." This is qualitatively different from YOLO-class detection networks where INT8 is well-tolerated.
- **FP8 hardware support**: requires Hopper / Ada / Blackwell architecture. **Jetson Orin Nano Super is Ampere (SM 87) — NOT FP8-native**. FP8 ModelOpt path applies only via INT8 emulation fallback on Ampere.
- **Two FP8 formats**: E4M3 (4 exponent bits + 3 mantissa bits, better precision for activations) + E5M2 (5 exponent bits + 2 mantissa bits, better dynamic range for gradients).
- **Community Jetson reference implementation**: `qdLMF/LightGlue-with-FlashAttentionV2-TensorRT` deploys on Jetson Orin NX 8 GB with TensorRT 8.5.2 + custom FlashAttentionV2 plugin.
**Use**: backs Fact #94 (TensorRT) feature-matching-network INT8 caveat; backs the "INT8-only candidates Experimental until calibration data exists" engine ruling per user-pinned `c7_quantization=A` scope; raises Plan-phase gate D-C7-6 INT8-vs-FP16-per-model-family-precision-policy.
---
### Source #104 — JetPack SDK release notes (NVIDIA official) — JetPack 6.0 / 6.1 / 6.2 version matrix
**Title**: NVIDIA JetPack 6.x SDK Release Notes — TensorRT/CUDA/cuDNN versions per release; Super Mode introduction in JetPack 6.2 (January 2025)
**Tier**: L1 — official authoritative release notes (NVIDIA Developer)
**URL**: <https://developer.nvidia.com/embedded/jetpack-sdk-60> + <https://developer.nvidia.com/embedded/jetpack-sdk-61> + <https://developer.nvidia.com/embedded/jetpack-sdk-62> + <https://developer.nvidia.com/blog/nvidia-jetpack-6-2-brings-super-mode-to-nvidia-jetson-orin-nano-and-jetson-orin-nx-modules/>
**Access date**: 2026-05-08
**Direct verification**: ✅ Web search results explicitly enumerate TensorRT / CUDA / cuDNN per JetPack release.
**Key data extracted**:
| JetPack | CUDA | TensorRT | cuDNN | Super Mode | Released |
|---|---|---|---|---|---|
| 6.0 | 12.2 | 8.6 | 8.9 | No | early 2024 |
| 6.1 | 12.6 | 10.3 | 9.3 | MAXN mode (dev kit only) | mid-2024 |
| **6.2** | **12.6** | **10.3** | **9.3** | **YES — Orin Nano Super + Orin NX production modules** | **2025-01-16** |
- **Super Mode performance gains** (vs base Orin Nano): up to 2× higher generative AI inference performance, 70% AI TOPS increase, 50% memory bandwidth boost.
- **TensorRT 10.3** is the canonical inference runtime version for JetPack 6.1 / 6.2 deployments. Major API upgrade from TensorRT 8.x → 10.x — `IInt8EntropyCalibrator2` API surface is preserved; `INetworkDefinition` and `IBuilderConfig` semantics unchanged.
**Use**: pins the project's target software stack to **JetPack 6.2 + CUDA 12.6 + TensorRT 10.3 + cuDNN 9.3 + Super Mode enabled** for the Jetson Orin Nano Super target hardware. Backs Facts #94, #95, #96 deployability claims.
---
### Source #105 — TensorRT-on-Jetson canonical install constraints (Ultralytics issue reports + NVIDIA forum)
**Title**: TensorRT 10.x on Jetson Orin Nano — install path, hardware-specificity, memory-pressure-during-build constraints
**Tier**: L2 — community-reported issues with NVIDIA-acknowledged root causes (high signal-to-noise on canonical constraints)
**URL**: <https://github.com/ultralytics/ultralytics/issues/18882> ("TensorRT does not currently build wheels for Tegra systems") + <https://forums.developer.nvidia.com/t/tensorrt-10-7-0-on-orin-nano/364236> (SM 87 compute-capability mismatch) + <https://github.com/ultralytics/ultralytics/issues/18730> (laptop-GPU-built engine cannot load on Jetson) + <https://github.com/ultralytics/ultralytics/issues/21281> (TensorRT export memory pressure on Orin AGX)
**Access date**: 2026-05-08
**Direct verification**: ✅ Web search returned direct issue links with NVIDIA-confirmed root causes.
**Key constraints extracted** (CRITICAL for C7 deployment design):
1. **TensorRT Python wheels are NOT installed via pip on Jetson Tegra**. Standard `pip install tensorrt` raises: `RuntimeError: TensorRT does not currently build wheels for Tegra systems`. The canonical install path is the JetPack-bundled TensorRT (already present after `apt install nvidia-jetpack`), accessed via the system Python at `/usr/lib/python3.10/dist-packages/tensorrt`.
2. **TensorRT engines are hardware-specific** — engines built against a laptop / dev-machine GPU CANNOT be loaded on the Jetson at runtime. **Engines must be built directly on the Jetson target**.
3. **GPU compute capability mismatch is silent at build-time, fatal at load-time**: laptop GPUs (e.g., RTX 4090 = SM 89) and Jetson Orin Nano Super (SM 87) produce incompatible engines; the build emits no error, the load logs `Target GPU SM 87 is not supported by this TensorRT release` — version-and-SM-compatibility matrix must be respected.
4. **TensorRT engine builds on Jetson under memory pressure can segfault during tactic profiling** (8 GB shared CPU+GPU is tight; a rich layer-fusion search consumes peak RAM during `tactic.profile` phase). Mitigation: limit `config.max_workspace_size` to a fraction of the budget (e.g., 1-2 GB) and avoid concurrent inference / Postgres / FAISS during builds.
5. **JetPack 6.x ships the canonical TensorRT version** (TensorRT 10.3 for JP 6.1/6.2 per Source #104); upgrading TensorRT independently of JetPack is not officially supported.
**Use**: drives D-C7-7 build-on-Jetson-vs-prebuilt-engine-shipping-strategy + D-C7-8 max-workspace-size-cap-for-build-stability + D-C7-9 SM-compatibility-version-pin.
---
(Subsequent sources #106+ added during fact extraction below as candidate-specific evidence is gathered. Closure target: 3 candidate rows + 1 cross-cutting integration matrix.)
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# Source Registry — C8: MAVLink / MSP2 FC adapter
> Mode A Phase 2 — engine Step 2 (Source Tiering & Exhaustive Web Investigation). C8 batch 1 sources for the FC adapter (per-FC adapter pattern verified at SQ6 closure: ArduPilot Plane via MAVLink `GPS_INPUT`, iNav via `MSP2_SENSOR_GPS` primary OR UBX-impersonation alternate). Confidence labels per `references/source-tiering.md`. Cross-references back to SQ6 fact card sources (#4, #9, #10, #12, #13, #15) where the iNav inbound-handler reality and MSP2/UBX transport options were originally established.
>
> Index: [`00_summary.md`](00_summary.md). Sibling component categories: [C1 VIO](C1_vio.md), [C2 VPR](C2_vpr.md), [C3 Matchers](C3_matchers.md), [C4 Pose](C4_pose_estimation.md), [C5 State estimator](C5_state_estimator.md), [C6 Tile cache](C6_tile_cache_spatial_index.md), [C7 Inference runtime](C7_inference_runtime.md). Cross-cuts: [SQ6 external positioning](SQ6_external_positioning.md).
## Sources
### Source #106 — ArduPilot Pymavlink (context7-indexed `/ardupilot/pymavlink`)
- **Tier**: L1 (canonical Python MAVLink implementation maintained by ArduPilot)
- **Found via**: context7 `resolve-library-id` → `/ardupilot/pymavlink` → `query-docs` for GPS_INPUT send patterns
- **Library posture**: 32 code snippets indexed in context7 (Source Reputation: High); coverage emphasizes the JavaScript MAVLink generator output, with thinner Python-side examples in context7 — supplementary primary sources (canonical pymavlink GitHub README + ArduPilot GPS_INPUT dev docs Source #107) carry the canonical Python `master.mav.gps_input_send(...)` send pattern.
- **License**: LGPL v3 (pymavlink itself); MAVLink generated dialects are MIT — the project's runtime dependency is on the LGPL pymavlink Python package. **Compatible with project's Apache-2.0 dual-use track**: LGPL allows linking from a non-LGPL application without "infecting" application license; the only obligation is to publish/redistribute any modifications to pymavlink itself (project does not modify pymavlink), and to allow users to relink against an updated pymavlink (trivially satisfied for an open-source / company-internal deployment with published `requirements.txt`).
- **Critical-novelty-sensitivity**: Established baseline; no time window — pymavlink has been the canonical Python MAVLink stack since 2010+, and `GPS_INPUT` (msg 232) has been in `common.xml` since 2017 ArduPilot dev iteration.
- **Per-mode capability verification (context7 + SQ6 Source #4 AP_GPS_MAV.cpp cross-cite)**: ✅ `GPS_INPUT` decoder confirmed in AP_GPS_MAV.cpp master per SQ6 Fact #1; Python sender uses `master = mavutil.mavlink_connection(...)` + `master.mav.gps_input_send(time_usec, gps_id, ignore_flags, time_week_ms, time_week, fix_type, lat, lon, alt, hdop, vdop, vn, ve, vd, speed_accuracy, horiz_accuracy, vert_accuracy, satellites_visible, yaw)` per pymavlink generated dialect.
- **Used to support**: Fact #97 (ArduPilot Plane FC adapter primary candidate).
### Source #107 — ArduPilot Plane Non-GPS Position Estimation + MAVProxy GPS Input module documentation
- **Tier**: L1 (official ArduPilot dev docs portal; documented configuration + canonical injection example)
- **Found via**: web search for `pymavlink GPS_INPUT msg 232 example ArduPilot Plane non-GPS external positioning companion computer 2025`
- **Date accessed**: 2026-05-08
- **URLs**:
- https://ardupilot.org/dev/docs/mavlink-nongps-position-estimation.html
- https://ardupilot.org/plane/docs/common-non-gps-navigation-landing-page.html
- https://ardupilot.org/mavproxy/docs/modules/GPSInput.html
- https://ardupilot.org/plane/docs/common-companion-computers.html
- **Critical configuration captured**: `GPS1_TYPE = 14` (MAVLink) is required on the FC for `GPS_INPUT` ingestion. Without this parameter set, AP_GPS will not accept the message. `EK3_SRC1_POSXY = 3` (GPS) selects the GPS_INPUT-fed virtual GPS as the primary horizontal-position source. Per ArduPilot dev docs, the **preferred method** for non-GPS navigation is `ODOMETRY` or `VISION_POSITION_ESTIMATE` at ≥4 Hz — but `GPS_INPUT` remains supported and is the right choice when the project's outcome contract is "WGS84 coordinates as a real-GPS replacement" (AC-4.3 wording aligns with GPS_INPUT semantics, not ODOMETRY semantics).
- **Cross-cite**: SQ6 Fact #1 (AP_GPS_MAV.cpp ingestion path) + SQ6 Fact #4 (`ODOMETRY`-velocity-only NOT supported) — together these pin `GPS_INPUT` as the right transport for the project's `{satellite_anchored, visual_propagated, dead_reckoned}` source-label scheme.
- **Per-mode capability verification**: ✅ All required ACs (AC-4.3 / AC-NEW-2 / AC-NEW-4 / AC-NEW-8) map directly into GPS_INPUT field semantics per SQ6 working summary table.
### Source #108 — pyubx2 (context7-indexed `/semuconsulting/pyubx2` + canonical GitHub README)
- **Tier**: L1 (canonical Python UBX/NMEA/RTCM3 parser; benchmark score 86.8 in context7; 139 code snippets)
- **Found via**: context7 `resolve-library-id` → `/semuconsulting/pyubx2` → `query-docs` for UBX-NAV-PVT message construction with full attribute control + serialize-to-bytes pattern for UART transmission
- **Library posture**: BSD-3-Clause license (clean, dual-use compatible); semuconsulting publishes both the canonical GitHub repo + comprehensive readthedocs.io documentation also indexed in context7 as `/websites/semuconsulting_pyubx2` (239 additional code snippets, benchmark 85.2). The library supports `UBXMessage(ubxClass, ubxID, mode, **kwargs)` constructor with three modes: `GET (0x00)` for output from the receiver, `SET (0x01)` for command input, `POLL (0x02)` for query input. NAV-PVT belongs to the GET output set.
- **Critical-novelty-sensitivity**: Library/SDK API behaviour — must reflect currently shipped version; semuconsulting/pyubx2 is daily-active (last released 2025).
- **Per-mode capability verification (context7-confirmed)**: ✅ NAV-PVT message construction with all UBX-NAV-PVT fields supported as keyword arguments per `UBXMessage('NAV', 'NAV-PVT', GET, iTOW=..., year=..., lon=..., lat=..., height=..., hMSL=..., hAcc=..., vAcc=..., velN=..., velE=..., velD=..., gSpeed=..., headMot=..., sAcc=..., headAcc=..., pDOP=..., fixType=..., flags=..., numSV=..., valid=...)`. ✅ `serialize()` method returns the full UBX wire-format bytestring (sync-bytes 0xB5 0x62 + class + ID + length + payload + 8-bit Fletcher checksum). ✅ `parsebitfield=1` mode allows individual bit attributes for `flags` (e.g., `gnssFixOK`, `diffSoln`, `psmState`) and `valid` (e.g., `validDate`, `validTime`, `fullyResolved`, `validMag`) — required for the impersonation path to set the `gnssFixOK` bit that iNav's `gpsMapFixType()` validates.
- **Used to support**: Fact #98 (iNav UBX impersonation alternate candidate).
### Source #109 — u-blox NEO-M9N Integration Manual (UBX-19014286) + u-blox 8/M8 Receiver Description (UBX-13003221) — UBX-NAV-PVT canonical specification
- **Tier**: L1 (vendor-authoritative protocol specification PDFs)
- **Found via**: web search for `UBX-NAV-PVT frame structure spec u-blox protocol M8 M9 fix type fabricate inject iNav 2025`
- **Date accessed**: 2026-05-08
- **URLs**:
- https://content.u-blox.com/sites/default/files/NEO-M9N_Integrationmanual_UBX-19014286.pdf
- https://content.u-blox.com/sites/default/files/products/documents/u-blox8-M8_ReceiverDescrProtSpec_UBX-13003221.pdf
- **Frame structure captured**: NAV-PVT (class=0x01, ID=0x07) carries 92-byte payload — `iTOW (u32 ms)` + `year (u16)` + `month/day/hour/min/sec (u8 each)` + `valid (u8 bitmask)` + `tAcc (u32 ns)` + `nano (i32 ns)` + `fixType (u8 enum: 0=NoFix, 1=DeadReck, 2=2D, 3=3D, 4=GNSS+DR, 5=TimeOnly)` + `flags (u8 bitmask incl. gnssFixOK bit 0)` + `flags2 (u8)` + `numSV (u8)` + `lon (i32 deg×1e-7)` + `lat (i32 deg×1e-7)` + `height (i32 mm above ellipsoid)` + `hMSL (i32 mm above mean sea level)` + `hAcc (u32 mm)` + `vAcc (u32 mm)` + `velN/velE/velD (i32 each mm/s)` + `gSpeed (i32 mm/s)` + `headMot (i32 deg×1e-5)` + `sAcc (u32 mm/s)` + `headAcc (u32 deg×1e-5)` + `pDOP (u16 ×0.01)` + reserved bytes + `headVeh (i32)` + `magDec (i16)` + `magAcc (u16)`. M9N supersedes M8 with refined NAV-PVT semantics; both are accepted by iNav 9.0 (per Source #11 in SQ6 — UBX ≥ 15.00 protocol version).
- **Critical-novelty-sensitivity**: Established baseline + library/SDK API behaviour — u-blox NAV-PVT is a stable protocol surface since u-blox 8 (2014); minor field semantics evolve across vendor protocol versions, so exact wire format must be checked against the iNav-target version (iNav 9.0 expects ≥ 15.00).
- **Per-mode capability verification**: ✅ NAV-PVT contains all fields needed for iNav's `gpsMapFixType()` validation (Source #110 cross-cite): `flags` byte bit 0 `gnssFixOK` + `fixType` enum + `numSV` + `hAcc/vAcc` for AC-NEW-4 covariance honesty.
- **Used to support**: Fact #98 (iNav UBX impersonation alternate candidate) NAV-PVT frame fabrication spec.
### Source #110 — iNav `gps_ublox.c` source (master, GitHub) — UBX validation gates that the impersonation must pass
- **Tier**: L1 (canonical iNav firmware source, master branch, accessed via cached web fetch)
- **Found via**: web search for `iNav GPS UBX validation fixType numSat hDOP threshold reject GNSS spoofing companion computer 2025`
- **URL**: https://github.com/iNavFlight/inav/blob/master/src/main/io/gps_ublox.c
- **Date accessed**: 2026-05-08
- **Critical-novelty-sensitivity**: Library/SDK API behaviour — must reflect current shipped iNav version. iNav 9.0 master (post-2025-12-11 wiki update per SQ6 Source #10) confirmed via direct file read.
- **Validation logic captured (line-numbered evidence)**:
- **Line 215-220**: `gpsMapFixType(fixValid, ubloxFixType)` returns `GPS_FIX_2D` if `fixValid && ubloxFixType == FIX_2D`, returns `GPS_FIX_3D` if `fixValid && ubloxFixType == FIX_3D`, otherwise `GPS_NO_FIX`. **THIS IS THE GATE** the impersonation must pass.
- **Line 654**: NAV-PVT path computes `next_fix_type = gpsMapFixType(_buffer.pvt.fix_status & NAV_STATUS_FIX_VALID, _buffer.pvt.fix_type)`. The `fix_status & NAV_STATUS_FIX_VALID` masks the lowest bit of NAV-PVT's `flags` byte (bit 0 = `gnssFixOK`).
- **Lines 656-683**: NAV-PVT-driven full state population including `lon (1e-7 deg)`, `lat (1e-7 deg)`, `altitude_msl (mm)`, NED velocity (mm/s converted to cm/s), `speed_2d (mm/s)`, `heading_2d (deg×1e-5 → deg×10)`, `satellites`, `horizontal_accuracy (mm)`, `vertical_accuracy (mm)`, `position_DOP`, valid date/time bits.
- **Lines 1024-1060**: Configuration logic — for u-blox version ≥ 15.0 (iNav 9.0+), iNav configures NAV-PVT-only via `configureMSG(MSG_CLASS_UBX, MSG_PVT, 1)`. For older receivers, configures the legacy NAV-POSLLH + NAV-SOL + NAV-VELNED + NAV-TIMEUTC quad. **Implication**: companion impersonator should advertise version ≥ 15.0 via NAV-VER (CLASS=0x0A, ID=0x04) to drive iNav into the simpler NAV-PVT-only protocol.
- **Per-mode capability verification**: ✅ Validation gate fully decoded; impersonation viability confirmed at the firmware-source level (no opaque downstream filter discovered).
- **Used to support**: Fact #98 — provides the iNav-firmware-side validation contract that the UBX impersonation must satisfy.
### Source #111 — iNav `docs/development/msp/README.md` (master, GitHub) — MSP2_SENSOR_GPS canonical payload specification
- **Tier**: L1 (canonical iNav protocol-reference documentation, master branch, accessed via cached web fetch)
- **Found via**: web search for `MSP2_SENSOR_GPS Python library iNav msp2 protocol companion computer external GPS injection 2025 2026`
- **URL**: https://github.com/iNavFlight/inav/blob/master/docs/development/msp/README.md
- **Date accessed**: 2026-05-08
- **Payload structure captured (line 2999-3031 of the master README)**: `MSP2_SENSOR_GPS (7939 / 0x1F03)` — request payload 36 bytes containing `instance (u8)` + `gpsWeek (u16)` + `msTOW (u32 ms)` + `fixType (u8 = gpsFixType_e)` + `satellitesInView (u8)` + `hPosAccuracy (u16 mm)` + `vPosAccuracy (u16 mm)` + `hVelAccuracy (u16 cm/s)` + `hdop (u16 ×0.01)` + `longitude (i32 deg×1e7)` + `latitude (i32 deg×1e7)` + `mslAltitude (i32 cm)` + `nedVelNorth (i32 cm/s)` + `nedVelEast (i32 cm/s)` + `nedVelDown (i32 cm/s)` + `groundCourse (u16 deg×100)` + `trueYaw (u16 deg×100, 65535 = unavailable)` + `year (u16)` + `month/day/hour/min/sec (u8 each)`. **Reply payload: None.** **Notes: Requires `USE_GPS_PROTO_MSP`. Calls `mspGPSReceiveNewData()`.**
- **Critical-novelty-sensitivity**: Library/SDK API behaviour — verified against iNav master (post-9.0).
- **Per-mode capability verification**: ✅ Full payload spec covers all AC-NEW-4 covariance honesty fields (`hPosAccuracy`, `vPosAccuracy`, `hVelAccuracy`); ✅ AC-NEW-8 graceful-degrade signal carried via `fixType` enum (`gpsFixType_e`) — companion can emit `GPS_NO_FIX` (0) or `GPS_FIX_2D` (1) for the "covariance >100 m" / "covariance >500 m" thresholds; ✅ AC-1.4 95% covariance proxy carried in `hPosAccuracy`.
- **Used to support**: Fact #99 (iNav MSP2_SENSOR_GPS primary candidate).
### Source #112 — Python MSP2 implementations: YAMSPy + INAV-Toolkit `inav_msp.py`
- **Tier**: L2 (community implementations; NOT vendor-canonical but actively maintained)
- **Found via**: web search for Python MSP2_SENSOR_GPS libraries; iNav Issue #4465 confirms YAMSPy as community-recommended; agoliveira/INAV-Toolkit confirmed via direct GitHub source read
- **URLs**:
- YAMSPy mention: https://github.com/iNavFlight/inav/issues/4465
- INAV-Toolkit `inav_msp.py`: https://github.com/agoliveira/INAV-Toolkit/blob/5c4ef789068399b4dc7461b71c6f71c25aef5e4e/inav_msp.py
- **Date accessed**: 2026-05-08
- **Library posture**:
- **YAMSPy** (`thecognifly/YAMSPy`): MIT-licensed Python library with explicit MSP V2 support; community-blessed for iNav external-device communication per the iNav issue thread.
- **INAV-Toolkit `inav_msp.py`**: 951-line MIT-licensed module implementing `msp_v2_encode(cmd, payload)` + `msp_v2_decode(buffer)` with CRC-8 DVB-S2 checksumming + serial transport. Direct primary-source implementation reference for MSP V2 frame construction.
- **Critical-novelty-sensitivity**: Library/SDK API behaviour — both libraries are recent (post-2024 commits). **Risk**: community libraries may lag the iNav protocol surface (e.g., MSP V2 sensor message range 0x1F00-0x1FFF was added later than the original MSP V2 baseline). The project may need to either (a) extend the chosen community library with MSP2_SENSOR_GPS-specific encoding helpers, or (b) implement a thin custom encoder using the canonical msp_v2_encode primitive — both paths verified feasible from primary sources.
- **License notes**: MIT throughout — clean dual-use compatible.
- **Per-mode capability verification**: ⚠️ MSP V2 frame envelope (0x24 + 'X' + 0x3C + flag + cmd_lo + cmd_hi + len_lo + len_hi + payload + CRC8-DVB-S2) confirmed via INAV-Toolkit primary source; ✅ MSP2_SENSOR_GPS payload structure confirmed via Source #111. Combining the two yields a complete companion-side encoder for the iNav primary path.
- **Used to support**: Fact #99 (iNav MSP2_SENSOR_GPS primary candidate, Python implementation path).
### Source #113 — iNav `src/main/msp/msp_protocol_v2_sensor.h` (master, GitHub) — MSP2 sensor command-ID range
- **Tier**: L1 (canonical iNav firmware source, master branch)
- **Found via**: web search co-result with Source #112; opens via the `msp_protocol_v2_sensor.h` direct link
- **URL**: https://github.com/iNavFlight/inav/blob/master/src/main/msp/msp_protocol_v2_sensor.h
- **Date accessed**: 2026-05-08
- **Critical fact captured**: `MSP2_SENSOR_GPS = 0x1F03` (= 7939 decimal); MSP V2 sensor-message range `0x1F00-0x1FFF` is reserved for sensor injection plugins. iNav 9.0 master expectation: MSP2 frame must use the MSP V2 envelope (sync = 0x24 0x58 0x3C; flag = 0x00; cmd = LE 16-bit; len = LE 16-bit; CRC = CRC-8 DVB-S2 over flag through end of payload).
- **Per-mode capability verification**: ✅ MSP2_SENSOR_GPS = 0x1F03 confirmed at source; ✅ MSP V2 envelope spec confirmed.
- **Used to support**: Fact #99 — provides the canonical MSP V2 sensor-message-range definition.
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# Source Registry — Mode B Addendum (2026-05-08)
> Mode B Solution Assessment of `_docs/01_solution/solution_draft01.md`. New sources gathered for findings F1–F20; Mode A sources #1–#121 remain canonical and are not duplicated.
>
> Index: [`00_summary.md`](00_summary.md). Mode B fact cards: [`../02_fact_cards/MODEB_addendum.md`](../02_fact_cards/MODEB_addendum.md). Mode B fit-matrix revisions: [`../06_component_fit_matrix/MODEB_revisions.md`](../06_component_fit_matrix/MODEB_revisions.md). Mode B output: [`../../01_solution/solution_draft02.md`](../../01_solution/solution_draft02.md).
## New Sources
| # | Title | Tier | Binding |
|---|-------|------|---------|
| 122 | HKUST-Aerial-Robotics/VINS-Mono LICENCE file (canonical, master branch) — GNU GPL Version 3 | L1 (verified raw LICENCE on github.com) | C1 candidate-table license-correction (F11/F15). Confirms VINS-Mono is **GPL-3.0**, not BSD-permissive as draft01 claims. Cross-confirms Mode A C1 Fact #28 against Mode A draft01 deliverable. |
| 123 | MegaLoc — "One Retrieval to Place Them All" (Berton & Masone, arXiv:2502.17237; CVPR 2025 Image Matching workshop; gmberton/megaloc repo, MIT) | L1 | C2 D-C2-11 candidate (F16). torch.hub install path; MIT license; SOTA on multiple VPR datasets; combines existing methods + training techniques + datasets into a unified retrieval model. |
| 124 | UltraVPR — "Unsupervised Lightweight Rotation-Invariant Aerial VPR" (cbbhuxx/UltraVPR repo, MIT; published RAL 2025; ICRA 2026) | L1 | C2 D-C2-11 alternative (F17). MIT license; **44 Hz on Jetson Orin NX (close cousin of Orin Nano Super)** via ONNX export; rotation-invariant; specifically designed for UAV; validated on VPAir + UAV-VisLoc datasets — directly relevant to the project's pinned operating context. |
| 125 | AirZoo — "Unified Large-Scale Dataset for Grounding Aerial Geometric 3D Vision" (arXiv:2604.26567v1, 2026) | L1 | C2 evidence base for MegaLoc on aerial domain (F16). Demonstrates that fine-tuning MegaLoc on aerial data yields substantial performance gains for aerial image retrieval and cross-view matching. |
| 126 | NVD CVE-2026-1579 — MAVLink protocol Missing Authentication for Critical Function (CVSS 9.8 CRITICAL) | L1 | New cross-cutting security gate (F18). MAVLink lacks cryptographic authentication by default; an unauthenticated party with MAVLink interface access can send arbitrary commands including SERIAL_CONTROL for interactive shell. **Mitigation: enable MAVLink 2.0 message signing.** Affects ArduPilot Plane and PX4; iNav has only partial MAVLink support and does not implement message signing. |
| 127 | NVD CVE-2025-53644 — OpenCV uninitialized variable on stack reading crafted JPEG (CVSS 9.8 CRITICAL) | L1 | C4 OpenCV pin update (F19). Affects 4.10.0 / 4.11.0; **fixed in 4.12.0**. Draft01 says "OpenCV 4.x" — must pin **≥4.12.0**. Triggers heap-buffer-write via crafted JPEG file load — relevant if any image format reaching OpenCV originates from uncertain provenance (e.g., tile cache import, FDR thumbnail re-load). |
| 128 | ArduPilot MAVLink2 Signing — Plane documentation (`ardupilot.org/plane/docs/common-MAVLink2-signing.html`) + Issue #28736 channel-specific signing PR #29546 (March 2025) | L1 | F18 mitigation evidence. Confirms ArduPilot supports MAVLink2 signing via Mission Planner SETUP > Advanced > "Mavlink Signing" menu; non-USB serial ports can be configured to only respond to MAVLink commands carrying the correct passkey; PR #29546 adds bitmask parameter to enable/disable signing per channel for wired companion-computer connections. |
| 129 | iNav MAVLink Wiki (`iNavFlight/inav/wiki/Mavlink`) | L1 | F18 cross-FC asymmetry (verified 2026-05-08 via web search). iNav has partial MAVLink support and **does NOT implement MAVLink message signing**. Companion-FC inbound on iNav is MSP2 (not MAVLink) so signing-gap is on the outbound MAVLink telemetry side, not the inbound external-positioning path — but cross-FC asymmetry is still material for AC-NEW-7 and the GCS link. |
| 130 | ArduPilot common-ekf-sources.rst + PR #18345 (`MAV_CMD_SET_EKF_SOURCE_SET`) — explicit "no GCSs are currently known to implement this" (verified 2026-05-08) | L1 | F8 D-C8-2 evidence (cross-confirms Mode A SQ6 Fact #3). Re-verifies on 2026-05-08 web search that ArduPilot supports the command at firmware level (since August 2021) but **no production-deployed GCS or companion is documented as implementing the companion-driven switch pattern** the project plans to use. Pattern is therefore **novel for a deployed production system** — confirms Mode A characterization but elevates to risk-graded selection. |
| 131 | XoFTR — "Cross-modal Feature Matching Transformer" (arXiv:2404.09692) + 2026 SAR-optical satellite registration benchmark (arXiv:2604.10217) | L2 | F20 contrarian-evidence reference. Cross-modal matcher; achieved lowest mean error (3.0 px) on SpaceNet9 SAR-optical training scenes among 24 pretrained matcher families benchmarked. **Important contrarian finding: matchers without explicit cross-modal training sometimes performed comparably**, suggesting foundation-model features (like DINOv2) provide modality invariance — reinforces SelaVPR (DINOv2-L) over MixVPR (CNN-only) on the BSD/permissive C2 axis when cross-domain UAV→satellite registration is the binding stress test. |
---
## Verification audit-trail (mandatory per `00_question_decomposition.md` Step 0.5 cross-validation rule)
| Source | Independent corroboration |
|---|---|
| #122 (VINS-Mono GPL-3.0) | Cross-confirms Mode A C1 Fact #28 (`02_fact_cards/C1_vio.md`) which already classified VINS-Mono as GPL-3.0; the discrepancy was inside the deliverable layer (`solution_draft01.md` C1 candidate table), not the evidence layer. Both Mode A C1 Fact #28 and Source #122 agree. |
| #123 (MegaLoc) | arXiv preprint + CVPR 2025 workshop + GitHub repo + Hugging Face — three-independent-source confirmation per Critical-novelty cross-validation rule. |
| #124 (UltraVPR) | RAL 2025 IEEE journal publication + ICRA 2026 + GitHub repo with pre-trained ONNX weights — three independent sources. |
| #125 (AirZoo) | arXiv preprint April 2026 — single source; treated as ⚠️ Medium confidence pending second cross-validation. |
| #126 (CVE-2026-1579) | NVD official entry + CISA ICS Advisory ICSA-26-090-02 + PX4 GHSA-fh32-qxj9-x32f — three-source confirmation; Critical CVSS. |
| #127 (CVE-2025-53644) | NVD official entry; OpenCV release notes confirming 4.12.0 fix — two-source confirmation. |
| #128 (ArduPilot MAVLink2 signing) | Official Plane documentation + Issue #28736 + PR #29546 — three-source confirmation. |
| #129 (iNav no signing) | iNav wiki (frogmane edited 2025-12-11) — single authoritative source per project convention; iNav wiki is the canonical iNav reference per Mode A SQ6 source #10. |
| #130 (companion-driven EKF source switch) | ArduPilot official ekf-sources doc + PR #18345 + cross-confirms SQ6 Mode A Source #3 already-documented "no GCSs known to implement". Three-source confirmation. |
| #131 (XoFTR cross-modal) | arXiv preprint + 2026 SAR-optical benchmark study (arXiv:2604.10217) — two-source confirmation. |
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# Source Registry — SQ1 — Existing / competitor GPS-denied UAV navigation systems
> Mode A Phase 2 — engine Step 2 (Source Tiering & Exhaustive Web Investigation).
> Critical-novelty sensitivity per Step 0.5 in `../00_question_decomposition.md`. Time windows applied:
> - **Lead-candidate / SOTA claims**: prefer sources within last 6 months; up to 18 months if older is the official authority.
> - **Library/SDK API behaviour**: must reflect the currently shipped version at search time (`context7` mandatory per lead candidate).
> - **Established baselines** (KLT, RANSAC, EKF, ORB, SIFT, GTSAM): no time window.
>
> This file replaces a section of the previous monolithic `01_source_registry.md`. See `00_summary.md` for the full category index. Investigation order is tracked in `../00_question_decomposition.md` and the cross-category Investigation Status table in `00_summary.md`.
---
### Source #25
- **Title**: Twist Robotics develops OSCAR — a GPS-independent visual navigation system for drones resistant to electronic warfare equipment
- **Link**: https://www.pravda.com.ua/eng/news/2026/01/28/8018266/
- **Tier**: L2 (national newspaper of record reporting on a Technology Forces of Ukraine release; primary press is the Technology Forces of Ukraine FB post)
- **Publication Date**: 2026-01-28 (accessed 2026-05-07)
- **Timeliness Status**: Currently valid (within 6-month critical-novelty window)
- **Target Audience**: Ukraine-deployment practitioners; UAV companion-system designers
- **Research Boundary Match**: **Full match** — Ukrainian fixed-wing-class UAV, GPS-denied, vision-based, deployed in active conflict
- **Summary**: Twist Robotics (UA) deployed OSCAR ("Optical System of Coordinates with Automatic Relocalisation") — camera + landmark-matching + map → autopilot ingests as a "reliable GPS signal". Vendor claims: 20 m accuracy without cumulative error, day/night/fog operation, 500,000 km logged across 25,000 combat missions over 24 months development, AI-augmented + Obrii proprietary simulator for training. Note: hardware photo shows active cooling on the module — implies non-trivial compute (probably Jetson-class). **No public independent benchmark.** Closest deployed peer system to this project.
- **Related Sub-question**: SQ1 (closest peer); also informs SQ8 (anti-spoofing claims), SQ9 (synthesis)
### Source #26
- **Title**: Ukraine Gives Drones Vision-Based Navigation to Push Past Heavy Jamming — The Defense Post
- **Link**: https://thedefensepost.com/2026/01/29/ukraine-drones-vision-navigation/
- **Tier**: L2 (defense-trade publication; corroborates Source #25 with a second-party byline)
- **Publication Date**: 2026-01-29 (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Target Audience**: Defense-policy / procurement readership
- **Research Boundary Match**: Full match
- **Summary**: Confirms OSCAR is operational, terrain-imagery-against-mapped-landmarks pattern, autopilot-ingestion. Adds "live imagery" framing. No new technical detail beyond Source #25.
- **Related Sub-question**: SQ1
### Source #27
- **Title**: Ukraine's Ruta Missile Drone Will Get an EW-Immune Navigation System — Defense Express
- **Link**: https://en.defence-ua.com/weapon_and_tech/ukraines_ruta_missile_drone_will_get_an_ew_immune_navigation_system-14541.html
- **Tier**: L2 (defense-trade publication, Ukraine-domestic)
- **Publication Date**: 2025-05-17 (accessed 2026-05-07)
- **Timeliness Status**: Currently valid (within 18-month authority window)
- **Target Audience**: Defense-procurement / industry analysts
- **Research Boundary Match**: Partial — operational profile (cruise-missile-class, terminal guidance) differs from our 8-h fixed-wing surveillance/strike profile; technique class is closely related (DSMAC pattern)
- **Summary**: Destinus Ruta (Ukrainian-Swiss origin; ~300 km strike range, miniature cruise missile) will integrate a navigation system from UAV Navigation (Spanish, Grupo Oesía). Defense Express infers DSMAC-style operating principle: "takes images of surface mid-flight, identifies location through comparison with reference". Vendor announcement notes validation in Ukrainian combat conditions including GNSS-denied / jamming / spoofing. Establishes that the cruise-missile-tier vision-nav pattern is now being miniaturised for ~300 km strike drones.
- **Related Sub-question**: SQ1 (commercial/military landscape)
### Source #28
- **Title**: Kilometer-Scale GNSS-Denied UAV Navigation via Heightmap Gradients: A Winning System from the SPRIN-D Challenge
- **Link**: https://arxiv.org/abs/2510.01348
- **Tier**: L1 (peer-style preprint, full system description, real flight data, competition results)
- **Publication Date**: October 2025 (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: arXiv v1 (2510.01348v1)
- **Target Audience**: GNSS-denied UAV system designers (academic + practitioner)
- **Research Boundary Match**: **Partial — different regime.** Multirotor (≤25 kg), <25 m AGL, LiDAR-equipped, no satellite-tile basemap; 9 km waypoint mission. Our project is fixed-wing, ~1 km AGL, no LiDAR, monocular + sat-tile basemap. **Architectural pattern transfers; specific algorithm does NOT** (heightmap gradients require LiDAR).
- **Summary**: CTU Prague team won SPRIN-D Funke Fully Autonomous Flight Challenge with: VIO (OpenVINS) + LiDAR-derived local heightmap + gradient template matching against open-data DEM + clustered K-means particle filter, all on Intel NUC i7 16 GB CPU-only (no GPU). Achieved RMSE <11 m over kilometer-scale flights vs ≤53 m for raw odometry. Critical observations explicitly stated:
- **RTAB-Map and ORB-SLAM3 both fail** beyond 1 km / above 2 m/s flight (compute/memory) and ORB-SLAM3 loses tracking in textureless areas — directly applicable to our 17 m/s cruise over agricultural steppe.
- **"Some teams used RGB satellite image-based matching, but this has proved to be highly unreliable at such low altitudes."** This is a low-altitude (<25 m AGL) finding; our 1 km AGL operates in the high-altitude regime where the same paper notes RGB sat-matching "works reasonably well" (refs [5][6]).
- Lesson: "ability to recover from periods of high uncertainty and re-localize is more critical than maintaining consistently low instantaneous RMSE." Direct architectural input for AC-NEW-2 / AC-NEW-8.
- Lesson: IMU-from-airframe vibration isolation is mission-critical for VIO usability.
- Lesson: magnetometer is unreliable near steel-reinforced structures; sensor-fusion is essential for heading robustness.
- **Related Sub-question**: SQ1 + SQ5 (failure modes for VIO/SLAM at speed) + SQ2 (canonical pipeline)
### Source #29
- **Title**: Hierarchical Image Matching for UAV Absolute Visual Localization via Semantic and Structural Constraints
- **Link**: https://arxiv.org/abs/2506.09748 (PDF: https://arxiv.org/pdf/2506.09748)
- **Tier**: L1 (peer-submitted preprint, IEEE-bound, with public CS-UAV dataset)
- **Publication Date**: June 2025 (accessed 2026-05-07)
- **Timeliness Status**: Currently valid (within 6-month critical-novelty window for SOTA claims)
- **Version Info**: arXiv v1 (2506.09748v1)
- **Target Audience**: Academic SOTA researchers + UAV-localization implementers
- **Research Boundary Match**: **Full match** — exact same problem (UAV absolute visual localization in GNSS-denied conditions, downward-facing camera, satellite reference)
- **Summary**: 2025 SOTA pipeline: (1) image retrieval module (off-the-shelf, optimal-transport feature aggregation), (2) Semantic-Aware and Structure-Constrained Matching Module using **DINOv2** features + 4D correlation tensor + SoftMNN + 4D conv, (3) lightweight fine-grained module for pixel-level. Constructs UAV absolute visual-loc pipeline **without VIO/relative-loc dependence** (retrieval-and-matching only). Evaluation on AerialVL + their own CS-UAV. **Direct relevance**: this is a candidate template for our C2 (VPR) + C3 (cross-domain registration) components, but DINOv2 is a heavyweight foundation model — must be benchmarked under our 25 W / 8 GB Jetson Orin Nano envelope before selection (handed off to SQ3/SQ4 + SQ5 for that component).
- **Related Sub-question**: SQ1 (academic SOTA), SQ3+SQ4 (C2/C3 candidates), SQ5 (Jetson-on-Foundation-Model failure mode)
### Source #30
- **Title**: Raptor — GPS-Denied UAV Navigation & Coordinate Extraction (Vantor product page; Guide / Sync / Ace suite)
- **Link**: https://www.vantor.com/product/mission-solutions/raptor/
- **Tier**: L2 (vendor product spec; primary for the product itself, not for independent benchmark numbers)
- **Publication Date**: live (accessed 2026-05-07; references Mar 2026 + Dec 2025 + Sep 2025 partner blog posts indicating active product line)
- **Timeliness Status**: Currently valid
- **Target Audience**: Defense / commercial / industrial UAV integrators
- **Research Boundary Match**: **Full match** — vision-based aerial position software using existing camera + 3D terrain data, deployable on commodity hardware
- **Summary**: Vantor Raptor product family: **Guide** (on-drone vision-based positioning, demonstrated <7 m absolute accuracy in all dimensions, day/night/low-altitude, runs on commodity HW); **Sync** (georegisters live drone video against 3D terrain in real time, <3 m coordinate extraction); **Ace** (laptop-side coordinate extraction at <3 m). Backbone: Vantor's "100 million-plus sq km of highly accurate 3D terrain data, regularly updated" (Vivid Terrain, 3 m accuracy). Inertial Labs partnership (VINS-integrated Raptor Guide). Use cases include joint multi-domain ops, large-scale autonomous delivery, search-and-rescue. **This is the closest production-grade commercial peer to the project's architecture (sat-basemap-as-service + on-drone vision).**
- **Related Sub-question**: SQ1 (commercial), SQ3+SQ4 (commercial alternatives to building C2/C3 ourselves), SQ8 (basemap as a service vs offline cache)
### Source #31
- **Title**: Auterion successfully completes Artemis program to deliver long-range deep strike drone (press release)
- **Link**: https://auterion.com/auterion-successfully-completes-artemis-program-to-deliver-long-range-deep-strike-drone/
- **Tier**: L1 (official vendor press release)
- **Publication Date**: 2025-10-15 (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Target Audience**: Defense-procurement; UAV-integration architects
- **Research Boundary Match**: **Full match** — fixed-wing-class one-way attack drone with Ukraine-validated GPS-denied navigation; the system architecture is directly comparable
- **Summary**: Auterion Artemis (DIU project, completed Oct 2025) = Shahed-style design developed in Ukraine; up to 1,000-mile range; up to 40 kg warhead; runs on Auterion Skynode N mission computer + Auterion Visual Navigation system + built-in terminal guidance. Government evaluators signed off after operational flight tests in Ukraine including ground launch, GPS and GPS-denied navigation, long-range transit, and terminal engagement. **Establishes that the integration pattern (companion-class autopilot + visual navigation + terminal guidance) is shipping at production scale to a US defense customer.** Open architecture, manufacturing in US/UA/DE.
- **Related Sub-question**: SQ1
### Source #32
- **Title**: Bring AI and computer vision to small autonomous systems — Auterion Skynode S product page
- **Link**: https://auterion.com/product/skynode-s
- **Tier**: L2 (vendor product spec)
- **Publication Date**: live (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Target Audience**: Small-UAS integrators
- **Research Boundary Match**: Full match (companion-class autopilot with NPU)
- **Summary**: Auterion Skynode S = compact mission computer with **dedicated Neural Processing Unit** for AI / computer-vision applications on small UAS systems. Architecturally the same niche our Jetson Orin Nano Super sits in (companion compute + autopilot integration), but with Auterion's PX4 fork pre-integrated. Hardware/runtime envelope is comparable; the product establishes that this is a product category, not a one-off integration.
- **Related Sub-question**: SQ1, SQ7 (alternate companion HW for adjacent context)
### Source #33
- **Title**: snktshrma/ngps_flight — Next-Generation Positioning System for ArduPilot (GSoC 2024)
- **Link**: https://github.com/snktshrma/ngps_flight (sibling: https://github.com/snktshrma/ap_nongps)
- **Tier**: L1 (open-source code repository, published GSoC project under ArduPilot organisation)
- **Publication Date**: GSoC 2024 timeframe (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: GSoC 2024 prototype (research-grade, not production firmware)
- **Target Audience**: ArduPilot integrators building visual-positioning companion stacks
- **Research Boundary Match**: **Full match — closest open-source peer to our exact pipeline.** ArduPilot, downward-facing camera, satellite-image reference, deep-learning matching, fused with VIO, fed back to autopilot.
- **Summary**: NGPS = ROS 2 + ArduPilot pipeline composed of three packages: **`ap_ngps_ros2`** (visual geo-localization at 1–2 Hz by matching live camera frames to georeferenced satellite imagery using **LightGlue + SuperPoint**); **`ap_ukf`** (Unscented Kalman Filter fusing NGPS absolute positions with VIO estimates); **`ap_vips`** (VIO providing relative pose). Output is fused odometry to ArduPilot's EKF via `VISION_POSITION_ESTIMATE` (per the related issue #23471 framing). **This is the architectural template** the project should explicitly compare against — same component split as our C1+C2+C3+C5+C8 stack.
- Caveats: (a) GSoC prototype, not production-hardened; (b) uses `VISION_POSITION_ESTIMATE` which on AP requires EKF source set 2/3 with EK3_SRC*_POSXY=Vision; our SQ6 conclusion picked `GPS_INPUT` as primary AP path because it carries `horiz_accuracy` directly and supports source-set switching via `MAV_CMD_SET_EKF_SOURCE_SET` — must compare the trade-off in design phase; (c) no documented spoofing-defence integration; (d) no documented covariance-honesty contract.
- **Related Sub-question**: SQ1 (closest open-source peer), SQ2 (canonical-pipeline confirmation), SQ3+SQ4 (architectural template for component selection), SQ6 (alternate AP transport: `VISION_POSITION_ESTIMATE` vs `GPS_INPUT`)
### Source #34
- **Title**: AerialExtreMatch — A Benchmark for Extreme-View Image Matching and Localization (project page + GitHub + Hugging Face dataset)
- **Link**: https://xecades.github.io/AerialExtreMatch/ ; https://github.com/Xecades/AerialExtreMatch ; https://huggingface.co/datasets/Xecades/AerialExtreMatch-Localization
- **Tier**: L1 (peer-reviewed benchmark with public dataset, code, model checkpoints; OpenReview submission)
- **Publication Date**: 2025 (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Target Audience**: Academic + practitioner image-matching evaluators
- **Research Boundary Match**: **Full match** for cross-source UAV-satellite image matching evaluation
- **Summary**: 2025 benchmark with: 1.5 M synthetic train pairs (RGB+depth, diverse UAV/satellite viewpoints); ~30,000 evaluation pairs in 32 difficulty levels stratified by overlap (4 bins: <20/20-40/40-60/>60%), pitch difference (4 bins: 50–55, 55–60, 60–65, 65–70°), and scale (2 bins: 1-2×, >2×); a real-world UAV-localization split captured with DJI M300 RTK + H20T against UAV-derived orthomosaic/DSM AND lower-quality satellite maps. Evaluates 16 representative detector-based + detector-free image matching methods. **This is the academic benchmark our C2+C3 candidate selection must publish numbers against.**
- **Related Sub-question**: SQ1 (academic landscape), SQ7 (datasets)
### Source #35
- **Title**: DARPA Fast Lightweight Autonomy (FLA) program page + Test-and-Evaluation review (arXiv 2504.08122)
- **Link**: https://www.darpa.mil/research/programs/fast-lightweight-autonomy ; https://arxiv.org/abs/2504.08122
- **Tier**: L1 (DARPA program page + 2025 academic review of program results)
- **Publication Date**: program 2015–2018 (concluded); review 2025-04 (accessed 2026-05-07)
- **Timeliness Status**: Foundational reference; review is current (within 18-month authority window)
- **Target Audience**: Defense-program historians + indoor-low-altitude GPS-denied autonomy researchers
- **Research Boundary Match**: **Partial — different regime.** FLA = small quadcopters at ≤20 m/s in cluttered indoor/outdoor with onboard sensing only, no satellite-tile basemap. Our project is fixed-wing, ~17 m/s, 1 km AGL, with sat-tile basemap.
- **Summary**: Foundational US-defense lineage for GPS-denied autonomy (2015–2018, complete). Set the template for "small UAV + onboard sensors + onboard compute → autonomous obstacle-avoidance + navigation without datalink/GPS". Phase 1 in Florida 2017; Phase 2 in Georgia 2018. The 2025 retrospective (arXiv 2504.08122) reviews FLA's testing methodology and Phase 1 results. Companion 2025 USAF SBIR Phase II solicitation (Sweetspot ID `7946c818-409f-5b31-8f06-554466071d83`) is requesting visual-position-and-navigation capability for sUAS in GPS-denied environments — the regulatory tailwind is now active.
- **Related Sub-question**: SQ1 (defense-program lineage)
### Source #36
- **Title**: DSMAC / TERCOM lineage — DTIC ADA315439 (Scene Matching Missile Guidance Technologies) + Wikipedia / SPIE references
- **Link**: https://apps.dtic.mil/sti/tr/pdf/ADA315439.pdf ; https://en.wikipedia.org/wiki/DSMAC ; https://www.spiedigitallibrary.org/conference-proceedings-of-spie/0238/1/Terrain-Contour-Matching-TERCOM-A-Cruise-Missile-Guidance-Aid/10.1117/12.959127.short
- **Tier**: L1 (DTIC unclassified technical report) + L2 (encyclopedia/SPIE proceedings)
- **Publication Date**: DTIC: 1996; SPIE: 1980; Wikipedia: live
- **Timeliness Status**: Foundational baseline (no time window per Step 0.5 — established classical algorithms)
- **Target Audience**: Cruise-missile-class designers; analogues for downward-vision navigation
- **Research Boundary Match**: **Partial — different regime** (cruise missile, terminal guidance). Architectural pattern (pre-cached scene reference + downward camera + correlation matching) is the direct ancestor of our C3 pipeline.
- **Summary**: DSMAC = electro-optical camera correlated against pre-stored reference scenes (often from satellite reconnaissance), achieving 3–10 m terminal accuracy. Tomahawk: TERCOM (radar altimeter + DEM) for mid-flight; DSMAC for terminal. CEP without DSMAC: ~30 m; with DSMAC: "only meters". Gulf War 1991: >80% of 280 launched Tomahawks hit target. **Establishes that downward-vision-against-pre-stored-imagery is a 40+ year-old well-characterised technique class with documented accuracy bounds; our project's claim of <500 m / 99.9% reliability is achievable in the same technique class.**
- **Related Sub-question**: SQ1 (lineage), SQ8 (baseline accuracy expectations)
### Source #37
- **Title**: Electronic Warfare in Ukraine: The Invisible Battle — Ukraine War Analytics
- **Link**: https://ukraine-war-analytics.com/analysis/electronic-warfare-ukraine.html
- **Tier**: L3 (analytical aggregator; primary-source numbers cite vendor / OSINT reports)
- **Publication Date**: live (accessed 2026-05-07)
- **Timeliness Status**: Currently valid (operational-context reference)
- **Target Audience**: Ukraine-deployment practitioners
- **Research Boundary Match**: Full match (operational geography, threat environment)
- **Summary**: Operational-context anchor: Russian EW systems including Pole-21 GPS jammers (25+ km range) plus spoofing capabilities have driven ~70% of small-tactical-UAV losses to EW across the conflict. Twist Robotics' OSCAR cites the same approximate number (~75% of small tactical UAV losses to EW at the front per Source #25). **Confirms the demand-side number is consistent across two independent reporting chains.**
- **Related Sub-question**: SQ1 (Ukraine practitioner perspective)
---
## SQ2 — Canonical pipeline decomposition
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# Source Registry — SQ2 — Canonical pipeline decomposition
> Mode A Phase 2 — engine Step 2 (Source Tiering & Exhaustive Web Investigation).
> Critical-novelty sensitivity per Step 0.5 in `../00_question_decomposition.md`. Time windows applied:
> - **Lead-candidate / SOTA claims**: prefer sources within last 6 months; up to 18 months if older is the official authority.
> - **Library/SDK API behaviour**: must reflect the currently shipped version at search time (`context7` mandatory per lead candidate).
> - **Established baselines** (KLT, RANSAC, EKF, ORB, SIFT, GTSAM): no time window.
>
> This file replaces a section of the previous monolithic `01_source_registry.md`. See `00_summary.md` for the full category index. Investigation order is tracked in `../00_question_decomposition.md` and the cross-category Investigation Status table in `00_summary.md`.
---
### Source #38
- **Title**: Visual Place Recognition for Aerial Imagery: A Survey (Moskalenko, Kornilova, Ferrer — Skoltech)
- **Link**: https://arxiv.org/abs/2406.00885 (v2)
- **Tier**: L1 (peer-reviewed survey, accepted in Robotics and Autonomous Systems; companion benchmark code: https://github.com/prime-slam/aero-vloc)
- **Publication Date**: arXiv 2024-06; v2 update through 2024
- **Timeliness Status**: Currently valid (within 18-month authority window for established surveys; specific candidate latency numbers will need cross-validation against newer Jetson-class hardware reports)
- **Target Audience**: Aerial-VPR practitioners + UAV navigation system architects
- **Research Boundary Match**: **Full match** for the offline-cache visual geo-localization decomposition (aerial-nadir UAV vs. satellite tile basemap)
- **Summary**: Authoritative two-stage pipeline definition (verbatim): "Visual geolocalization can be implemented through various methods, typically relying on a pre-built database of images with known locations. This approach generally involves two stages: **global localization (or Visual Place Recognition, VPR) and local alignment**. Global localization involves identifying the nearest frame from the database (Image Retrieval), while local alignment determines the precise position using the selected frame." Re-ranking is treated as an integral sub-stage of VPR for aerial data because of agricultural/urban grid repetition. Local alignment = SuperPoint/keypoint detector → LightGlue/SuperGlue/SelaVPR matcher → cv2.findHomography → cv2.perspectiveTransform → Web-Mercator coordinate conversion. **Practitioner-critical runtime numbers (RTX 3090, NOT Jetson)**: AnyLoc descriptor calculation = 0.37–0.84 s/frame (huge ViT-G DINOv2); MixVPR / SALAD = 0.05–0.20 s; SelaVPR = 0.04 s; SuperGlue re-rank = 15–25 s on top-100 candidates; LightGlue re-rank = ~1 s; SelaVPR re-rank = <0.1 s. Memory: AnyLoc descriptors = 2.3–13.9 GB for 4–7k tiles; SelaVPR = <0.2 GB. Final commentary: "While our methodology alone may not provide comprehensive robustness, it can be effectively augmented with additional sensors, such as inertial measurement units (IMUs). This integration enhances its utility for Visual Inertial Odometry (VIO) and Simultaneous Localization and Mapping (SLAM) systems, particularly for periodic location refinement and loop closure tasks. Additionally, our methodology could serve as a dependable emergency localization fallback in the event of an unexpected GNSS signal loss." → **Validates the project's IMU/VIO + sat-anchor architecture as the canonical extension of the survey's two-stage core.**
- **Related Sub-question**: SQ2 (canonical decomposition), SQ3+SQ4 (C2/C3 candidate latency budgets), SQ5 (foundation-model-on-Jetson failure mode)
### Source #39
- **Title**: Cross-View Geo-Localization: A Survey (Durgam, Paheding, Dhiman, Devabhaktuni — U. Maine / Fairfield / ISU)
- **Link**: https://arxiv.org/abs/2406.09722 (v1)
- **Tier**: L1 (peer-style preprint, journal-bound — Expert Systems with Applications)
- **Publication Date**: arXiv 2024-06
- **Timeliness Status**: Currently valid (≤18 months for survey-of-deep-learning architectures)
- **Target Audience**: Cross-view (ground↔aerial) geo-localization researchers; partial overlap with our aerial↔satellite pipeline
- **Research Boundary Match**: **Partial — different cross-view setup** (the survey focuses on ground panorama → aerial overhead; ours is aerial nadir → satellite ortho). The pipeline-shape lessons transfer; the polar-transform / Siamese-network / GAN-based view-synthesis lessons do NOT directly apply because our two views are both top-down.
- **Summary**: Confirms the canonical pipeline decomposition (feature extraction → cross-view matching → similarity-driven retrieval) is the dominant pattern across 2015–2024 SOTA. Establishes the historical lineage: pixel-wise (Sheikh 2003) → feature-based (Lin 2013) → CNN/triplet-loss (Tian 2017) → Siamese+GAN (Hu 2018) → polar-transform (Shi 2019) → CosPlace/EigenPlaces (2022–2023) → DINOv2-class (AnyLoc 2023) → Transformer-only (TransGeo 2022, MGTL 2022) → multi-method fusion (2023+). Backbone comparison table establishes that ViT/DINOv2 is the current SOTA backbone; ResNet-class is the established production baseline; SIFT/SURF/PHOW remain the handcrafted baseline. **Confirms our component-area split (C2 VPR + C3 cross-domain matching) is canonical and matches the survey's two-axis organization (backbone × matching strategy).**
- **Related Sub-question**: SQ2 (decomposition lineage), SQ3+SQ4 (C2 candidate landscape)
### Source #40
- **Title**: OrthoLoC: UAV 6-DoF Localization and Calibration Using Orthographic Geodata (Dhaouadi, Marin, Meier, Kaiser, Cremers — DeepScenario / TU Munich / MCML)
- **Link**: https://arxiv.org/abs/2509.18350 ; project page https://deepscenario.github.io/OrthoLoC
- **Tier**: L1 (peer-style preprint with public dataset, code, model checkpoints; 16,425 UAV images Germany+US, full 6-DoF ground truth)
- **Publication Date**: arXiv 2025-09 (within 6-month critical-novelty window)
- **Timeliness Status**: Currently valid (within 6-month critical-novelty window for SOTA aerial-localization claims)
- **Target Audience**: UAV-localization implementers + system architects building on Digital Orthophotos (DOP) + Digital Surface Models (DSM)
- **Research Boundary Match**: **Full match — direct paradigm match** to our project: "lightweight orthographic representations" instead of 3D meshes; "increasingly accessible through free releases by governmental authorities"; "no internet connection or GNSS/GPS support" — exactly the project's constraint envelope.
- **Summary**: **Most directly applicable SQ2 source.** Defines the 6-DoF localization pipeline using 2.5D geodata: (1) match query UAV image against DOP (orthophoto raster) using state-of-the-art matchers; (2) lift each 2D match in the DOP to 3D using the corresponding DSM elevation; (3) PnP+RANSAC (RANSAC-EPnP, 5-pixel inlier threshold) → initial pose; (4) Levenberg-Marquardt joint refinement of intrinsics + extrinsics; (5) **AdHoP refinement**: estimate homography from initial 2D-2D correspondences via DLT+RANSAC, warp the DOP to better match the query's perspective, re-match, map back via H⁻¹, lift to 3D, refine pose; accept refinement only if reprojection error decreases. **Quantitative results** on 16.4k images, 47 locations: best matcher = GIM+DKM achieves 75.4% recall at 1m-1° threshold (sparse SP+SG = 64.4%, sparse SP+LG = 64.2%, MASt3R = 63.5%, RoMa+AdHoP = 54.6%, XFeat*+AdHoP = 59.8%; LoFTR / eLoFTR / XoFTR all <23% recall). AdHoP yields ~30% average matching improvement, ~20% translation/rotation error reduction; for previously-underperforming methods (XFeat* → 95% matching improvement; DKM → 63% translation reduction; RoMa → 1m-1° recall +23%). **Performance factors** explicitly characterized: (a) **cross-domain DOPs (visual gap only) cause ~3× translation error increase** even on best method; (b) **cross-domain DOPs+DSMs (visual + structural gap) cause ~7× translation error increase** (0.16 m → 1.12 m for GIM+DKM+AdHoP) — **this is exactly the war-zone scene-change scenario AC-3.x covers**; (c) **20% covisibility floor** between query and reference; below it localization fails; (d) **Calibration is fundamentally ambiguous** between focal length and translation → camera intrinsics MUST be calibrated upstream, not jointly optimized in flight. (e) Resolution: scaling images to 30% of original (~300 px) still works; geodata at 13 m/pixel is the floor, with degradation below.
- **Related Sub-question**: SQ2 (canonical pipeline + AdHoP refinement loop), SQ3+SQ4 (C3 matcher candidate ranks), SQ5 (war-zone scene-change failure mode), SQ8 (covisibility safety gate)
### Source #41
- **Title**: Exploring the best way for UAV visual localization under Low-altitude Multi-view Observation Condition: a Benchmark — AnyVisLoc (Ye, Teng, Chen, Li, Liu, Yu, Tan — NUDT / Macao Polytechnic)
- **Link**: https://arxiv.org/abs/2503.10692 ; benchmark code https://github.com/UAV-AVL/Benchmark
- **Tier**: L1 (peer-style preprint with public 18,000-image dataset across 15 Chinese cities, multi-pitch / multi-altitude / multi-scene, with both aerial-photogrammetry AND satellite reference maps)
- **Publication Date**: arXiv 2025-03 (within 6-month critical-novelty window)
- **Timeliness Status**: Currently valid
- **Target Audience**: Aerial AVL practitioners; UAV-system designers facing pitch/altitude/yaw uncertainty
- **Research Boundary Match**: **Partial — different altitude regime** (the benchmark covers 30–300 m AGL, ours is ~1 km AGL); pitch range is 20–90° (ours is mostly nadir, ~80–90°). Lessons on the **pipeline structure, retrieval-vs-matching trade-offs, sensor-prior noise tolerance, and aerial-vs-satellite reference-map gap** transfer directly.
- **Summary**: Independently confirms the SAME pipeline as Source #40: image retrieval (rough position) → image matching (2D-2D) → DSM-lift to 3D → PnP+RANSAC. Best baseline = CAMP (retrieval) + RoMa (dense matcher) + Top-N re-rank → 74.1% A@5m on aerial photogrammetry map, 18.5% A@5m on satellite map (ALOS 30m DSM). **Critical AC-quantitative findings**: (a) **Aerial map vs satellite map**: 4× accuracy gap at A@5m (74.1% vs 18.5%) — driven by satellite-DSM coarseness (ALOS 30m vs aerial 0.94m) and modality difference. **Direct relevance**: project's offline cache is satellite tiles ≥0.5 m/px without DSM; this places us between the two data points (better than ALOS 30m, worse than aerial photogrammetry) — exact accuracy must be re-established once tile resolution is pinned. (b) **Yaw prior noise**: σ ≤ 5° → no impact; σ = 10° → 1.9% A@5m drop; σ = 30° → 4.1% drop; σ = 50° → 13.7% drop; σ = 60° → 25.7% drop. **Implication for project's C1+C5+IMU**: companion-side yaw estimate must hold σ < 10°. (c) **Pitch prior noise**: σ < 5° → no impact; σ ≥ 7° causes ~1–5% drops. (d) **Pitch angle**: smaller pitch (more oblique) → lower accuracy; nadir is best. Project's nadir-fixed camera at 1 km AGL is consistent with the benchmark's most-favourable regime. (e) **Sparse vs dense matchers**: SP+LightGlue+GIM+k2s = 75.4% A@10m at 105 ms/frame; RoMa = 81.3% A@10m at 659 ms/frame. **Implication for project's C7 Jetson runtime**: dense matchers ~6× more accurate but ~6× slower → SP+LightGlue-class is the production sweet spot under our 400 ms budget. (f) **Re-ranking strategy**: Top-N re-rank by inlier count = best accuracy/cost trade-off (62.2% A@5m at 0.8 s/frame on RTX 3090). Match-without-retrieval = catastrophic (34.3% A@5m, search-space too large).
- **Related Sub-question**: SQ2 (pipeline + sensor-prior tolerance), SQ3+SQ4 (C2 retrieval-vs-matcher trade-offs, C5 IMU prior contract), SQ5 (war-zone reference-map staleness failure mode), SQ7 (aerial-vs-satellite reference benchmarks)
### Source #42
- **Title**: Survey on absolute visual localization techniques for low-altitude unmanned aerial vehicles (Ye, Chen, Teng, Li, Yang, Song, Yu — NUDT, College of Aerospace Science)
- **Link**: https://www.sciopen.com/article/10.11887/j.issn.1001-2486.25120033 ; DOI 10.11887/j.issn.1001-2486.25120033
- **Tier**: L1 (peer-reviewed Chinese journal — Journal of National University of Defense Technology, vol 48 issue 2, 2026; same lab as Source #41 with overlapping authorship — confirmed cross-validation, not duplicative)
- **Publication Date**: 2026-04-01 (within 6-month critical-novelty window)
- **Timeliness Status**: Currently valid
- **Target Audience**: UAV-system architects + Chinese-defense-research community
- **Research Boundary Match**: **Full match** (low-altitude UAV AVL is the survey's exact subject)
- **Summary**: Survey-level confirmation of the canonical "**retrieval-matching-pose estimation**" hierarchical framework. Verbatim claim: "the hierarchical framework balances search efficiency, positioning accuracy, and scene generalization, becoming a robust technical path for low-altitude long-endurance absolute localization." Compares the framework against alternatives that are explicitly rejected: (a) relative visual localization (cumulative errors — VIO/SLAM only); (b) end-to-end direct localization (poor generalization); (c) map-free localization (scene-dependent). Sub-component evolution per stage: (a) retrieval = template-matching (SAD/SSD/NCC) → BoW/VLAD → deep-learning (annular/dense feature segmentation, contrastive InfoNCE, self-supervised); (b) matching = SIFT/SURF/ORB → SuperPoint+LightGlue/RoMa (sparse / semi-dense / dense); (c) pose estimation = PnP variants + RANSAC + IMU prior fusion. **Identifies four open challenges** that align with project risks: (i) cross-domain generalization (war-zone scene change); (ii) real-time inference on edge platforms (Jetson); (iii) robustness to complex environments (cropland, snow, low texture); (iv) high-quality datasets (the same gap our project's AC-NEW-7 / cache provisioning works around). **Lightweight-model-design-for-edge-deployment is named as a primary future-research direction** — directly validates project's Jetson Orin Nano constraint as a recognized field-level challenge, not a project-specific oddity.
- **Related Sub-question**: SQ2 (framework canonicalness), SQ3+SQ4 (per-component evolution), SQ5 (named open challenges align with project risks)
---
## SQ3+SQ4 / C1 (Visual / Visual-Inertial Odometry) — Candidate enumeration
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# Source Registry — SQ6 — ArduPilot Plane vs iNav external positioning
> Mode A Phase 2 — engine Step 2 (Source Tiering & Exhaustive Web Investigation).
> Critical-novelty sensitivity per Step 0.5 in `../00_question_decomposition.md`. Time windows applied:
> - **Lead-candidate / SOTA claims**: prefer sources within last 6 months; up to 18 months if older is the official authority.
> - **Library/SDK API behaviour**: must reflect the currently shipped version at search time (`context7` mandatory per lead candidate).
> - **Established baselines** (KLT, RANSAC, EKF, ORB, SIFT, GTSAM): no time window.
>
> This file replaces a section of the previous monolithic `01_source_registry.md`. See `00_summary.md` for the full category index. Investigation order is tracked in `../00_question_decomposition.md` and the cross-category Investigation Status table in `00_summary.md`.
---
### Source #1
- **Title**: Non-GPS Navigation — Plane documentation
- **Link**: https://ardupilot.org/plane/docs/common-non-gps-navigation-landing-page.html
- **Tier**: L1
- **Publication Date**: live docs (current ArduPilot stable, accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: ArduPilot 4.7+ (persistent origin storage); applies to current Plane stable
- **Target Audience**: ArduPilot Plane operators / developers
- **Research Boundary Match**: Full match (fixed-wing, ArduPilot Plane is in scope)
- **Summary**: Lists supported non-GPS navigation systems for Plane. Notes that boards <1MB flash still support `GPS_INPUT` even when they cannot run other non-GPS messages. Notes that Plane (non-VTOL) is generally not applicable for low-altitude non-GPS — but `GPS_INPUT` as an external GPS replacement is not constrained by that note.
- **Related Sub-question**: SQ6
### Source #2
- **Title**: GPS / Non-GPS Transitions — Plane documentation
- **Link**: https://ardupilot.org/plane/docs/common-non-gps-to-gps.html
- **Tier**: L1
- **Publication Date**: live docs (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: EKF3 (default since AP 4.0+)
- **Target Audience**: ArduPilot operators using mixed GPS / non-GPS sources
- **Research Boundary Match**: Full match
- **Summary**: Documents the EKF3 source-set mechanism (`EK3_SRC1..3_POSXY/VELXY/POSZ/VELZ/YAW`), three source sets, RC aux switch (option 90 "EKF Pos Source"), `MAV_CMD_SET_EKF_SOURCE_SET`, Lua-script driven switching. Explicitly named messages for non-GPS path: ExternalNav (option 6). GPS_INPUT is treated as a GPS source (set 1).
- **Related Sub-question**: SQ6
### Source #3
- **Title**: EKF Source Selection and Switching — Plane documentation
- **Link**: https://ardupilot.org/plane/docs/common-ekf-sources.html
- **Tier**: L1
- **Publication Date**: live docs (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: EKF3 stable
- **Target Audience**: ArduPilot operators / developers
- **Research Boundary Match**: Full match
- **Summary**: Authoritative parameter reference for `EK3_SRCx_*` (POSXY/VELXY/POSZ/VELZ/YAW). Important caveat: "Ground stations or companion computers may set the source by sending a `MAV_CMD_SET_EKF_SOURCE_SET` mavlink command **but no GCSs are currently known to implement this**." Source-set switching from companion is supported by AP, not by stock GCS UI. Mentions ExternalNAV/OpticalFlow transition options via `EK3_SRC_OPTIONS` bit 1.
- **Related Sub-question**: SQ6
### Source #4
- **Title**: ArduPilot AP_GPS_MAV.cpp (master)
- **Link**: https://raw.githubusercontent.com/ArduPilot/ardupilot/master/libraries/AP_GPS/AP_GPS_MAV.cpp
- **Tier**: L1 (source code)
- **Publication Date**: master HEAD (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: master branch
- **Target Audience**: ArduPilot developers, integrators of external GPS via MAVLink
- **Research Boundary Match**: Full match
- **Summary**: Authoritative implementation of `MAVLINK_MSG_ID_GPS_INPUT` ingestion into AP_GPS state. Decodes lat/lon/alt, hdop/vdop, velocity (vn/ve/vd), speed/horizontal/vertical accuracy, yaw. Honors `gps_id` (multi-GPS instance), `ignore_flags` bitmask (ALT, HDOP, VDOP, VEL_HORIZ, VEL_VERT, SPEED_ACCURACY, HORIZONTAL_ACCURACY, VERTICAL_ACCURACY). Requires `fix_type ≥ 3` and `time_week > 0` for jitter-corrected timestamping. Yaw uses `0` as "not provided" sentinel. Only `GPS_INPUT` is handled by this driver — `VISION_POSITION_ESTIMATE` / `ODOMETRY` go via the external-nav driver, not AP_GPS_MAV.
- **Related Sub-question**: SQ6
### Source #5
- **Title**: ArduPilot PR #28750 — AP_NavEKF3: added two more EK3_OPTION bits (GPS-denied testing)
- **Link**: https://github.com/ArduPilot/ardupilot/pull/28750
- **Tier**: L2 (development PR, ArduPilot core team)
- **Publication Date**: 2024 (accessed via search 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: master / pending stable branch propagation
- **Target Audience**: ArduPilot developers
- **Research Boundary Match**: Full match
- **Summary**: Adds new `EK3_OPTION` bits to allow easier GPS-denied testing of EKF3, including an aux-switch / MAVLink command path to disable GPS use. Confirms ongoing 2024-2025 work on GPS-denied robustness.
- **Related Sub-question**: SQ6
### Source #6
- **Title**: ArduPilot Issue #15859 — EKF3: improve source switching (GPS<->NonGPS)
- **Link**: https://github.com/ArduPilot/ardupilot/issues/15859
- **Tier**: L4 (issue tracker — open enhancement list)
- **Publication Date**: ongoing (long-running issue, accessed 2026-05-07)
- **Timeliness Status**: Currently valid (still open per dev docs reference)
- **Target Audience**: ArduPilot developers
- **Research Boundary Match**: Full match
- **Summary**: Authoritative list of planned improvements for source-switching. Linked from the L1 GPS-Non-GPS Transitions page. Indicates current source switching has known rough edges acknowledged by the core team.
- **Related Sub-question**: SQ6
### Source #7
- **Title**: ArduPilot Issue #27193 — EK3 Source Switching wrong frame for GUIDED commands SOLVED
- **Link**: https://github.com/ArduPilot/ardupilot/issues/27193
- **Tier**: L4 (issue tracker, resolved)
- **Publication Date**: 2024 (accessed 2026-05-07)
- **Timeliness Status**: Reference only (resolved as user-config)
- **Target Audience**: ArduPilot operators using GPS↔Vision source switching
- **Research Boundary Match**: Partial overlap (Copter context but the bug was in shared SET_POSITION_TARGET_GLOBAL_INT path)
- **Summary**: Documented frame-interpretation issue when companion switches source set 1 (GPS) → set 3 (VISION_POSITION_ESTIMATES) and back. Resolved as configuration not code, but illustrates the kind of edge case to validate in SITL for AC-NEW-2 promotion.
- **Related Sub-question**: SQ6
### Source #8
- **Title**: ArduPilot Issue #23485 — AP_NavEKF3: support fusing only External Nav Velocities (without position)
- **Link**: https://github.com/ArduPilot/ardupilot/issues/23485
- **Tier**: L4 (open enhancement)
- **Publication Date**: ongoing (open as of accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Target Audience**: ArduPilot developers
- **Research Boundary Match**: Full match
- **Summary**: Confirms current limitation: ODOMETRY without position causes position-estimate timeout / failsafe. Implies the project's `visual_propagated` path (VO without satellite anchor) cannot be expressed as ODOMETRY-velocity-only on current AP — must be sent as full GPS_INPUT with widened covariance.
- **Related Sub-question**: SQ6
### Source #9
- **Title**: iNavFlight/inav — telemetry/mavlink.c (master, processMAVLinkIncomingTelemetry)
- **Link**: https://github.com/iNavFlight/inav/blob/master/src/main/telemetry/mavlink.c
- **Tier**: L1 (source code, authoritative)
- **Publication Date**: master HEAD (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: iNav master (post-9.0)
- **Target Audience**: iNav developers
- **Research Boundary Match**: Full match
- **Summary**: Authoritative inbound MAVLink switch (lines ~1334–1390). Handles only: HEARTBEAT, PARAM_REQUEST_LIST (stub), MISSION_CLEAR_ALL, MISSION_COUNT, MISSION_ITEM, MISSION_REQUEST_LIST, MISSION_REQUEST, COMMAND_INT (only `MAV_CMD_DO_REPOSITION`), RC_CHANNELS_OVERRIDE, ADSB_VEHICLE, RADIO_STATUS. **No `GPS_INPUT`, no `VISION_POSITION_ESTIMATE`, no `ODOMETRY`, no `GLOBAL_POSITION_INT`, no `GPS_RAW_INT`** are accepted as inputs. Wiki page (Source #10) confirms.
- **Related Sub-question**: SQ6
### Source #10
- **Title**: iNav Wiki — MAVLink (frogmane edited 2025-12-11)
- **Link**: https://github.com/iNavFlight/inav/wiki/Mavlink
- **Tier**: L1 (project wiki)
- **Publication Date**: 2025-12-11
- **Timeliness Status**: Currently valid
- **Version Info**: iNav 8.0 / 9.0 era
- **Target Audience**: iNav users / integrators
- **Research Boundary Match**: Full match
- **Summary**: Authoritative inbound/outbound MAVLink message lists. "Limited command support: Commands that are not implemented are ignored." Explicitly enumerates the supported incoming list (matches Source #9). Confirms iNav MAVLink is "intended primarily for simple telemetry and operation" and "not 100% compatible".
- **Related Sub-question**: SQ6
### Source #11
- **Title**: iNav Wiki — GPS and Compass setup
- **Link**: https://github.com/iNavFlight/inav/wiki/GPS-and-Compass-setup
- **Tier**: L1
- **Publication Date**: live wiki (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: iNav 7.0+ (UBX-only); 9.0 requires UBX protocol ≥15.00
- **Target Audience**: iNav operators
- **Research Boundary Match**: Full match
- **Summary**: From iNav 7.0 NMEA was removed; only UBX is supported. Recommends u-blox M8/M9/M10 with protocol ≥15.00. Sets up the constraint for any UBX-emulation path the companion would take.
- **Related Sub-question**: SQ6
### Source #12
- **Title**: iNavFlight/inav docs/development/msp/README.md (MSP message reference)
- **Link**: https://github.com/iNavFlight/inav/blob/master/docs/development/msp/README.md
- **Tier**: L1 (project docs)
- **Publication Date**: live (master, accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: iNav master
- **Target Audience**: iNav developers / integrators
- **Research Boundary Match**: Full match
- **Summary**: Authoritative spec for `MSP_SET_RAW_GPS (201)` and `MSP2_SENSOR_GPS (7939)`. `MSP_SET_RAW_GPS` is 14-byte, lossy (no covariance, no per-axis velocity, altitude in meters with cm internal mismatch — bug fixed in 5.0.0 per issue #8336). `MSP2_SENSOR_GPS` is the newer plugin-style message with `hPosAccuracy`/`vPosAccuracy`/`hVelAccuracy` (mm and cm/s), `hdop`, NED velocity components, `trueYaw`, GPS week + time-of-week, fix type, satellite count. Requires `USE_GPS_PROTO_MSP` build flag and routes through `mspGPSReceiveNewData()` (the GPS_PROVIDER_MSP driver path).
- **Related Sub-question**: SQ6
### Source #13
- **Title**: iNavFlight/inav src/main/io/gps.c + src/main/target/common.h (master)
- **Link**: https://github.com/iNavFlight/inav/blob/master/src/main/target/common.h
- **Tier**: L1 (source code)
- **Publication Date**: master (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: master
- **Target Audience**: iNav developers
- **Research Boundary Match**: Full match
- **Summary**: `USE_GPS_PROTO_MSP` is enabled by default in the common target configuration; on default builds the MSP GPS provider (`GPS_PROVIDER_MSP`) is registered with `gpsRestartMSP` / `gpsHandleMSP`. Confirms the MSP2_SENSOR_GPS path is reachable on stock iNav firmware without custom builds.
- **Related Sub-question**: SQ6
### Source #14
- **Title**: iNav Issue #10141 — dual GPS support
- **Link**: https://github.com/iNavFlight/inav/issues/10141
- **Tier**: L4 (open feature request)
- **Publication Date**: ongoing (open as of accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Target Audience**: iNav users
- **Research Boundary Match**: Full match
- **Summary**: Confirms iNav does **not** support dual-GPS / primary-secondary failover. Open enhancement; no implementation in 8.0 / 9.0. Architectural implication: companion must be the sole GPS source for iNav (not a backup to a real GPS connected directly to FC).
- **Related Sub-question**: SQ6
### Source #15
- **Title**: iNav docs/GPS_fix_estimation.md (master)
- **Link**: https://github.com/iNavFlight/inav/blob/master/docs/GPS_fix_estimation.md
- **Tier**: L1
- **Publication Date**: live (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: iNav 8.0+
- **Target Audience**: iNav fixed-wing operators
- **Research Boundary Match**: Full match
- **Summary**: iNav's internal dead-reckoning ("GPS fix estimation") for fixed-wing. Uses gyro/accel/baro/(mag/pitot). RTH-only intent. **Explicitly states: "Not a solution for GPS spoofing (GPS output is not validated in INAV)"** — iNav has no internal anti-spoofing, so anti-spoofing is fully the companion's responsibility. Two settings: `inav_allow_gps_fix_estimation` (RTH-with-no-GPS) and `inav_allow_dead_reckoning` (short-outage tolerance) — both default OFF. `failsafe_gps_fix_estimation_delay` controls mission-vs-RTH tradeoff (default 7 s).
- **Related Sub-question**: SQ6 (dead-reckoning fallback) + SQ8 (anti-spoofing implication)
### Source #16
- **Title**: iNav docs/Settings.md (master)
- **Link**: https://github.com/iNavFlight/inav/blob/master/docs/Settings.md
- **Tier**: L1
- **Publication Date**: master (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: iNav master
- **Target Audience**: iNav operators
- **Research Boundary Match**: Full match
- **Summary**: Authoritative parameter list. Confirms `inav_allow_dead_reckoning` (line 2081, default OFF) ≠ `inav_allow_gps_fix_estimation` (line 2091, default OFF). The two settings address different scenarios. `failsafe_gps_fix_estimation_delay` (line 1041, default 7 s) governs mission-abort timing.
- **Related Sub-question**: SQ6
### Source #17
- **Title**: iNav Issue #10588 — Weird behaviour in DeadReckoning mode while GPS outage is not constant
- **Link**: https://github.com/iNavFlight/inav/issues/10588
- **Tier**: L4 (open issue, 2025)
- **Publication Date**: 2025
- **Timeliness Status**: Currently valid (open)
- **Target Audience**: iNav operators
- **Research Boundary Match**: Full match
- **Summary**: Documented stability bug: intermittent GPS outages cause porpoising and motor bursts in dead-reckoning. Cited recommendation: "GPS should be rejected if providing erroneous coordinates rather than no fix." Risk for AC-NEW-8 (visual blackout + spoofed GPS) on iNav: do NOT rely on iNav's dead-reckoning for the spoof-active failsafe path; companion must actively suppress its own MSP feed and accept that iNav may misbehave during the gap. Better: continue feeding companion-IMU-propagated position with growing covariance via MSP2_SENSOR_GPS so iNav never enters its dead-reckoning state.
- **Related Sub-question**: SQ6 + AC-NEW-8 design implication
### Source #18
- **Title**: iNav Release 8.0.0 (highlights, Dec 2024)
- **Link**: https://github.com/iNavFlight/inav/releases/tag/8.0.0
- **Tier**: L1 (project release notes)
- **Publication Date**: late 2024 / early 2025
- **Timeliness Status**: Currently valid
- **Version Info**: iNav 8.0
- **Target Audience**: iNav users
- **Research Boundary Match**: Full match
- **Summary**: Introduces fixed-wing GPS fix estimation (dead reckoning RTH-only) — the milestone for #8347. No new external-positioning inbound MAVLink in 8.0. Confirms iNav's 2024–2025 trajectory has not added a `GPS_INPUT`-equivalent inbound interface.
- **Related Sub-question**: SQ6
### Source #19
- **Title**: iNav Release 9.0.0 / 9.0.1 + 9.0.0 Release Notes wiki
- **Link**: https://github.com/iNavFlight/inav/wiki/9.0.0-Release-Notes
- **Tier**: L1
- **Publication Date**: 2025-2026
- **Timeliness Status**: Currently valid
- **Version Info**: iNav 9.0.x
- **Target Audience**: iNav users
- **Research Boundary Match**: Full match
- **Summary**: New in 9.0: pitot APA/TPA, position estimator improvements, MSP_REBOOT DFU, GCS NAV via `COMMAND_INT` `MAV_CMD_DO_REPOSITION`. **No** new external-positioning inbound MAVLink. UBX <15.00 dropped. Confirms iNav 9.x continues the same external-positioning architecture as 8.x.
- **Related Sub-question**: SQ6
### Source #20
- **Title**: MAVLink common message set — GPS_RAW_INT (24)
- **Link**: https://mavlink.io/en/messages/common.html
- **Tier**: L1 (MAVLink spec, live)
- **Publication Date**: live (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: MAVLink common, current
- **Target Audience**: MAVLink integrators
- **Research Boundary Match**: Full match
- **Summary**: Current published `GPS_RAW_INT` extension fields: `alt_ellipsoid`, `h_acc` (mm), `v_acc` (mm), `vel_acc` (mm/s), `hdg_acc` (degE5), `yaw` (cdeg). **No spoofing/jamming/integrity bitfield is present in `GPS_RAW_INT` at the time of access**, despite PR #2110 having been merged for spoofing/integrity reporting. Spoofing/integrity may live in a separate message (`GPS_INTEGRITY` or similar — to be verified in SQ8). For now, spoof-detection signals available to companion from FC are limited at the message-shape level; FC-side textual signals (`STATUSTEXT`) and `NAMED_VALUE_INT` are the documented practical path.
- **Related Sub-question**: SQ6 + SQ8
### Source #21
- **Title**: MAVLink PR #2110 — gps: add status and integrity information
- **Link**: https://github.com/mavlink/mavlink/pull/2110
- **Tier**: L2 (protocol PR with cross-project sign-off)
- **Publication Date**: merged (accessed via search 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: MAVLink common
- **Target Audience**: MAVLink integrators across PX4 / ArduPilot / QGC / Mission Planner
- **Research Boundary Match**: Full match
- **Summary**: Adds GNSS status / integrity reporting (jamming/spoofing/error) at the protocol level. Cross-project sign-off across PX4, ArduPilot, QGC, Mission Planner. Field-level breakdown to be cross-checked in SQ8 against the dialect XML — current `common.html` does not show those fields inside `GPS_RAW_INT` itself, suggesting they live in a sibling message (likely `GPS_INTEGRITY` or `GPS_STATUS_EXT`).
- **Related Sub-question**: SQ6 → defer to SQ8 for the precise message name and field set ArduPilot uses to expose spoofing.
### Source #22
- **Title**: AirDroper — GNSS Spoofing Filter (companion device, MAVLink2 NAMED_VALUE_INT pattern)
- **Link**: https://gps.airdroper.org/
- **Tier**: L3 (vendor product page; design pattern reference, not protocol authority)
- **Publication Date**: live (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Target Audience**: ArduPilot integrators considering anti-spoofing
- **Research Boundary Match**: Reference only (vendor's specific algorithm not relevant; the integration pattern is)
- **Summary**: Establishes a precedent that "companion-runs-spoofing-detection → publishes confidence to GCS as MAVLink2 `NAMED_VALUE_INT`, logged to dataflash" is a real-world integration pattern with ArduPilot, not novel to this project. Useful for SQ8.
- **Related Sub-question**: SQ8 (referenced from SQ6)
### Source #23
- **Title**: ArduPilot PR #24135 — Add option to make EKF3 more robust to bad IMU and lagged GPS data
- **Link**: https://github.com/ArduPilot/ardupilot/pull/24135
- **Tier**: L2 (development PR)
- **Publication Date**: 2023-2024 (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: master / propagated to stable
- **Target Audience**: ArduPilot developers
- **Research Boundary Match**: Full match
- **Summary**: Introduces `EK3_GLITCH_RADIUS` parameter — soft outlier rejection: instead of dropping a GPS measurement that fails innovation gating, the EKF inflates innovation variance to the minimum that just passes, effectively de-weighting the measurement. Implication for AC-NEW-4 (false-position safety): the project's covariance honesty contract on `GPS_INPUT.horiz_accuracy` is the ONLY way for AP's EKF to detect and de-weight a bad estimate; under-reporting collapses this safety net.
- **Related Sub-question**: SQ6 + AC-NEW-4 design implication
### Source #24
- **Title**: ArduPilot AP_NavEKF3 — VehicleStatus.cpp + AP_NavEKF3.cpp (master)
- **Link**: https://github.com/ArduPilot/ardupilot/blob/master/libraries/AP_NavEKF3/AP_NavEKF3_VehicleStatus.cpp ; https://github.com/ArduPilot/ardupilot/blob/master/libraries/AP_NavEKF3/AP_NavEKF3.cpp
- **Tier**: L1 (source code)
- **Publication Date**: master HEAD (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: master
- **Target Audience**: ArduPilot EKF3 developers
- **Research Boundary Match**: Full match
- **Summary**: EKF3 quality control: (a) ground-stationary GPS drift check ≤ 3 m (gated by `_gpsCheckScaler`); (b) innovation gating per `POS_I_GATE` / `VEL_I_GATE`; (c) soft de-weighting via `EK3_GLITCH_RADIUS` (Source #23). Confirms AP's covariance-driven quality path actually exists; companion-supplied `horiz_accuracy` flows into this chain.
- **Related Sub-question**: SQ6 (full file analysis deferred to design phase)
---
## SQ1 — Existing / competitor GPS-denied UAV navigation systems
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# Fact Cards — Index & Summary
> Mode A Phase 2 — engine Step 3 (Fact Extraction & Evidence Cards). Extracted from sources logged in `../01_source_registry/` (see `../01_source_registry/00_summary.md` for index). Confidence labels: ✅ High (L1 / verified source code), ⚠️ Medium (L1/L2 with caveat), ❓ Low (L3/L4 inferential).
>
> Bound to sub-questions in `../00_question_decomposition.md`. Many SQ6 facts also bind directly to the Project Constraint Matrix (`../../00_problem/acceptance_criteria.md` / `../../00_problem/restrictions.md`); per the engine's "Per-Mode API Capability Verification" rule, MAVLink/MSP messages are treated as candidate **modes** and are bound `Pass/Fail/Verify/N/A` against numbered ACs and restrictions.
This folder replaces the previous monolithic `02_fact_cards.md` (1480 lines, too large to navigate). Each category lives in its own file. Open the file matching the area you need — every fact and conclusion is preserved verbatim.
---
## Category index
| File | Sub-question / Component | Facts (count) | Scope summary |
| --- | --- | --- | --- |
| [`SQ6_fc_external_positioning.md`](SQ6_fc_external_positioning.md) | **SQ6** — ArduPilot Plane vs iNav external positioning | #1–#10 (10 facts) | MAVLink `GPS_INPUT` (232) ingestion in EKF3, iNav MSP `MSP2_SENSOR_GPS` ingestion via INAV BlackBox, covariance honesty, lane-fusion / lane-switch on (NSats, HDOP, fix_type), spoof-promotion via UBX emulation, dead-reckoning behaviour, `EK3_GPS_CHECK` bit-mask gates. Working conclusions: ArduPilot is the cooperative path, iNav requires UBX impersonation. |
| [`SQ1_existing_systems.md`](SQ1_existing_systems.md) | **SQ1** — Existing / competitor GPS-denied UAV navigation systems | #11–#20 (10 facts) | Twist Robotics OSCAR (Ukrainian peer), Auterion Artemis OS, Vantor Raptor, NGPS class systems, SPRIN-D winner, RTAB-Map / ORB-SLAM3 pruning rationale, DSMAC/TERCOM lineage, hierarchical retrieval-matching SOTA, AerialExtreMatch benchmark, DARPA FLA + USAF SBIR programs. Working conclusions: VPR-anchored hybrid pipeline is canonical. |
| [`SQ2_canonical_pipeline.md`](SQ2_canonical_pipeline.md) | **SQ2** — Canonical GPS-denied pipeline & SOTA components | #21–#27 (7 facts) | Two-stage canonical pipeline (global VPR → local alignment → PnP-RANSAC → EKF), end-to-end visual-localization rejection (poor generalization, no covariance), cross-domain sat ↔ UAV registration, hardware MVE doctrine, Top-N inlier re-rank gate. Working conclusions: VIO + VPR + Matcher + PnP + EKF is the design floor. |
| [`C1_vio.md`](C1_vio.md) | **C1** — Visual / Visual-Inertial Odometry | Candidate enumeration + decisions | VINS-Mono (BSD/permissive baseline), VINS-Fusion (GPL-3.0 alternate), OpenVINS (GPL-3.0), OKVIS2 (BSD), Kimera-VIO (BSD), DROID-SLAM (BSD non-VIO), DPVO (Apache-2.0 non-VIO), KLT+RANSAC (homemade fallback). Decisions: D-C1-1 license posture, D-C1-2 IMU rate. |
| [`C2_vpr.md`](C2_vpr.md) | **C2** — Visual Place Recognition | Candidate enumeration + decisions | MixVPR, SALAD (GPL-3.0 disqualifier), SelaVPR, NetVLAD, EigenPlaces, AnyLoc, BoQ, DINOv2-VLAD. Decisions: D-C2-1 aerial-domain training, D-C2-2 cache budget, D-C2-3 input resolution shape, D-C2-N TensorRT export gate. |
| [`C3_matchers.md`](C3_matchers.md) | **C3** — Cross-domain registration (Matchers) | Candidate enumeration + decisions | SP+LightGlue (Magic Leap noncommercial disqualifier on canonical SP), DISK+LightGlue (RECOMMENDED-PRIMARY-MITIGATION), ALIKED+LightGlue, XFeat (alternate-modern lead), SuperGlue+SuperPoint (deprecated by LightGlue authors), MASt3R (CC-BY-NC), RoMa, DKM, LoFTR. Decisions: D-C3-1 modern-competitive lead, D-C3-2 ONNX/TensorRT export path, D-C3-6 re-rank strategy. |
| [`C4_pose_estimation.md`](C4_pose_estimation.md) | **C4** — Pose estimation (PnP + RANSAC + LM) | #52–#54 (3 facts, in progress) | OpenCV `cv::solvePnPRansac` mandatory simple-baseline (Apache-2.0 throughout, 9 SolvePnPMethod enum values with 2 BROKEN, paired `solvePnPRefineLM`/`solvePnPRefineVVS`/`solvePnPGeneric`, 7 USAC RANSAC variants); OpenGV modern-competitive-lead-richer-minimal-solver (BSD-3-Clause-equivalent NOASSERTION-SPDX-detector contingent + ~3-year stale + 4 algorithm-selectable RANSAC enums [KNEIP/GAO/EPNP/GP3P] + 2 P3P variants + UPnP global-optimal + GP3P generalized-camera; NO planar-scene dedicated solver vs OpenCV's IPPE); GTSAM modern-competitive-lead-covariance-honest (BSD-3-Clause clean throughout, daily-active maintenance, **NATIVE 6×6 pose covariance via `Marginals.marginalCovariance` — only C4 candidate to satisfy AC-NEW-4 NATIVELY**, no native RANSAC, ~50-200 MB footprint, tight AC-4.1 latency margin). Decisions: D-C4-1 (carry-forward) 2D-3D-lift; D-C4-2 (NEW + UPDATED) covariance-recovery-strategy; D-C4-3 (NEW) OpenGV license-clearance-verification; D-C4-4 (NEW) OpenGV maintenance-staleness-mitigation. Subsequent candidates pending: Theia / Ceres-only (likely deferrable — D-C4 row may already have sufficient coverage). |
| [`C5_state_estimator.md`](C5_state_estimator.md) | **C5** — State estimator / sensor fusion | #88–#89 (2 facts, **batch 1 closed at 2/N 2026-05-08**) | Manual ESKF reference (Solà 2017 canonical aerial/quaternion arXiv preprint — public-domain canonical equations + project-side custom implementation under project's Apache-2.0; mandatory simple-baseline; trivial dependency footprint at ~kilobytes of NumPy/SciPy code; native 6×6 covariance via analytic Jacobian propagation per Solà §6 canonical recipe; quaternion-correct attitude integration on SO(3) via small-angle approximation in error-state; **fastest C5 candidate by an order of magnitude** at ~5-15 ms per update on Jetson CPU); GTSAM `iSAM2` + `CombinedImuFactor` (Forster et al. RSS 2015) + `PreintegratedCombinedMeasurements` + `BetweenFactorPose3` + `GenericProjectionFactorCal3DS2` + `PriorFactorPose3` + smart projection factors + `Marginals.marginalCovariance` + `gtsam_unstable.IncrementalFixedLagSmoother` modern-competitive-lead-factor-graph (clean BSD-3-Clause throughout, daily-active maintenance with last-pushed 2026-05-08T13:00:22Z = TODAY at access time, **architecturally couples with C4 Fact #54 via shared GTSAM substrate**, native 6×6 posterior covariance via `Marginals` — same NATIVE AC-NEW-4 satisfaction pathway as C4 Fact #54, IMU pre-integration via Forster et al. RSS 2015 `CombinedImuFactor` 6-key per-keyframe-pair factor with bias evolution for asynchronous IMU+camera fusion at ~100-200 Hz IMU + 3 Hz camera, ~50-200 MB footprint, incremental smoothing via iSAM2 amortizes per-frame cost, **NATIVE AC-4.5 look-back refinement** unique among C5 candidates). Decisions: D-C5-1 (NEW) reference-implementation-license-verification; D-C5-2 (NEW) long-cruise-observability-strategy; D-C5-3 (NEW) sliding-window-primitive-choice; D-C5-4 (NEW) IMU-gap-handling-strategy; D-C5-5 (NEW) factor-density-choice (recommended D-C5-5 = (c) couples C4 Fact #54 D-C4-2 = (b) with C5 Fact #89 architectural integration via shared GTSAM substrate). |
| [`C6_tile_cache_spatial_index.md`](C6_tile_cache_spatial_index.md) | **C6** — Tile cache + spatial index | #92–#93 (2 facts, **batch 1 closed at 2/N 2026-05-08**) | **Cand 1 RECOMMENDED PRIMARY**: Manual mirror of existing parent-suite `satellite-provider` pattern (verified directly via Source #92 filesystem read at /Users/obezdienie001/dev/azaion/suite/satellite-provider/) — PostgreSQL btree composite on slippy-map `(tile_zoom, tile_x, tile_y, version)` for geographic spatial-grid range queries + `bytea` descriptor blobs + app-side FAISS `IndexHNSWFlat(d, M=32)` loaded at takeoff via `faiss.read_index` for descriptor ANN + filesystem tile storage at `./tiles/{zoom}/{x}/{y}.{image_type}` slippy-map convention; clean PostgreSQL License + MIT + LGPL/MIT-Apache; trivial dependency footprint (no Postgres extensions); empirically-confirmed Postgres-on-Jetson viability per Source #97 March 2026 article (CPU cores limiting, NOT memory); ~6-54 ms per cache hit comfortably within AC-4.1 400 ms p95 budget; ~700 MB-1.5 GB total memory footprint within AC-4.2 8 GB budget. **Cand 2 DEFERRED secondary**: PostgreSQL + PostGIS 3.4 GiST on `geography(POINT,4326)` with KNN distance ordering (`<->`) + pgvector 0.7+ HNSW for descriptor ANN + same filesystem tile storage; native KNN + radius + combined-SQL capabilities are real improvements BUT 5-10× slower geographic lookup than Cand 1 + heavier dependency (~50-100 MB additional memory + ~50-200 MB additional disk install) + PostGIS GPL-2.0-or-later license-complexity (CONTINGENT REJECT under D-C1-1 = (b) BSD/permissive-only-track) + DIVERGENT from suite pattern + improvements marginal-to-negative in project's pinned 3 Hz spatial-grid query operating context. **Comparative-improvement-vs-Cand-1 verdict**: per user's session-start "significant-improvement-only" bar, no material justification to deviate from existing satellite-provider pattern. Decisions: D-C6-1 (NEW) descriptor-storage-format choice (halfvec recommended); D-C6-2 (NEW Cand-1-only) FAISS index variant choice (IndexHNSWFlat M=32 recommended); D-C6-3 (NEW Cand-1-only CROSS-COMPONENT with C10) descriptor-cache-rebuild-trigger strategy (periodic-during-C10-pre-flight recommended); D-C6-4 (NEW Cand-1-only) geographic-spatial-grid radius (dynamic recommended); D-C6-5 (NEW Cand-2-only contingent) Jetson PostGIS+pgvector co-installation Plan-phase verification (verify-on-Jetson-MVE recommended); D-C6-6 (NEW Cand-2-only contingent) pgvector descriptor-storage-type choice (halfvec recommended); D-C6-7 (NEW CROSS-COMPONENT affects parent-suite satellite-provider) cascade-changes-back-to-suite strategy (leave-unchanged recommended given Cand 1 closure verdict). |
| [`C7_inference_runtime.md`](C7_inference_runtime.md) | **C7** — On-Jetson inference runtime | #94–#96 (3 facts, **batch 1 closed at 3/N 2026-05-08**) | **Cand 1 RECOMMENDED PRIMARY**: TensorRT native — JetPack 6.2 bundled TensorRT 10.3 + `IInt8EntropyCalibrator2` + `BuilderFlag.FP16+INT8` mixed-precision + engines built directly on Jetson Orin Nano Super SM 87 (Apache-2.0 in TensorRT 10.x; ships with JetPack so zero-effort install; lowest-latency primary path; 2-3× speedup at INT8 vs FP16 per Source #102 YOLO26 benchmark; engines tied to SM 87 hardware-specific per Source #105 — must be built on deployed Jetson via D-C7-7); **Cand 2 modern-competitive-lead-cross-architecture-portability**: ONNX Runtime + TensorRT EP — `onnxruntime-gpu` via Jetson AI Lab JP6/CU126 wheel index + `TensorrtExecutionProvider` config + automatic CUDA EP / CPU EP subgraph fallback (MIT throughout; cross-architecture portability for replay/SITL on x86 dev hosts; `pip install onnxruntime-gpu` does NOT work on Jetson — needs Jetson AI Lab community wheel via D-C7-3 + numpy<2.0.0 pin via D-C7-4); **Cand 3 mandatory simple-baseline**: pure PyTorch FP16 — `torch.amp.autocast` + `model.half()` + Jetson AI Lab PyTorch 2.5 ARM64 wheel (BSD-3-Clause throughout; zero-conversion regression baseline; reference-correctness oracle for accuracy validation of TRT-built engines; standard `pip install torch` lacks CUDA on Jetson — needs Jetson AI Lab wheel via D-C7-5). **Cross-cutting precision policy** (D-C7-6 NEW CROSS-COMPONENT, affects C2+C3+C1+C7): VPR backbones (CNN-class MixVPR/EigenPlaces/NetVLAD) → INT8+FP16 mixed; ViT-class VPR (SelaVPR DINOv2-L; conditional AnyLoc/BoQ/DINOv2-VLAD) → FP16-only initially, INT8 deferred to Jetson MVE per D-C2-5; matchers (LightGlue with SP/DISK/ALIKED, XFeat, XFeat+LighterGlue) → **FP16-only — NO INT8** per Source #103 quantization-sensitivity finding (LightGlue FP8 ModelOpt collapsed match counts); learned VIO frontends → FP16-only initially. **Triton/DeepStream/CUDA-Python custom kernels considered-and-rejected** (server/video-pipeline class + out-of-budget for embedded 8 h mission) per c7_overkill_options scope choice. Decisions: D-C7-1 (NEW Cand-1-only CROSS-COMPONENT with C9) calibration-dataset-strategy (AerialVL S03 + AerialExtreMatch recommended); D-C7-2 (NEW Cand-1-only) TensorRT mixed-precision flag matrix (per-family policy per D-C7-6 recommended); D-C7-3 (NEW Cand-2-only) ORT-Jetson-wheel-index-pin (mirror to project artifact registry + cu126 recommended); D-C7-4 (NEW Cand-2-only) numpy-version-pin (`numpy<2.0.0` recommended); D-C7-5 (NEW Cand-3-only) PyTorch-Jetson-wheel-pin (PyTorch 2.5 + torchvision 0.20 recommended); D-C7-6 (NEW CROSS-COMPONENT C2+C3+C1+C7) INT8-vs-FP16-per-model-family-precision-policy (per-family policy recommended); D-C7-7 (NEW Cand-1-only CROSS-COMPONENT with C10) engine-build-on-Jetson-vs-prebuilt strategy (primary build-on-target + reference-Jetson fallback recommended); D-C7-8 (NEW Cand-1-only) `config.max_workspace_size` cap (1 GB safe default recommended); D-C7-9 (NEW Cand-1-only) TensorRT version pin within JetPack lifecycle (JetPack 6.2 + TensorRT 10.3 recommended). |
| [`C10_preflight_provisioning.md`](C10_preflight_provisioning.md) | **C10** — Pre-flight cache provisioning (CROSS-COUPLING MINIMAL scope per 2026-05-08 user choice C; only D-C6-3 + D-C7-7 confirmation pipelines researched here, operator tooling design deferred to Plan-phase) | #100–#101 (2 facts, **batch 1 closed at 2/N 2026-05-08**) | **D-C6-3 confirmation (Fact #100)**: descriptor-cache rebuild trigger + atomic-write strategy via direct `faiss.write_index`/`faiss.read_index` Python API + `python-atomicwrites` (write-temp + `fsync` + atomic rename) + content-hash (SHA-256) verification gate at takeoff load + `IO_FLAG_MMAP_IFC` mmap load with `madvise(MADV_WILLNEED)` pre-fault + manifest-hash-driven rebuild trigger; FAISS MIT + atomicwrites MIT throughout; FAISS warns "no internal integrity check, expects validated input" — MITIGATED by content-hash gate at takeoff (binds AC-NEW-7 cache-poisoning safety); rebuild-while-not-flying constraint per restrictions.md. **D-C7-7 confirmation (Fact #101)**: hybrid TensorRT engine-build orchestration — Polygraphy CLI primary for INT8-calibrating builds (`polygraphy convert --int8 --calib-cache=<path> ...` Apache-2.0 + Calibrator API replaces hand-written `IInt8EntropyCalibrator2`) + `trtexec` for fast cache-reuse rebuilds (`--fp16 --int8 --calib=<existing_cache>`) + direct `IBuilderConfig` Python API as escape hatch for unusual models (LightGlue dynamic-shape profiles); calibration cache binary-blob reuse keyed by `SHA-256(calib_corpus)` per D-C10-6; engines tied to SM 87 hardware-specific per Source #105 → must be built on deployed Jetson per D-C7-7 closure (D-C10-8 reference-Jetson-at-HQ + deployed-Jetson-copy-to-archive prebuilt-fallback venue); self-describing filename schema `<model>_sm<SM>_jp<JP>_trt<TRT>_<precision>.engine` per D-C10-7; binds AC-4.1/4.2 latency+memory budgets via D-C7-2 mixed-precision flag matrix + D-C7-1 calibration corpus closure. |
| [`MODEB_addendum.md`](MODEB_addendum.md) | **Mode B addendum** — solution_draft01 assessment (2026-05-08) | #102–#113 (12 facts) | Documentary-audit findings (Facts #102–#108): VINS-Mono BSD/GPL deliverable-formatting error (#102), AC-4.1 latency budget overrun (#103), camera calibration unspecified (#104), Suite Sat Service voting-layer contract gap (#105), `00_ac_assessment.md` BLOCKING-gate skip acknowledged (#106), AC-4.5 FC-consumption pathway scope clarification (#107), SQ2 AdHoP + Top-N re-rank sub-stage absence in solution_draft01 architecture (#108). Web-research findings (Facts #109–#113): MAVLink no-default-auth + MAVLink-2.0 message-signing per FC (#109), MegaLoc + UltraVPR D-C2-11 deferred-evaluation revision (#110), `MAV_CMD_SET_EKF_SOURCE_SET` no-deployed-GCS-implementer re-confirmation (#111), OpenCV ≥4.12.0 CVE pin (#112), XoFTR + DINOv2-features cross-modal contrarian evidence (#113). |
| [`C8_fc_adapter.md`](C8_fc_adapter.md) | **C8** — MAVLink / MSP2 FC adapter | #97–#99 (3 facts, **batch 1 closed at 3/N 2026-05-08**) | **Cand 1 RECOMMENDED PRIMARY for ArduPilot**: pymavlink → MAVLink `GPS_INPUT` (msg 232) cooperative-path; `master.mav.gps_input_send(time_usec, gps_id, ignore_flags, time_week_ms, time_week, fix_type, lat, lon, alt, hdop, vdop, vn, ve, vd, speed_accuracy, horiz_accuracy, vert_accuracy, satellites_visible, yaw)` periodic injection at 5 Hz over MAVLink (UART/USB/UDP per D-C8-1); FC-side `GPS1_TYPE=14` MAVLink + `EK3_SRC1_POSXY=3` GPS source-set drives EKF3 ingestion via `AP_GPS_MAV` (verified Source #4 SQ6 + Source #106 + Source #107); pymavlink LGPL-3.0 linkable from Apache-2.0 app per LGPL §6 (D-C8-3 mitigation). **Cand 2 RECOMMENDED PRIMARY for iNav**: `MSP2_SENSOR_GPS` (id 7939 / 0x1F03) via Python MSP V2 (YAMSPy or INAV-Toolkit `msp_v2_encode`); `mspGPSReceiveNewData()` direct passthrough (no validation gate beyond data parse); covariance fields `hPosAccuracy`/`vPosAccuracy`/`hVelAccuracy` align directly with AP `GPS_INPUT.horiz_accuracy`/`vert_accuracy`/`speed_accuracy`; YAMSPy + INAV-Toolkit MIT throughout; `USE_GPS_PROTO_MSP` enabled by default in iNav target/common.h (verified Source #111 + #112 + #113); locked SQ6 + AC-4.3 + restrictions.md transport. **Cand 3 DEFERRED secondary for iNav**: UBX impersonation via pyubx2 NAV-PVT — forging u-blox NAV-PVT frames through standard GPS pipeline; iNav-side `gpsMapFixType()` validation gate requires `flags & 0x01 = 1` (gnssFixOK) AND `fixType ∈ {2,3}` per Source #110 `gps_ublox.c` lines 215-220 + 654; pyubx2 BSD-3-Clause clean dual-use; **does NOT clear user's "significant-improvement-only" bar over Cand 2** — richer protocol surface (NAV-PVT periodic + NAV-VER startup + CFG-MSG/CFG-RATE ACK behaviour) + AC-NEW-7 forgery posture + stricter validation gate + AP-path field-name divergence outweigh pyubx2 library-maturity advantage. **Mid-batch correction**: I caught a contradiction between my own initial AskQuestion phrasing ("UBX impersonation as ONLY iNav path") and locked SQ6 + AC-4.3 + restrictions.md verdicts; user re-locked scope via `c8_inav_recovery=B` to evaluate both as parallel candidates. Decisions: D-C8-1 (NEW Cand-1-only) pymavlink connection-string transport choice (env-driven default-UART recommended); D-C8-2 (NEW Cand-1-only CROSS-COMPONENT with AC-NEW-2) `MAV_CMD_SET_EKF_SOURCE_SET` companion-driven switch ownership pattern (companion publishes to source-set 2 + auto-switches FC recommended); D-C8-3 (NEW Cand-1-only) pymavlink LGPL-3.0 license-posture verification (bundle-unmodified-with-version-pin recommended); D-C8-4 (NEW Cand-2-only) Python MSP V2 implementation choice (YAMSPy primary + thin custom encoder fallback recommended); D-C8-5 (NEW Cand-2-only) MSP2_SENSOR_GPS injection rate (5 Hz periodic recommended); D-C8-6 (NEW Cand-3-only contingent) UBX-version-advertisement strategy (advertise version ≥ 15.0 recommended); D-C8-7 (NEW Cand-3-only contingent CROSS-COMPONENT with AC-NEW-7) AC-NEW-7 audit-trail posture for UBX impersonation (explicit FDR audit entry recommended); D-C8-8 (NEW CROSS-COMPONENT C5+C8) covariance-honesty cross-FC enforcement strategy (per-FC unit conversion recommended via 95% confidence ellipse semi-major axis from C5 GTSAM `Marginals.marginalCovariance`). |
**Cross-cutting consumers** (do not duplicate facts here, just point in):
- The Component Fit Matrix (`../06_component_fit_matrix/`) cites every fact here by `Fact #N` or by candidate row.
---
## Confidence-label legend
| Label | Meaning | Source class |
| --- | --- | --- |
| ✅ High | Source code / official spec / canonical repo verified | L1 (primary code, official docs, published benchmarks) |
| ⚠️ Medium | Authoritative but with stated caveat (out-of-date version, partial coverage, single-source confirmation) | L1 / L2 |
| ❓ Low | Inferential or extrapolated (vendor blog, secondary commentary, candidate not yet runtime-verified on target hardware) | L3 / L4 |
Whenever a candidate is marked **Selected** in `../06_component_fit_matrix/`, its row depends on at least one ✅ High fact in the corresponding C-file plus a `context7` per-mode API capability verification.
---
## Editing rules
1. Add new facts only inside their owning category file. Cross-reference siblings; do not duplicate text.
2. Each fact keeps the existing schema — `### Fact #N — title`, `**Statement**`, `**Source**`, `**Phase**`, `**Confidence**`, `**Sub-Question Binding**`, `**Implication**`.
3. When extending C-rows, also touch the corresponding component file in `../06_component_fit_matrix/` so the matrix stays in sync.
4. Working conclusions and decisions (`D-Cx-y`) live at the bottom of their owning file, not here.
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# Fact Cards — C10: Pre-flight cache provisioning (cross-coupling minimal scope)
> Mode A Phase 2 — engine Step 3 (Fact Extraction & Evidence Cards). Bound to sub-questions in `../00_question_decomposition.md` line 78 (C10 = "Pre-flight cache provisioning + sector classification + freshness pipeline" with 2026-05-08 user-locked CROSS-COUPLING MINIMAL scope per `c10_scope=C` — see "C10 Scope Restructure" section). Sources for C10 cluster live in [`../01_source_registry/C10_preflight_provisioning.md`](../01_source_registry/C10_preflight_provisioning.md).
>
> Index: [`00_summary.md`](00_summary.md). Sibling components: [C1 VIO](C1_vio.md), [C2 VPR](C2_vpr.md), [C3 Matchers](C3_matchers.md), [C4 Pose](C4_pose_estimation.md), [C5 State estimator](C5_state_estimator.md), [C6 Tile cache + spatial index](C6_tile_cache_spatial_index.md), [C7 On-Jetson inference runtime](C7_inference_runtime.md), [C8 MAVLink/MSP2 FC adapter](C8_fc_adapter.md). Cross-component gates: [`../06_component_fit_matrix/99_cross_component_gates.md`](../06_component_fit_matrix/99_cross_component_gates.md).
---
## Scope summary
C10 batch 1 closed at 2/N on 2026-05-08. **Fact #100** = D-C6-3 confirmation pipeline (descriptor-cache rebuild trigger orchestration for the FAISS HNSW index built during C10 pre-flight provisioning + serialized via `faiss.write_index` + atomic-write + content-hash + manifest-driven rebuild trigger + load-at-takeoff via `faiss.read_index` or memory-mapped via `IO_FLAG_MMAP_IFC`). **Fact #101** = D-C7-7 confirmation pipeline (TensorRT engine-build orchestration via Polygraphy CLI primary + `trtexec` simpler fallback + direct `IBuilderConfig` Python API for reference-Jetson-prebuilt-engine generation; calibration corpus shipping mechanism per D-C7-1 closure). User-pinned scope: cross-coupling-minimal — operator CLI/desktop tooling, sector classification heuristics, and freshness pipeline workflow are **deferred to Plan-phase**.
---
### Fact #100 — D-C6-3 confirmation: descriptor-cache rebuild trigger pipeline orchestrated via direct `faiss.write_index` / `faiss.read_index` Python API + atomic-write + content-hash + manifest-driven rebuild trigger + optional `IO_FLAG_MMAP_IFC` load
**Statement**: For C10 (pre-flight cache provisioning, cross-coupling minimal scope), the D-C6-3 descriptor-cache rebuild trigger pipeline (Recommendation = `periodic rebuild during C10 pre-flight provisioning`) is operationalized as the direct FAISS Python API wrapped in a thin project-side orchestration module:
- **Build pipeline (per pre-flight, manifest-hash-driven)**:
1. C10 pre-flight CLI computes `manifest_hash = sha256(descriptor_blobs.sha256, descriptor_dim, faiss_M, ef_construction, vpr_model_sha256)` over the inputs that would change the index content.
2. Compare to `manifest_hash_prev` recorded in `/var/lib/onboard/cache/faiss/manifest.json` from the last successful build.
3. If `manifest_hash != manifest_hash_prev` (or if `manifest.json` is missing): rebuild the FAISS index. Otherwise: skip.
4. Rebuild = `index = faiss.IndexHNSWFlat(d=descriptor_dim, M=faiss_M)` (per D-C6-2 = `IndexHNSWFlat M=32` recommendation) → `index.hnsw.efConstruction = 40` (per Source #96 / Source #114 / C6 Fact #92 canonical pattern) → `index.add(descriptor_blobs)` → write to disk via the atomic-write wrapper (next bullet).
5. Write atomic-write wrapper:
```python
# pseudocode; implementation may use python-atomicwrites package or be hand-rolled per Source #116
temp_path = target_path + ".tmp"
faiss.write_index(index, temp_path) # FAISS writes serialized binary
fd = os.open(temp_path, os.O_RDONLY)
os.fsync(fd) # flush content + metadata to disk
os.close(fd)
os.rename(temp_path, target_path) # POSIX atomic rename (same filesystem)
parent_fd = os.open(os.path.dirname(target_path), os.O_RDONLY | os.O_DIRECTORY)
os.fsync(parent_fd) # flush directory entry change
os.close(parent_fd)
content_hash = sha256(open(target_path, 'rb').read())
manifest = {"manifest_hash": manifest_hash,
"content_hash": content_hash,
"descriptor_dim": descriptor_dim,
"faiss_M": faiss_M,
"ef_construction": ef_construction,
"n_tiles": index.ntotal,
"build_iso8601": now(),
"vpr_model_sha256": vpr_model_sha256,
"build_duration_sec": build_duration_sec}
write_atomic(manifest_path, json.dumps(manifest))
```
6. C10 also records the build event into the AC-NEW-3 FDR record: `(model="faiss_hnsw", manifest_hash, content_hash, build_duration_sec, n_tiles, descriptor_dim)`.
- **Load pipeline (per takeoff)**:
1. Read `/var/lib/onboard/cache/faiss/manifest.json` → recover `expected_content_hash`.
2. Compute `actual_content_hash = sha256(open(target_path, 'rb').read())` (single-pass file read; ~0.5-2 s on JetPack 6 ARM64 NVMe per ~430 MB halfvec file at 2048-D × 100K tiles per Source #115 size formula).
3. Compare: if `actual != expected` → REJECT the cache; emit `STARTUP_FAULT_FAISS_CACHE_HASH_MISMATCH` MAVLink STATUSTEXT to QGC; refuse takeoff (per AC-NEW-7 cache-poisoning safety budget — never silently load a tampered cache file).
4. Otherwise: `index = faiss.read_index(target_path, faiss.IO_FLAG_MMAP_IFC)` (memory-mapped load — zero-copy; <1 s wall-time for the syscall to set up mmap regardless of file size; per Source #114 supports HNSW + IndexFlatCodes-derived classes via the `IO_FLAG_MMAP_IFC` flag).
5. Optional: warmup query at takeoff (issue ~10 dummy `index.search(rand_query, k=10)` calls) to prime the kernel page cache — smooths post-load p99 latency per Source #115 Issue #622 observation.
- **Pinned input/output contract**:
- inputs: `descriptor_blobs[*]` per tile (numpy.ndarray of shape `(n_tiles, descriptor_dim)` and dtype float32 or halfvec per D-C6-1) computed by C10 pre-flight via running C2 VPR backbone over each cached tile image; `vpr_model_sha256` (the C2 VPR model artifact hash) — feeds into `manifest_hash` so a model-swap forces an index rebuild.
- outputs: `<faiss_cache_dir>/v_<descriptor_dim>_M<HNSW_M>.index` (FAISS binary serialization per Source #114) + `<faiss_cache_dir>/manifest.json` (project-defined JSON manifest with content-hash + build provenance).
- runtime: pre-flight build runs on the operator workstation OR on the deployed Jetson (per D-C7-7 = primary build-on-target-Jetson recommendation; the same workflow runs on the deployed Jetson to avoid the C7-style SM 87 hardware-tying constraint that doesn't apply to FAISS — FAISS HNSW serialization is hardware-agnostic and can be built once on any x86/ARM machine and shipped). Load runs on the deployed Jetson at takeoff via `faiss.read_index` Python call.
**Mode pinning** (per-mode API verification rule):
- inputs: `descriptor_blobs: numpy.ndarray of shape (n_tiles, descriptor_dim) and dtype float32 or halfvec`; `descriptor_dim: int ∈ {256, 512, 1024, 2048, 4096}` per D-C2-9/10/6 final lock; `faiss_M: int = 32` per D-C6-2 lock; `ef_construction: int = 40` per Source #96 + C6 Fact #92 canonical pattern; `vpr_model_sha256: str` for manifest-hash binding
- outputs: serialized FAISS index file at canonical path `<faiss_cache_dir>/v_<descriptor_dim>_M<HNSW_M>.index` + manifest.json with content-hash + build provenance + per-takeoff load latency <5 s (mmap path: <1 s; full-load path at 100K × 2048-D halfvec = ~430 MB / SATA SSD ~500 MB/s = ~0.9 s + page-cache warmup ~1-2 s)
- runtime: FAISS-CPU 1.7+ ARM64 wheel via `pip install faiss-cpu` on JetPack 6 + Python 3.10 + NumPy<2.0.0 (per D-C7-4 cross-coupled numpy-version-pin from C7 batch 1 — same pinning applies here since FAISS-CPU shares the numpy ABI dependency)
**Source**:
- Primary FAISS API: Source #114 (`faiss.write_index` / `faiss.read_index` + `IO_FLAG_MMAP_IFC` flag + explicit security warning — canonical FAISS GitHub Wiki + context7 indexed at `/facebookresearch/faiss`)
- File-size + load-latency formula: Source #115 (FAISS GitHub Discussions #3953 + canonical `IndexHNSWFlat` C++ API docs cross-cite — per-vector cost formula `(vector_dim × 4) + (M × 4 × 2) + overhead`)
- Atomic-write pattern: Source #116 (gocept blog reliable Python file updates + python-atomicwrites docs + Python tracker Issue 8604 — write-temp + fsync + atomic rename + parent-dir fsync canonical pattern; aligns with POSIX `rename(2)` atomicity guarantee)
- Cross-cite: C6 Fact #92 (D-C6-3 originating recommendation = periodic rebuild during C10 pre-flight + `faiss.write_index`), C7 Fact #94 (D-C7-1 calibration-dataset-strategy closure that drives the `vpr_model_sha256` provenance binding)
**Phase**: Mode A Phase 2 — engine Step 3 + Step 7.5 (Component Applicability Gate)
**Confidence**: ✅ High — all evidence is L1/L2 with direct API verification; security-warning-driven content-hash gate is the project-side mitigation for the documented FAISS warning; atomic-write pattern is canonical POSIX semantics; FAISS load latency at the project's pinned descriptor dimensions comfortably fits the <5 s takeoff budget via either full-load or mmap path.
**Sub-Question Binding**:
- SQ3+SQ4 → C10 row in `../06_component_fit_matrix/C10_preflight_provisioning.md` (this fact populates the D-C6-3 confirmation candidate row)
- D-C6-3 cross-coupling: closes the C6 ↔ C10 cross-component gate inherited from C6 Fact #92 (`Plan-phase architect + C10 owner` joint ownership)
- AC-NEW-7 (cache-poisoning safety budget): the content-hash verification gate at takeoff is the project-side mitigation for FAISS's documented "no internal integrity check" warning; binds to AC-NEW-7's per-flight forgery-detection contract
- AC-3.3 (re-localization stability): atomic-write + content-hash gate guarantees same-cache-content → same-cache-load → same-result determinism across reboots and pre-flight rebuilds
**Implication / per-numbered-Restriction × per-numbered-AC sub-matrix**:
| Project Restriction / AC | Verdict | Evidence |
|---|---|---|
| **R-NEW-2 no cloud at flight** | ✅ PASS | All FAISS read/write operations are local; `faiss.read_index` opens a local file; no network calls. |
| **R-NEW-4 Jetson Orin Nano Super JetPack 6 ARM64** | ✅ PASS | FAISS-CPU ARM64 wheels are available via `pip install faiss-cpu` (cross-cite C6 Fact #92 + Source #97); no Jetson-specific issues with `faiss.write_index` / `faiss.read_index` / `IO_FLAG_MMAP_IFC` (canonical FAISS Python API works identically on ARM64). |
| **AC-1.x position accuracy** | N/A | Cache file write/read is downstream of accuracy; this fact concerns the descriptor-cache provenance layer. |
| **AC-3.3 re-localization stability** | ✅ PASS | Atomic-write + content-hash gate guarantees deterministic cache load across reboots; rebuild only when manifest hash changes; no silent cache mutation at runtime. |
| **AC-3.4 operator re-loc hint** | ✅ PASS | Operator re-loc hint uses the same loaded FAISS index (no rebuild required at runtime); content-hash gate at takeoff suffices. |
| **AC-4.1 latency budget (<400 ms p95 end-to-end)** | N/A | This is pre-flight + takeoff-load, NOT runtime per-frame. Runtime per-frame latency is governed by C6 Fact #92 (~6-54 ms per cache hit). |
| **AC-4.2 memory budget (<8 GB shared on Jetson)** | ✅ PASS | FAISS index in-memory footprint at the project's pinned descriptor dimensions: ~430 MB at 2048-D halfvec × 100K tiles per Source #115 formula (well within C6 Fact #92's 700 MB-1.5 GB Postgres+FAISS+cache subtotal). With `IO_FLAG_MMAP_IFC` the index is mmap'd from disk on demand — peak RSS reduces further at the cost of a page-fault per first-time access. |
| **AC-4.5 look-back refinement** | N/A | Pre-flight cache + takeoff load are forward-only events. |
| **AC-8.3 10 GB persistent tile cache budget** | ✅ PASS | FAISS index file size at the project's pinned descriptor dimensions: ~430 MB at 2048-D halfvec × 100K tiles + ~80-160 MB at 256-D/512-D halfvec for smaller VPR backbones — fits comfortably within the 10 GB cache budget (well under 5% even at the largest 2048-D variant). |
| **AC-NEW-1 cold-start TTFF (<30 s p95)** | ✅ PASS | Takeoff-load via mmap path: <1 s; full-load path at 430 MB file: ~0.9-2 s; well within the AC-NEW-1 30-second cold-start TTFF budget. Content-hash gate adds ~0.5-2 s for the 430 MB SHA-256 pass; together <5 s — comfortably within budget. |
| **AC-NEW-3 (FDR)** | ✅ PASS | Per-rebuild manifest entry (manifest_hash, content_hash, build_duration_sec, n_tiles, descriptor_dim, vpr_model_sha256) is recordable as an FDR field; per-takeoff load-latency + hash-verification result are recordable as FDR fields. |
| **AC-NEW-4 covariance honesty** | N/A | Pre-flight pipeline is upstream of the C5 estimator; covariance honesty is C5's contract. |
| **AC-NEW-7 cache-poisoning safety budget** | ✅ PASS at the FAISS-cache layer | Content-hash gate at takeoff load REJECTS cache files that don't match the manifest (per Source #114 explicit security warning); atomic-write pattern (Source #116) prevents partial-write corruption from masquerading as a valid cache; manifest-hash-driven rebuild triggers ensure that a model swap forces a rebuild with new content hash. **Cross-flight cache poisoning** (per AC-NEW-7's "P(geo-misalign >30 m) <1%" budget) is upstream of C10 — it's the C6 Fact #92 + AC-8.4 mid-flight tile generation responsibility plus the Suite Service voting layer per AC-NEW-7 external-dependency note. |
| **AC-NEW-8 blackout failsafe** | ✅ PASS | Pre-flight pipeline doesn't run during flight; if the FAISS cache is corrupt at takeoff, the cache-hash-mismatch gate refuses takeoff (which is safer than launching with a bad cache). C5 demotion to `dead_reckoned` is the runtime failsafe path, not the pre-flight one. |
**Strengths** (positive structural advantages):
1. **Direct FAISS API — minimal abstraction surface**. No additional library dependency beyond FAISS-CPU (already required by C6 Fact #92); no orchestration framework to maintain. The atomic-write wrapper is ~30 lines of Python; trivially auditable; works identically across operator workstation + deployed Jetson environments.
2. **Manifest-hash-driven rebuild trigger** — idempotent (skip rebuild if no change); minimum-rebuild semantics (rebuild only when descriptor_blobs OR vpr_model_sha256 OR descriptor_dim changes); aligns naturally with C10 pre-flight workflow (descriptor blobs change when tiles are pulled/refreshed; VPR model changes only on dev-side model swap).
3. **Content-hash verification gate at takeoff** — operationalizes the FAISS security warning as project-side AC-NEW-7 coverage; never silently loads a tampered cache file.
4. **Atomic-write pattern guarantees crash safety** — power loss or process kill mid-build leaves the previous valid cache file intact (per POSIX `rename(2)` atomicity); next pre-flight rebuild detects the manifest mismatch and rebuilds cleanly.
5. **Optional mmap load path (`IO_FLAG_MMAP_IFC`)** — zero-copy load syscall completes in <1 s regardless of file size; reduces takeoff RSS pressure; canonical FAISS HNSW + IndexFlatCodes-derived support per Source #114.
6. **Hardware-agnostic FAISS serialization** — index can be built on the operator workstation (x86) and shipped to the Jetson (ARM64) without rebuild (vs C7's SM 87 hardware-tying constraint for TensorRT engines). Useful for the prebuilt-fallback path.
7. **License clean throughout** — FAISS (MIT); python-atomicwrites if used (MIT); no GPL contagion path on this orchestration layer.
**Negative-but-mitigable structural findings**:
8. **No FAISS-internal integrity check on `read_index`** (per Source #114 explicit warning) — must be mitigated project-side via the content-hash gate above. Without that gate, AC-NEW-7 fails. **Mitigation**: project-side ~5 lines of Python (open file → SHA-256 → compare to manifest) before the `read_index` call; cost ~0.5-2 s at takeoff for a 430 MB cache file.
9. **Atomic-write pattern is project-side, not FAISS-internal** — must be hand-rolled or via `python-atomicwrites`. **Mitigation**: ~30 lines of Python; well-documented canonical POSIX pattern per Source #116; trivially auditable.
10. **Manifest-hash binding requires VPR model SHA-256** — implies the C2 VPR model artifact has a stable SHA-256 (i.e., a versioned ONNX-or-engine file is checked into the cache directory or referenced from a versioned URI). **Mitigation**: standard ML artifact versioning; aligns with the C7 Fact #94 + C7 Fact #95 + C7 Fact #96 ONNX export pathway (each ONNX export is a binary file with a deterministic hash).
11. **Mmap path RAM behavior depends on OS page cache pressure** — if other workloads consume RAM, mmap'd FAISS index pages may be evicted and re-faulted at runtime, adding ~1-5 ms per evicted page-fault to per-frame query latency. **Mitigation**: `mlock` / `madvise(MADV_WILLNEED)` syscalls available in Python via `mmap.MADV_WILLNEED` to pre-fault the pages; cost: one-time at takeoff (~1-2 s for the 430 MB file). At 8 GB shared budget (with C6 Fact #92's 700 MB-1.5 GB total subtotal) there's ample headroom for keeping the mmap'd index resident.
**Caveats / open Plan-phase decisions raised** (D-C10-N gates):
- **D-C10-1 NEW** — descriptor-cache rebuild trigger choice (manifest-hash-driven [recommended] / always-rebuild-every-pre-flight / operator-manual flag): trade-off between idempotency vs simplicity vs operator control. **Recommendation**: D-C10-1 = (a) manifest-hash-driven (idempotent + minimum-rebuild + operator-manual override flag `--force-rebuild` available).
- **D-C10-2 NEW** — descriptor-cache atomic-write strategy (write-temp+fsync+rename hand-rolled / `python-atomicwrites` package / accept-non-atomic-write-and-pray): trade-off between dependency surface vs implementation cost vs crash safety. **Recommendation**: D-C10-2 = (b) `python-atomicwrites` (MIT, ~zero-cost dependency, cross-platform, well-tested in production); fallback (a) hand-rolled if dependency-policy gate prefers in-tree.
- **D-C10-3 NEW (CROSS-COMPONENT with AC-NEW-7)** — content-hash verification gate at takeoff load (yes — REJECT cache + STATUSTEXT + refuse takeoff [recommended] / yes — WARN + load anyway / no — trust filesystem): trade-off between safety vs availability vs operator-friction. **Recommendation**: D-C10-3 = (a) reject-and-refuse-takeoff; AC-NEW-7 cache-poisoning budget makes silent acceptance unsafe; operator can re-run pre-flight with `--force-rebuild` to cleanly recover.
- **D-C10-4 NEW** — descriptor-cache load path (full-`read_index` / mmap via `IO_FLAG_MMAP_IFC` [recommended] / both available via env flag): trade-off between determinism (full-load is fully resident; mmap RSS depends on page cache) vs takeoff latency (mmap is faster) vs runtime page-fault sensitivity. **Recommendation**: D-C10-4 = (b) mmap with optional `madvise(MADV_WILLNEED)` pre-fault at takeoff (~1-2 s additional cost; eliminates runtime page-faults for the lifetime of the flight) OR (c) both available for Plan-phase Jetson MVE comparison.
---
### Fact #101 — D-C7-7 confirmation: TensorRT engine-build pipeline orchestrated via Polygraphy CLI (primary) + `trtexec` (simpler fallback) + direct `IBuilderConfig` Python API (reference-Jetson-prebuilt-engine fallback generation)
**Statement**: For C10 (pre-flight cache provisioning, cross-coupling minimal scope), the D-C7-7 TensorRT engine-build pipeline (Recommendation = `primary build-on-deployed-Jetson during pre-flight + reference-Jetson-built engines as fallback`) is operationalized as a three-tool orchestration matrix:
- **Primary path: Polygraphy CLI on the deployed Jetson during pre-flight** (per D-C7-7 = primary build-on-target):
```bash
polygraphy convert <model>.onnx \
--int8 --fp16 \
--data-loader-script ./calib_data_loader.py \
--calibration-cache <calib_cache_dir>/<model>_calib.cache \
--workspace=1000000000 \
-o <engine_cache_dir>/<model>_sm87_jp62_trt103_<precision>.engine
```
- First build per-model: `--data-loader-script` reads the project's pinned calibration corpus per D-C7-1 closure (real UAV nadir flight footage at ~1 km AGL over season-matched satellite tiles; ~500-1500 representative samples per Source #120) and runs INT8 calibration; the resulting calibration scales are written to `--calibration-cache` for subsequent builds.
- Subsequent rebuilds (when calibration corpus is unchanged): `polygraphy convert ... --calibration-cache <existing_cache>` — calibration step is skipped per Source #117 ("If the provided path does exist, it will be read and int8 calibration will be skipped during engine building").
- Per-model precision flags follow D-C7-2 / D-C7-6 cross-component policy: VPR backbones (CNN-class) → `--int8 --fp16`; ViT-class VPR + matchers + learned VIO → `--fp16` only (NO `--int8`).
- `--workspace=1000000000` (1 GB cap) per D-C7-8 lock to prevent tactic-profile segfault on 8 GB shared budget.
- On-disk engine filename incorporates SM 87 + JetPack 6.2 + TRT 10.3 + precision tag (per D-C7-9 lock) so the runtime can reject a cached engine that was built for a different SM/JP/TRT/precision combination.
- **Simpler fallback: `trtexec` CLI** (when calibration cache already exists or for ad-hoc/emergency rebuilds):
```bash
trtexec --onnx=<model>.onnx \
--saveEngine=<engine_cache_dir>/<model>_sm87_jp62_trt103_<precision>.engine \
--fp16 --int8 \
--calib=<calib_cache_dir>/<model>_calib.cache \
--shapes=input:1x3x224x224 \
--workspace=1000
```
- Faster invocation (no Python imports; single C++ binary).
- Calibration cache file format is interoperable with Polygraphy's per Source #119 — caches built by Polygraphy are loadable by `trtexec` and vice versa.
- Used as fallback when Polygraphy is unavailable (e.g., minimal install) OR for reference-Jetson-prebuilt-engine generation when no calibration data shipping is needed.
- Critical caveat: `trtexec --int8` without `--calib` falls back to RANDOM data calibration → ~5-15% INT8 accuracy collapse → forbidden in the project's C10 path (always supply `--calib` from the existing calibration cache).
- **Reference-Jetson-prebuilt-engine fallback generation** (per D-C7-7 fallback path, for emergency provisioning): direct TensorRT `IBuilderConfig` + `IInt8EntropyCalibrator2` Python API per Source #121 — used when Polygraphy's `--data-loader-script` abstraction is too rigid for an unusual model (e.g., LightGlue with dynamic-shape inputs requiring a custom calibration profile per D-C3-2 + D-C3-3). Output: a versioned `.engine` file shipped to the deployed Jetson alongside the calibration cache file. The deployed Jetson at takeoff loads this prebuilt engine via `IRuntime.deserializeCudaEngine` (no on-Jetson rebuild required for the fallback path).
- **Manifest-hash + content-hash + atomic-write** (same pattern as Fact #100):
- `manifest_hash = sha256(model_onnx.sha256, calibration_corpus.sha256, precision_mode, sm_version, jp_version, trt_version)` per engine.
- `content_hash = sha256(<engine>.engine)` after build.
- Atomic-write wrapper around the engine file output (Polygraphy + trtexec both write to a temp path inside their respective CLIs, but the project-side wrapper enforces the rename-into-position step on top to maintain crash safety across the broader pre-flight workflow).
- Per-engine manifest entry recorded in `<engine_cache_dir>/manifest.json`: `(model, precision_mode, calib_corpus_sha256, build_iso8601, build_duration_sec, content_hash, sm_version, jp_version, trt_version)`.
- **Pinned input/output contract**:
- inputs: `<model>.onnx` per inference target (C2 VPR backbone + C3 matcher + optional C1 learned VIO frontend, exported on the dev machine via `torch.onnx.export`); `calibration_corpus` per D-C7-1 closure (real UAV nadir flight footage at ~1 km AGL over season-matched satellite tiles in NumPy `.npy` or Torch `.pt` tensor format); `<calib_cache>` per Polygraphy/trtexec INT8 calibration cache file (project-side ships the calibration corpus + the calibration cache; cache is reusable across rebuilds when the corpus hash is unchanged).
- outputs: per-model `.engine` file at canonical path `<engine_cache_dir>/<model>_sm87_jp62_trt103_<precision>.engine` + per-engine manifest entry in `<engine_cache_dir>/manifest.json` + AC-NEW-3 FDR record.
- runtime context: pre-flight build runs ON the deployed Jetson Orin Nano Super (per D-C7-7 = primary build-on-target — per Source #105 SM 87 hardware-tying constraint). Reference-Jetson-prebuilt-engine fallback runs on a known-good HQ Jetson (same SM 87 / JetPack 6.2 / TensorRT 10.3 — per D-C7-9 lock).
**Mode pinning** (per-mode API verification rule):
- inputs: `<model>.onnx: bytes` (ONNX graph from `torch.onnx.export`); `calibration_corpus: numpy.ndarray of shape [N=500-1500, C=3, H=224-320, W=224-320] and dtype float32 normalized to [0, 1]` per project's pinned VPR + matcher input shapes per D-C2-3 / D-C2-5 / D-C3-3; `precision_mode: str ∈ {'int8+fp16', 'fp16'}` per D-C7-6 per-family policy
- outputs: serialized TensorRT engine file `.engine` + calibration cache file `.cache` (interoperable between Polygraphy and trtexec per Source #119) + manifest entry
- runtime: TensorRT 10.3 + CUDA 12.6 + cuDNN 9.3 on JetPack 6.2 + Polygraphy bundled with TensorRT distribution OR `pip install nvidia-pyindex && pip install polygraphy` (Polygraphy is pure Python; ARM64 Python + TensorRT Python bindings sufficient)
**Source**:
- Primary Polygraphy CLI: Source #117 NVIDIA/TensorRT GitHub `tools/Polygraphy/examples/cli/convert/01_int8_calibration_in_tensorrt/README.md` + canonical Polygraphy docs context7 indexed at `/websites/nvidia_deeplearning_tensorrt_static_polygraphy` (1041 code snippets, Source Reputation High)
- Polygraphy `Calibrator` class API: Source #118 canonical NVIDIA TensorRT/Polygraphy SDK documentation (entropy/min-max algo defaults, dynamic-shapes calibration profile, data-loader-script + calibration-cache CLI flags)
- `trtexec` CLI: Source #119 canonical NVIDIA TensorRT SDK documentation (`--onnx --saveEngine --int8 --fp16 --calib --shapes --workspace` flag set; calibration cache format interoperability with Polygraphy)
- Calibration corpus size guidance: Source #120 vendor-aligned engineering guide (500-1000 image recommendation; cross-cite to project's D-C7-1 closure 500-1500 sample range)
- Direct `IBuilderConfig` Python API: Source #121 (cross-cite from C7 batch 1 Source #102 + Source #105) — used for reference-Jetson-prebuilt-engine fallback generation
- Cross-cite: C7 Fact #94 (D-C7-7 originating recommendation = primary build-on-deployed-Jetson + fallback prebuilt; D-C7-8 = 1 GB workspace; D-C7-9 = JetPack 6.2 + TRT 10.3 lock); C7 Fact #94 (D-C7-1 closure = real UAV nadir flight footage as calibration corpus distribution; specific fixture pin delegated to Test Spec)
**Phase**: Mode A Phase 2 — engine Step 3 + Step 7.5 (Component Applicability Gate)
**Confidence**: ✅ High for Polygraphy + trtexec API capability verification (L1 canonical NVIDIA docs); ✅ High for the orchestration pattern (canonical NVIDIA-blessed workflow per Source #117 README); ⚠️ Medium for the specific build-duration-on-Jetson-Orin-Nano-Super claim (extrapolated from C7 Fact #94 reference of "30-300 sec per model" + Source #105 constraints — exact build-duration depends on model complexity + INT8 calibration scope; needs Plan-phase Jetson MVE confirmation per D-C1-2)
**Sub-Question Binding**:
- SQ3+SQ4 → C10 row in `../06_component_fit_matrix/C10_preflight_provisioning.md` (this fact populates the D-C7-7 confirmation candidate row)
- D-C7-7 cross-coupling: closes the C7 ↔ C10 cross-component gate inherited from C7 Fact #94 (`Plan-phase architect + C10 owner` joint ownership)
- D-C7-1 closure (real UAV nadir flight footage corpus): C10 owns the calibration-corpus assembly at pre-flight; specific fixture-file pin remains delegated to Test Spec per the 2026-05-08 C9 / SQ7 restructure
- AC-NEW-1 (cold-start TTFF <30 s p95): pre-flight engine build is amortized across all takeoffs that use the same artifacts; takeoff-load via `IRuntime.deserializeCudaEngine` is ~100-500 ms per engine × 3-5 engines = ~0.5-2.5 s — well within 30 s budget
- AC-NEW-3 (FDR): per-engine manifest entry recorded as FDR field
- AC-NEW-7 (cache-poisoning safety): same content-hash + atomic-write pattern as Fact #100 protects the engine cache file against partial-write corruption
**Implication / per-numbered-Restriction × per-numbered-AC sub-matrix**:
| Project Restriction / AC | Verdict | Evidence |
|---|---|---|
| **R-NEW-2 no cloud at flight** | ✅ PASS | All Polygraphy/trtexec invocations are local CLI subprocess calls; engine build runs entirely on the deployed Jetson. |
| **R-NEW-4 Jetson Orin Nano Super JetPack 6 ARM64** | ✅ PASS | Polygraphy is pure Python (works on ARM64 + Python 3.10); trtexec is bundled with TensorRT 10.3 in JetPack 6.2 (installed by default at `/usr/src/tensorrt/bin/trtexec`); both interoperate with the JetPack-bundled TensorRT 10.3 per Source #117 + Source #119. |
| **AC-1.x position accuracy** | N/A | Engine build is upstream of accuracy; this fact concerns the engine provenance layer. |
| **AC-3.x resilience** | N/A | Engine cache is a takeoff-load artifact; runtime resilience is C5/C8 responsibility. |
| **AC-4.1 latency budget (<400 ms p95 end-to-end)** | N/A | Engine build is pre-flight + takeoff-load, NOT runtime per-frame. Per-engine inference latency is governed by C7 Fact #94 / Fact #95 / Fact #96. |
| **AC-4.2 memory budget (<8 GB shared on Jetson)** | ✅ PASS | Per Source #105 + D-C7-8: Polygraphy/trtexec engine build with `--workspace=1000` (1 GB cap) holds peak build-time memory at ~3-5 GB out of 8 GB shared (build-time peak; runtime is much lower per C7 Fact #94 ~50-150 MB shared library + ~50-300 MB per engine). Pre-flight build is performed when no other workloads are active, so the 5 GB peak is acceptable. |
| **AC-4.5 look-back refinement** | N/A | Engine build pipeline is forward-only. |
| **AC-8.3 10 GB persistent tile cache budget** | ✅ PASS | Engine `.engine` files at 10-200 MB each per C7 Fact #94 × 3-5 engines = ~100-500 MB on disk (separate from the 10 GB tile cache; lives at `/var/lib/onboard/cache/trt/` or equivalent). Calibration cache files at 1-10 MB each are negligible. |
| **AC-NEW-1 cold-start TTFF (<30 s p95)** | ✅ PASS | Takeoff-load via `IRuntime.deserializeCudaEngine` is ~100-500 ms per engine × 3-5 engines = ~0.5-2.5 s; combined with FAISS load <5 s (Fact #100) and content-hash gates total ~5-10 s, well within 30 s budget. **Build is pre-flight, NOT during cold-start** — engines are pre-built during pre-flight provisioning and persisted across reboots. |
| **AC-NEW-3 (FDR)** | ✅ PASS | Per-engine manifest entry (model, precision_mode, calib_corpus_sha256, build_iso8601, build_duration_sec, content_hash, sm_version, jp_version, trt_version) is recordable as an FDR field per AC-NEW-3 forensic trail requirement. |
| **AC-NEW-4 covariance honesty** | N/A | Engine build pipeline is upstream of the C5 estimator. |
| **AC-NEW-7 cache-poisoning safety budget** | ✅ PASS at the engine-cache layer | Same content-hash + atomic-write pattern as Fact #100 (project-side wrapper around Polygraphy/trtexec output); engine-cache poisoning is detected at takeoff load via SHA-256 verification; manifest-hash binding guarantees that a calibration-corpus swap or ONNX-model swap forces a clean rebuild with new content hash. The reference-Jetson-prebuilt-engine fallback path uses a versioned `.engine` artifact that is signed/checksummed at the HQ source-of-truth (the project's release pipeline owns this signing). |
| **AC-NEW-8 blackout failsafe** | ✅ PASS | Engine cache is loaded at takeoff; if a content-hash mismatch is detected, takeoff is refused (same posture as Fact #100). C5 demotion to `dead_reckoned` is the runtime failsafe path, not the pre-flight one. |
**Strengths** (positive structural advantages):
1. **Polygraphy is the canonical NVIDIA-blessed orchestration tool** for TensorRT engine builds with INT8 calibration cache reuse — first-party support, multi-snippet docs coverage, production-mature; eliminates the need to write the calibrator + data-loader + builder-config glue code from scratch.
2. **Calibration cache reuse across rebuilds** — first build per-model takes ~30-300 sec including INT8 calibration (per C7 Fact #94 reference); subsequent rebuilds skip the calibration step (per Source #117 explicit "calibration will be skipped" semantics) — typically <30 sec even for the most complex matchers. Critical for fast iteration during the operator's pre-flight workflow.
3. **CLI interoperability between Polygraphy and trtexec** — the calibration cache file format is identical between the two tools per Source #119; the project can use Polygraphy for the canonical INT8-calibration-bearing build and trtexec for emergency/ad-hoc rebuilds without re-shipping calibration data.
4. **Mixed-precision flag matrix matches D-C7-2 / D-C7-6 cross-component policy** — `--int8 --fp16` is the canonical Polygraphy/trtexec invocation for the project's per-family mixed precision per Source #117 + Source #119.
5. **`--load-tactics` / `--save-tactics` for reference-Jetson-prebuilt-engine workflow** — Polygraphy supports replaying tactic-search results across multiple builds (per Source #118); the project can ship the tactic replay file alongside the prebuilt engine for fast on-Jetson rebuild without re-running tactic profiling.
6. **Direct `IBuilderConfig` Python API as escape hatch** — for unusual models requiring custom calibration profiles (e.g., LightGlue with dynamic-shape inputs per D-C3-2 + D-C3-3) the project can drop down to the direct TensorRT Python API per Source #121 without abandoning the orchestration framework.
7. **Pre-flight build amortized across all takeoffs** — engine cache is persistent; build runs only when calibration corpus or ONNX model changes (manifest-hash-driven); typical operator workflow is: build once at HQ ship → operator pulls fresh tile cache → operator triggers pre-flight (FAISS rebuild + maybe TRT rebuild if calibration-corpus refreshed) → takeoff.
8. **License clean throughout** — Polygraphy (Apache-2.0); TensorRT (Apache-2.0 in TensorRT 10.x per C7 Fact #94); python-atomicwrites (MIT); no GPL contagion path on this orchestration layer.
**Negative-but-mitigable structural findings**:
9. **First-build INT8 calibration takes 30-300 sec per model on Jetson** — large matcher models (e.g., LightGlue at K=1024 keypoints) can hit the upper end of this range. **Mitigation**: calibration cache reuse — once the cache is built, subsequent rebuilds are <30 sec; first build at HQ + ship cache to operator workstation pre-deployment.
10. **Engine cache is hardware-specific (SM 87)** per C7 Fact #94 + Source #105 — can't ship engines across Jetson hardware variants. **Mitigation**: D-C7-7 = (c) primary-build-on-target with reference-Jetson-prebuilt-engine fallback ONLY for SM 87 / JetPack 6.2 / TRT 10.3 combinations; the project's deployed fleet is uniform per restrictions.md (Jetson Orin Nano Super pinned).
11. **Polygraphy CLI requires `pip install polygraphy` separately if not bundled with TensorRT distribution** — minimal Jetson installs may need `pip install nvidia-pyindex && pip install polygraphy`. **Mitigation**: include in the project's pre-flight Docker image / OS image bake; verify at C10 setup.
12. **`trtexec --int8` without `--calib` falls back to random-data calibration** with documented ~5-15% INT8 accuracy collapse per Source #119. **Mitigation**: project-side wrapper around `trtexec` invocation enforces `--calib=<existing_cache>` non-empty as a precondition; reject the build otherwise with clear error message.
13. **Build-time peak memory ~3-5 GB out of 8 GB shared** per Source #105 constraint #4 + D-C7-8 — not safe to run pre-flight build concurrently with other heavy workloads (e.g., camera pipeline, FAISS build). **Mitigation**: pre-flight orchestration is sequential — build TRT engines one at a time, then FAISS index, then verification; takes ~5-15 min total at first-build (with calibration); ~1-3 min for subsequent rebuilds (cache-reused).
14. **Calibration-corpus shipping mechanism** — per D-C7-1 closure the corpus is real UAV nadir flight footage at ~1 km AGL; this corpus is several GB of tensor data. **Mitigation**: ship calibration corpus + calibration cache together as a versioned artifact bundle; ship cache only (not raw corpus) to operators when the cache is sufficient (i.e., fixture-pin from Test Spec is stable and operators don't need to recalibrate).
**Caveats / open Plan-phase decisions raised** (D-C10-N gates):
- **D-C10-5 NEW (CROSS-COMPONENT with C7)** — TensorRT engine-build orchestration tool choice (Polygraphy CLI primary [recommended] / `trtexec` CLI primary / direct `IBuilderConfig` Python API primary / hybrid: Polygraphy for INT8-calibrating builds + `trtexec` for cache-reuse rebuilds + direct API for unusual models): trade-off between orchestration sophistication vs install footprint vs flexibility. **Recommendation**: D-C10-5 = (d) hybrid — Polygraphy for INT8-calibrating builds (canonical NVIDIA tool, multi-snippet docs, supports custom data loaders); `trtexec` for cache-reuse fast rebuilds (single binary, no Python imports, faster invocation); direct `IBuilderConfig` Python API as escape hatch for unusual models (e.g., LightGlue dynamic shapes per D-C3-2 + D-C3-3).
- **D-C10-6 NEW (CROSS-COMPONENT with D-C7-1)** — TensorRT calibration-cache reuse strategy (always reuse if cache file exists [most-aggressive] / rebuild on calib-corpus SHA-256 change [recommended] / rebuild every pre-flight [most-conservative]): trade-off between rebuild cost vs calibration-data freshness vs operator-workflow simplicity. **Recommendation**: D-C10-6 = (b) rebuild on calib-corpus SHA-256 change — manifest-hash-driven rebuild trigger from Fact #100 pattern naturally extends to TRT engine cache; idempotent + minimum-rebuild + operator-manual override flag `--force-trt-rebuild` available.
- **D-C10-7 NEW** — TensorRT engine on-disk filename schema (`<model>_sm<SM>_jp<JP>_trt<TRT>_<precision>.engine` [recommended] / hash-only filename / opaque content-addressable storage with separate manifest mapping): trade-off between operator-debuggability vs filesystem-simplicity vs versioning-rigor. **Recommendation**: D-C10-7 = (a) `<model>_sm<SM>_jp<JP>_trt<TRT>_<precision>.engine` self-describing filename + manifest.json side-cache; runtime can reject a cached engine that doesn't match the deployed Jetson's SM/JP/TRT combination with a clear error message at takeoff load.
- **D-C10-8 NEW** — TensorRT prebuilt-fallback engine generation venue (reference Jetson at HQ [recommended] / CI pipeline with Jetson-class runner / deployed Jetson copy-to-HQ-archive after first successful local build): trade-off between reproducibility vs CI cost vs reduced pre-flight risk. **Recommendation**: D-C10-8 = (a) reference Jetson at HQ + (c) deployed-Jetson-copy-to-archive on first successful local build for opportunistic redundancy; both venues use the same Polygraphy/trtexec pipeline so artifacts are interchangeable; HQ-built engines serve as authoritative fallbacks signed by the project's release pipeline.
---
## C10 — Working conclusions and decisions (compounded from Fact #100 + Fact #101 closures)
**Selected primary**:
- **D-C6-3 confirmation**: descriptor-cache rebuild trigger pipeline orchestrated via direct `faiss.write_index` / `faiss.read_index` Python API + `python-atomicwrites` (or hand-rolled atomic-write) + content-hash verification gate at takeoff + manifest-hash-driven rebuild trigger + optional `IO_FLAG_MMAP_IFC` mmap load path with `madvise(MADV_WILLNEED)` pre-fault. **Closes the C6 ↔ C10 cross-component gate.**
- **D-C7-7 confirmation**: TensorRT engine-build pipeline orchestrated via the **hybrid** tool matrix per D-C10-5 = (d): Polygraphy CLI for INT8-calibrating builds (primary) + `trtexec` for cache-reuse fast rebuilds + direct `IBuilderConfig` Python API for unusual models (LightGlue dynamic shapes). Reference-Jetson-prebuilt-engine fallback per D-C10-8 = (a)+(c). Calibration corpus per D-C7-1 closure (real UAV nadir flight footage at ~1 km AGL over season-matched satellite tiles; specific fixture-file pin delegated to Test Spec). **Closes the C7 ↔ C10 cross-component gate.**
**Decisions raised (D-C10-N gates)** — see [`../06_component_fit_matrix/99_cross_component_gates.md`](../06_component_fit_matrix/99_cross_component_gates.md):
- **D-C10-1** (Fact #100) — descriptor-cache rebuild trigger choice: manifest-hash-driven / always-rebuild / operator-manual — RECOMMENDED manifest-hash-driven + `--force-rebuild` override
- **D-C10-2** (Fact #100) — descriptor-cache atomic-write strategy: hand-rolled / `python-atomicwrites` / no-atomic — RECOMMENDED `python-atomicwrites` (fallback hand-rolled if dependency-policy gate prefers in-tree)
- **D-C10-3** (Fact #100, CROSS-COMPONENT with AC-NEW-7) — content-hash verification gate at takeoff load: reject + STATUSTEXT + refuse takeoff / warn + load anyway / no — RECOMMENDED reject + STATUSTEXT + refuse takeoff
- **D-C10-4** (Fact #100) — descriptor-cache load path: full-`read_index` / mmap via `IO_FLAG_MMAP_IFC` / both via env flag — RECOMMENDED mmap with `madvise(MADV_WILLNEED)` pre-fault (or both for Plan-phase Jetson MVE)
- **D-C10-5** (Fact #101, CROSS-COMPONENT with C7) — TensorRT engine-build orchestration tool choice: Polygraphy primary / trtexec primary / direct API primary / hybrid — RECOMMENDED hybrid (Polygraphy + trtexec + direct API by use case)
- **D-C10-6** (Fact #101, CROSS-COMPONENT with D-C7-1) — TensorRT calibration-cache reuse strategy: always-reuse / rebuild-on-calib-corpus-SHA-256-change / rebuild-every-pre-flight — RECOMMENDED rebuild-on-calib-corpus-SHA-256-change + `--force-trt-rebuild` override
- **D-C10-7** (Fact #101) — TensorRT engine on-disk filename schema: self-describing `<model>_sm<SM>_jp<JP>_trt<TRT>_<precision>.engine` / hash-only / content-addressable + manifest — RECOMMENDED self-describing filename + manifest.json side-cache
- **D-C10-8** (Fact #101) — TensorRT prebuilt-fallback engine generation venue: reference Jetson at HQ / CI pipeline with Jetson-class runner / deployed-Jetson-copy-to-HQ-archive on first successful local build — RECOMMENDED reference Jetson at HQ + deployed-Jetson-copy-to-archive (opportunistic redundancy)
C10 batch 1 closed at 2/N on 2026-05-08 (cross-coupling minimal scope per `c10_scope=C` user choice). Operator CLI/desktop tooling, sector classification heuristics, freshness pipeline workflow remain **deferred to Plan-phase as `operator tooling design` out-of-research-scope**. **No further C10 batches required at the research layer** — D-C6-3 and D-C7-7 are now closed; remaining C10 questions are operational/UX, not architectural.
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# Fact Cards — C1: Visual / Visual-Inertial Odometry
> Mode A Phase 2 — engine Step 3 (Fact Extraction & Evidence Cards). Extracted from sources logged in `../01_source_registry/C1_vio.md` (see `../01_source_registry/00_summary.md` for index). Confidence labels: ✅ High (L1 / verified source code), ⚠️ Medium (L1/L2 with caveat), ❓ Low (L3/L4 inferential). Bound to sub-questions in `../00_question_decomposition.md`.
>
> Index: [`../00_summary.md`](../00_summary.md). Sibling categories: SQ6 ([FC external positioning](SQ6_fc_external_positioning.md)), SQ1 ([existing systems](SQ1_existing_systems.md)), SQ2 ([canonical pipeline](SQ2_canonical_pipeline.md)), C2 ([VPR](C2_vpr.md)), C3 ([matchers](C3_matchers.md)).
**Facts in this file**: VIO candidate enumeration (VINS-Mono, VINS-Fusion, OpenVINS, OKVIS2, Kimera-VIO, DROID-SLAM, DPVO, KLT+RANSAC baseline) + Plan-phase decisions D-C1-1, D-C1-2 + C1 working conclusions.
---
## SQ3+SQ4 / C1 — Visual / Visual-Inertial Odometry candidate enumeration
> **Project's pinned mode for every C1 candidate (binding)**: monocular ADTi 20MP nav camera @ 3 fps + IMU from FC over MAVLink @ ≥100 Hz, on Jetson Orin Nano Super (JetPack/CUDA/TensorRT, 8 GB shared LPDDR5, 25 W TDP), producing relative 6-DoF metric pose between consecutive frames + per-axis covariance, with attitude (yaw + pitch) hard-contract σ ≤ 5° at 1 σ (Fact #24), output cadence ≥3 Hz, no in-flight network, license compatible with onboard-binary distribution to a dual-use customer.
>
> Per the engine's "Per-Mode API Capability Verification" rule, any candidate marked `Selected` requires a `context7` lookup (mode enum + project's exact mode runnable example + disqualifier probe) AND a per-numbered-Restriction × per-numbered-AC sub-matrix. **This session covers candidate enumeration + preliminary applicability assessment only**; `context7` verification and the structured sub-matrix are deferred to the next session per the autodev context budget heuristic.
### Fact #28 — VINS-Mono is a canonical monocular-only sliding-window VIO with a working Jetson-Nano deployment record but no GitHub release and ~24-month-old master branch
- **Statement**: VINS-Mono is the canonical mono+IMU sliding-window VIO from HKUST-Aerial-Robotics (Qin, Li, Shen — IEEE T-RO 2018). Features: efficient IMU pre-integration, automatic initialization, online camera-IMU spatial + temporal calibration, failure detection + recovery, DBoW2 loop detection, global pose-graph optimization. Output: metric-scale 6-DoF pose at IMU rate. **Repository state**: master-branch only (no tagged releases), 5,829 stars; last meaningful master-branch commit 2024-02-25 with a 2024-05-23 simulation-data commit. **Jetson record**: a 2021 IEICE paper (zinuok / KAIST) demonstrated VINS-Mono real-time on the original Jetson Nano (much weaker than Orin Nano Super) for MAV state estimation; a 2024 arXiv paper (2406.13345) showed an enhanced VINS-Mono variant achieving 50 FPS on a Raspberry Pi CM4 with on-sensor accelerated optical flow. **License**: GPL-3.0 (copyleft viral) — distribution of the onboard binary requires source disclosure for the entire linked binary and triggers GPL-3 anti-tivoization clauses for embedded firmware.
- **Source**: Source #43 (canonical), Source #46 (KAIST Jetson benchmark), Source #43-linked LICENCE for license confirmation
- **Phase**: Phase 2
- **Target Audience**: System architects + C1 implementer
- **Confidence**: ✅ for algorithm class, mode support, and Jetson Nano feasibility; ⚠️ for Jetson Orin Nano Super specific latency (no direct measurement — but Orin Nano Super >> Jetson Nano, so feasibility is virtually certain); ⚠️ for the maintenance-status risk implied by ~24-month-old master branch.
- **Related Dimension**: SQ3+SQ4 / C1 Established-production candidate
- **Fit Impact**: **carry as lead candidate, conditional on user license decision.** Algorithmic fit is excellent (canonical mono+IMU VIO with metric scale and covariance); maintenance status is borderline; **GPL-3.0 license is a project-level decision required from the user** before this candidate can be marked Selected — see "C1 Open Decisions" section below.
### Fact #29 — VINS-Fusion is a multi-sensor superset of VINS-Mono but its monocular+IMU mode failed to run on Jetson TX2 in a 2021 KAIST benchmark; Orin Nano Super feasibility unverified
- **Statement**: VINS-Fusion (Qin, Cao, Pan, Shen — extension of VINS-Mono) supports four documented sensor configurations: stereo+IMU, mono+IMU, stereo only, +GPS-fusion (toy example). KITTI Odometry top-ranked open-source stereo algorithm as of January 2019. **Repository state**: 4,476 stars; last update 2024-05-23; same master-branch-only convention. **Jetson record**: KAIST 2021 benchmark (Source #46) — on Jetson TX2, both **VINS-Fusion (CPU) and VINS-Fusion-imu fail to run** due to insufficient memory and CPU; VINS-Fusion-gpu (GPU-accelerated front-end) runs on TX2. Orin Nano Super has more memory than TX2 (8 GB LPDDR5 shared vs TX2's 8 GB LPDDR4 shared) and stronger CPU/GPU, but the project's onboard stack is *co-resident* with C2 VPR + C3 matcher + C5 estimator + C6 cache → memory-pressure on the VINS-Fusion-imu path is plausible. **License**: GPL-3.0, same dual-use distribution constraint as VINS-Mono.
- **Source**: Source #44 (canonical), Source #46 (KAIST Jetson benchmark)
- **Phase**: Phase 2
- **Target Audience**: System architects + C1 implementer
- **Confidence**: ✅ for the multi-sensor mode support and KITTI ranking; ✅ for the 2021 TX2 failure-to-run finding; ⚠️ for Orin Nano Super viability (between TX2 and Xavier NX in CPU/memory; not yet measured).
- **Related Dimension**: SQ3+SQ4 / C1 Open-source candidate
- **Fit Impact**: **carry as alternate candidate, with mandatory Jetson Orin Nano Super MVE before promotion.** VINS-Mono's narrower scope (mono+IMU only, no stereo overhead) makes VINS-Mono the preferred lead within the HKUST-Aerial-Robotics family; VINS-Fusion's multi-sensor coverage is a distractor for our pinned mode. **GPL-3.0 license decision is the same as VINS-Mono** — see "C1 Open Decisions".
### Fact #30 — OpenVINS is the most actively maintained MSCKF-class VIO and runs on Jetson Orin Nano Dev Kit + JetPack 6 + ROS 2 Humble with documented build adjustments; latency 270 ms on Xavier NX needs Orin-Nano-Super MVE
- **Statement**: OpenVINS (rpng, U. Delaware — Geneva, Eckenhoff, Lee, Yang, Huang — ICRA 2020) is a modular MSCKF (Multi-State Constraint Kalman Filter) implementation that fuses IMU state with sparse visual feature tracks via the Mourikis-Roumeliotis 2007 sliding-window MSCKF. **Mode support**: monocular, stereo, multi-camera (1–N) + IMU; mono+IMU is a documented first-class configuration. Supports SLAM features (in-state landmarks) plus pure MSCKF features. **Jetson Orin Nano evidence**: rpng/open_vins issue #421 (Genozen, Feb 2024, closed) confirms OpenVINS ROS 2 builds on Jetson Orin Nano Dev Kit + JetPack 6 + Ubuntu 22.04 + ROS 2 Humble after one build patch (`#include <opencv2/aruco.hpp>` with newer OpenCV); fdcl-gwu/openvins_jetson_realsense (Nov 2025) provides a complete setup guide for Jetson Orin Nano + Intel RealSense + librealsense compiled-from-source + `--parallel-workers 1` build to avoid memory issues. **Latency record**: rpng/open_vins issue #164 — ~270 ms latency on Jetson Xavier NX (4 cores, 40% CPU utilisation). Recommended optimisations: subscriber queue size 1, Release builds with ARM-specific optimization flags (e.g., `armv8.2-a`), reduced camera resolution, prefer `odometry` topic over `pose_imu`. **License**: GPL-3.0, same dual-use distribution constraint as VINS-Mono / VINS-Fusion. Stars 2,828; 30 contributors; 12 releases; latest tag v2.7 (June 2023) but master branch active through 2024–2025 issue threads.
- **Source**: Source #45 (canonical + LICENSE + docs.openvins.com), Source #46 (KAIST Jetson benchmark for class-level CPU/memory profile), agent-tools record `29ebf728...txt` (Jetson Orin Nano build evidence)
- **Phase**: Phase 2
- **Target Audience**: System architects + C1 implementer
- **Confidence**: ✅ for mode support, MSCKF formulation, and Jetson Orin Nano build feasibility; ⚠️ for steady-state latency on Orin Nano Super under our 5472×3648 nav frames — KAIST benchmark used 640×480; 16× pixel count is a yellow-flag.
- **Related Dimension**: SQ3+SQ4 / C1 Established-production candidate
- **Fit Impact**: **carry as lead candidate, conditional on user license decision.** OpenVINS has the most documented Jetson-Orin-Nano build path of the three GPL-3.0 candidates; MSCKF formulation is more memory-efficient than VINS-Mono's full sliding-window optimisation, which is a meaningful advantage under co-resident-process memory pressure. **GPL-3.0 license decision is the same as VINS-Mono / VINS-Fusion**.
### Fact #31 — OKVIS2 is the most actively maintained VI-SLAM in the BSD-permissive license bucket; OKVIS2-X (T-RO 2025) extends it with optional GNSS fusion that is architecturally aligned with the project's spoof-promotion path
- **Statement**: OKVIS2 (Leutenegger — arXiv 2022, ETH/Imperial/TUM Smart Robotics Lab) is a factor-graph VI-SLAM with bounded-size optimization. Algorithmic novelty: pose-graph edges from marginalised observations are "seamlessly turned back into observations" upon loop closure, reviving old landmarks and reprojection errors. Includes lightweight CNN segmentation for dynamic-region removal. **Mode support**: monocular and multi-camera + IMU; mono+IMU is a documented first-class configuration. **Successor OKVIS2-X (Boche, Jung, Laina, Leutenegger — IEEE T-RO 2025 vol 41 pp 6064–6083, DOI 10.1109/TRO.2025.3619051; arXiv 2510.04612, Oct 2025)** generalises the core to fuse multi-camera + IMU + optional GNSS receiver + LiDAR or depth. The OKVIS2-X GNSS-fusion mode (lineage: Visual-Inertial SLAM with Tightly-Coupled Dropout-Tolerant GPS Fusion, IROS 2022) directly mirrors the project's "VIO that may opportunistically fuse a non-spoofed GPS update when promotion completes" pattern (AC-NEW-2). **Repository state**: ethz-mrl/OKVIS2-X created 2025-09-23, last push 2026-03-17, 295 stars, 2 active contributors (bochsim, SebsBarbas). **License**: 3-clause BSD on the LICENSE file (GitHub UI shows "Other (NOASSERTION)" but the file is canonical 3-clause BSD per ASL-ETH Zurich convention) — permissive, no dual-use distribution friction.
- **Source**: Source #47 (OKVIS2 canonical), Source #48 (OKVIS2-X T-RO 2025)
- **Phase**: Phase 2
- **Target Audience**: System architects + C1 / C5 implementer
- **Confidence**: ✅ for algorithm, mode support, license, T-RO 2025 publication, repository activity; ⚠️ for Jetson Orin Nano runtime — no direct Jetson Orin Nano benchmark located; OKVIS2's factor-graph backend is plausibly heavier than OpenVINS' MSCKF on memory but lighter than Kimera (Kimera also produces a 3D mesh + semantic mesher, OKVIS2 does not).
- **Related Dimension**: SQ3+SQ4 / C1 Open-source-permissive lead candidate; potential C1+C5+C8 unified factor-graph design
- **Fit Impact**: **strong lead candidate by license + maintenance + GNSS-fusion alignment.** If license permissiveness is a priority, OKVIS2 + OKVIS2-X is the natural choice. The OKVIS2-X factor-graph also opens a design path where C5 (state estimator) collapses INTO C1 (the same factor graph absorbs sat-anchor measurements as constraints) — would simplify the pipeline at the cost of departing from the C1/C5 split, which is a Step-7.5 / `solution_draft01` design decision, not a SQ3+SQ4 question. **Pending Jetson Orin Nano Super MVE.**
### Fact #32 — Kimera-VIO is BSD-permissive but resource-heavy; KAIST benchmark found Kimera had the highest memory usage among VIOs tested and failed Xavier-NX-class memory under multi-process load
- **Statement**: Kimera-VIO (MIT-SPARK — Rosinol, Abate, Chang, Carlone — ICRA 2020) is a VI-SLAM pipeline with frontend + backend (factor-graph optimization in iSAM2 or GTSAM) + 3D mesher + pose-graph optimizer. Mode support: stereo+IMU primary, mono+IMU optional but documented. **License**: BSD 2-Clause "Simplified" (LICENSE.BSD on the repo) — permissive. **Maintenance**: active issue/PR threads through Dec 2024 / Feb 2025 covering ROS 2 integration, mono-inertial discussion, dependency management. **Resource profile** (Source #46 KAIST 2021 benchmark): Kimera had the highest memory usage among the 9 algorithms tested (numerous computations per keyframe); Kimera failed to fit on Xavier NX-class memory under sustained multi-process load. The 3D mesh + semantic-label outputs are unused by the project's narrow C1 mandate (relative 6-DoF + covariance only) — Kimera's overhead is unjustified vs OKVIS2 / OpenVINS for our use case.
- **Source**: Source #49 (Kimera canonical + LICENSE.BSD), Source #46 (KAIST Jetson benchmark)
- **Phase**: Phase 2
- **Target Audience**: System architects (build-vs-buy, mesh-feature decision)
- **Confidence**: ✅ for algorithm, license, maintenance status; ✅ for the Source #46 finding (KAIST 2021); ⚠️ for whether Orin Nano Super's larger memory + Ampere GPU lifts Kimera into feasibility — the Source-46 failure was on Xavier NX 8 GB shared, same memory budget as Orin Nano Super, but Orin Nano Super has higher per-core throughput.
- **Related Dimension**: SQ3+SQ4 / C1 Open-source-permissive secondary candidate
- **Fit Impact**: **carry as fallback only, not lead.** Kimera's permissive license is attractive but its resource overhead (especially the unused 3D mesh + semantic mesher) is a poor fit under co-resident process pressure. Use as a conservative secondary fallback if OKVIS2 unexpectedly fails Jetson MVE. **Status**: not lead.
### Fact #33 — DROID-SLAM is disqualified by AC-4.2: ≥11 GB GPU VRAM inference budget exceeds the project's 8 GB shared LPDDR5; further, DROID-SLAM is monocular VO/SLAM without IMU fusion and would require an external metric-scale wrapper
- **Statement**: DROID-SLAM (princeton-vl, Teed & Deng — NeurIPS 2021; arXiv 2108.10869) requires ≥11 GB GPU memory to run inference per the official README; training requires ≥24 GB on 4× RTX 3090. Issue #121 confirms that even with 128 GB system RAM and 16 GB VRAM (RTX 4080), users hit very large RAM consumption quickly. Algorithmically, DROID-SLAM is **monocular VO/SLAM** with recurrent dense bundle adjustment over a complete history of camera poses — no native IMU fusion; output pose is in arbitrary scale (no metric scale recovery without external alignment). DPV-SLAM (ECCV 2024, princeton-vl) is the lighter successor at ~4–5 GB GPU memory; DPVO (NeurIPS 2023, princeton-vl) is even lighter at ~3 GB, but neither natively integrates IMU.
- **Source**: Source #50 (DROID-SLAM canonical), Source #51 (DPVO / DPV-SLAM successor), Source #52 (DPVO-QAT++ memory measurement)
- **Phase**: Phase 2
- **Target Audience**: System architects + C1 implementer
- **Confidence**: ✅
- **Related Dimension**: SQ3+SQ4 / C1 disqualified candidate
- **Fit Impact**: **DISQUALIFIED outright.** AC-4.2 sets the 8 GB shared CPU+GPU memory budget; DROID-SLAM's ≥11 GB GPU-only requirement violates it before adding co-resident C2/C3/C5/C6 processes. Cite as "what the project cannot afford" in `solution_draft01` to pre-empt obvious questions.
### Fact #34 — DPVO is monocular VO only (no IMU fusion); it can fit a Jetson-suitable memory footprint with QAT but cannot satisfy the C1 VIO mandate alone — would need an external IMU + metric-scale wrapper
- **Statement**: DPVO (Teed, Lipson, Deng — NeurIPS 2023; ECCV 2024 DPV-SLAM successor) is a deep-learning monocular VO with sparse patch tracking + differentiable bundle adjustment. **Mode**: monocular VO only — no IMU fusion in the published paper or repository; output pose is in arbitrary scale. Memory footprint: DPVO ~3 GB GPU, DPV-SLAM ~4–5 GB GPU on standard hardware; DPVO-QAT++ (arXiv 2511.12653, Cheng Liao, Nov 2025) reduces peak reserved memory to 1.02 GB on RTX 4060 (8 GB) via fused-CUDA INT8 fake-quantization while preserving ATE on TartanAir/EuRoC. **License**: MIT (permissive). Repository: 989 stars; last update 2024-10-12. **Crucial gap**: DPVO does NOT meet the C1 mandate of a "VIO that produces metric-scale 6-DoF + attitude with σ ≤ 5°" — for the project to use DPVO as the *VO half* of C1, an additional IMU+scale-fusion module (loosely-coupled ESKF with VO velocity / displacement priors) must be designed; alternatively, DPVO's pose can feed C5 directly as a relative-displacement constraint, with attitude served separately by FC IMU integration. **Jetson Orin Nano runtime evidence**: indirect — DPVO-QAT++ benchmarks on RTX 4060 desktop, NOT Jetson Orin Nano. The Ampere GPU architecture is shared between RTX 4060 and Orin Nano Super (both Ampere); the Orin Nano Super's GPU is smaller, so direct extrapolation is not safe — Jetson MVE required.
- **Source**: Source #51 (DPVO / DPV-SLAM canonical), Source #52 (DPVO-QAT++ Nov 2025)
- **Phase**: Phase 2
- **Target Audience**: System architects + C1 / C5 implementer
- **Confidence**: ✅ for "VO only, no IMU fusion" and the memory footprints; ⚠️ for Jetson Orin Nano direct runtime (no measurement); ⚠️ for the operational complexity of the QAT pipeline (teacher-student distillation training is a significant prerequisite vs the classical VINS-* / OpenVINS / OKVIS2 candidates).
- **Related Dimension**: SQ3+SQ4 / C1 conditional candidate (VO not VIO; needs external IMU wrapper)
- **Fit Impact**: **NOT a drop-in C1 candidate; conditional fit only.** DPVO is **not** a substitute for VINS-Mono / OpenVINS / OKVIS2 — it is a candidate for the *VO half* of a hybrid design where C5 (estimator) absorbs IMU and DPVO provides relative-pose priors. This adds design complexity and is **not preferred** unless one of the established VIO candidates fails Jetson MVE for memory reasons. **Status**: secondary, conditional.
### Fact #35 — Pure VO baseline (KLT optical flow + 5-point essential matrix or homography RANSAC) is the project's mandatory simple-baseline candidate and is the de-facto fallback when learning-based methods fail on Jetson-budget constraints
- **Statement**: The classical pipeline — Shi-Tomasi or FAST corner detection → KLT pyramidal optical flow tracking (`cv::calcOpticalFlowPyrLK`) → 5-point essential matrix (Nister, `cv::findEssentialMat`) or homography RANSAC (`cv::findHomography`) → relative pose with arbitrary scale → metric-scale alignment via IMU integration externally — is the foundational visual-odometry pipeline implemented in OpenCV samples and pedagogical repositories. For the project's nadir-down UAV at 1 km AGL over Ukrainian steppe (predominantly planar terrain, low relief), the **homography path is geometrically appropriate** (a plane induces a homography between two views); for non-planar relief, the **essential-matrix path is appropriate** at a small overhead. License: public domain / OpenCV-Apache-2.0 / MIT (whatever reference implementation is chosen) — permissive. Reference: representative public Monocular-Video-Odometery (MIT, alishobeiri 2018), Monocular-Visual-Odometry (Yacynte) at translation error 0.94% / rotation error 0.015°/m on KITTI dataset.
- **Source**: Source #53 (OpenCV docs + reference implementations)
- **Phase**: Phase 2
- **Target Audience**: System architects + C1 implementer + risk reviewer
- **Confidence**: ✅
- **Related Dimension**: SQ3+SQ4 / C1 Simple-baseline candidate (mandatory per Component Option Breadth rule)
- **Fit Impact**: **carry as the project's `Simple baseline / known-runnable / known-failure-mode` C1 fallback.** Not a lead, but mandatory presence. Failure modes: (a) low-texture cropland / snow → KLT track loss; (b) sharp turns → low-overlap homography degeneracy; (c) no native IMU fusion → must wrap with external metric-scale alignment (same wrapper as DPVO). **Status**: simple-baseline reference; cited in `solution_draft01` to anchor the failure analysis.
### Fact #36 — Step-0.5-time-window assessment: VINS-Mono / VINS-Fusion master branches are at the Critical-novelty 18-month boundary; OpenVINS and OKVIS2 are within window; DPVO is borderline; the established baselines (KLT + RANSAC) are exempt
- **Statement**: Per Step 0.5 timeliness assessment in `00_question_decomposition.md`, Critical-novelty topics require sources within 6 months for SOTA claims and 18 months for established libraries' API behaviour. Audit at access time 2026-05-07: VINS-Mono master last meaningful commit 2024-02-25 → ~27 months → **just over the 18-month window**; VINS-Fusion 2024-05-23 → ~24 months → just over; OpenVINS master active (issue threads through Feb 2025) and v2.7 release June 2023 → ~35 months for the tagged release but master in stable maintenance → within de-facto window for an established library; OKVIS2-X push 2026-03-17 → ~2 months → **fully within window**; DPVO last code update 2024-10-12 → ~19 months → just over but DPV-SLAM ECCV 2024 keeps the algorithm class within 6-month claim window; KLT / 5-point / RANSAC / homography → established baselines per Step 0.5 → **no time window applies**. **Implication**: VINS-Mono / VINS-Fusion fall into the "older than 18 months but classical authoritative reference" bucket — Step 0.5 allows up to 18 months strictly, but downstream forks (vins-mono-android, embedded variants) and the IEEE T-RO 2018 publication keep the algorithm class in active community use. Recommended treatment: **keep as candidates but require live MVE on Jetson Orin Nano Super before promotion to Selected**, to revalidate against the current OpenCV / Ceres / ROS 2 stack.
- **Source**: Source #43, Source #44, Source #45, Source #47, Source #48, Source #51 (timeliness audit per source)
- **Phase**: Phase 2
- **Target Audience**: Step-7.5 reviewer + System architects
- **Confidence**: ✅
- **Related Dimension**: SQ3+SQ4 / C1 candidate-pool integrity
- **Fit Impact**: **applies a conservative timeliness gate: every C1 candidate from VINS-Mono / VINS-Fusion / DPVO requires an Orin-Nano-Super MVE before being marked Selected**, since their master-branch staleness pushes them out of the Critical-novelty 18-month window. OpenVINS / OKVIS2 / OKVIS2-X / Kimera are within window via active issue threads or recent releases.
### C1 Component Applicability Gate — preliminary table (this session; structured Restrictions×AC sub-matrix per candidate is next session's work)
| Candidate | Mode (project) | License | Active maintenance? | Jetson Orin Nano Super runnable? | Native IMU fusion? | Native metric scale? | License blocks dual-use? | Preliminary status |
|---|---|---|---|---|---|---|---|---|
| **VINS-Mono** | mono+IMU | GPL-3.0 (copyleft) | ⚠️ borderline (24 mo) | ✅ proven on Jetson Nano (2021) → Orin Nano Super virtually certain | ✅ | ✅ | **⚠️ Verify with user** | Lead candidate **conditional on user license decision** + Orin-Nano-Super MVE |
| **VINS-Fusion** | mono+IMU (mode) | GPL-3.0 | ⚠️ borderline (24 mo) | ⚠️ failed on TX2 (KAIST 2021); Orin Nano Super untested | ✅ | ✅ | **⚠️ Verify with user** | Alternate, secondary to VINS-Mono within HKUST family |
| **OpenVINS** | mono+IMU | GPL-3.0 | ✅ active master | ✅ build confirmed on Orin Nano Dev Kit + JetPack 6 (2024 + 2025 community evidence); ~270 ms latency on Xavier NX | ✅ MSCKF | ✅ | **⚠️ Verify with user** | **Lead candidate** **conditional on user license decision** (best Jetson-Orin-Nano evidence + most maintained of the GPL-3 trio) |
| **OKVIS2 / OKVIS2-X** | mono+IMU (+ optional GNSS) | BSD-3 | ✅ very active (2026 pushes) | ⚠️ no direct Jetson Orin Nano measurement; factor-graph backbone plausibly heavier than MSCKF | ✅ | ✅ | ✅ no | **Lead candidate by license + maintenance + spoof-promotion architectural alignment**, pending Jetson MVE |
| **Kimera-VIO** | mono+IMU (optional) | BSD-2 | ✅ active | ⚠️ failed on Xavier NX 8 GB shared under multi-process (KAIST 2021) | ✅ | ✅ | ✅ no | Fallback secondary; resource overhead poor fit for project |
| **DROID-SLAM** | mono VO/SLAM only | (project repo) | reference baseline | ❌ ≥11 GB GPU VRAM > 8 GB AC-4.2 budget | ❌ | ❌ (arbitrary scale) | n/a | **DISQUALIFIED** by AC-4.2 |
| **DPVO / DPV-SLAM** | mono VO only | MIT | ⚠️ borderline (19 mo on code, ECCV 2024 paper) | ⚠️ DPVO-QAT++ (Nov 2025) shows 1.02 GB peak on RTX 4060 desktop; Jetson Orin Nano untested | ❌ (needs external IMU wrapper) | ❌ (needs external scale alignment) | ✅ no | Conditional secondary — VO half of a hybrid C1+C5 design only; not a drop-in VIO replacement |
| **Pure VO baseline (KLT + 5pt RANSAC / homography)** | mono VO only | OpenCV-Apache-2.0 / MIT | ✅ foundational (no time window) | ✅ runs on any Jetson | ❌ (needs external IMU wrapper) | ❌ (needs external scale alignment) | ✅ no | **Mandatory simple-baseline reference** per Component Option Breadth rule |
**Surviving lead candidates (preliminary)**, in priority order based on this session's evidence:
1. **OpenVINS** (GPL-3.0, MSCKF, best Jetson Orin Nano evidence) — pending user license decision + Orin-Nano-Super MVE
2. **OKVIS2 / OKVIS2-X** (BSD-3, factor-graph + GNSS-fusion alignment, most active maintenance) — pending Jetson MVE
3. **VINS-Mono** (GPL-3.0, sliding-window optimization, proven on Jetson Nano) — pending user license decision + Orin-Nano-Super MVE
4. **Pure VO baseline** (mandatory simple-baseline; runtime guaranteed; carries the project as a graceful fallback)
**Disqualified outright**: DROID-SLAM (AC-4.2 memory budget), RTAB-Map and ORB-SLAM3 (already pruned by Fact #16).
**Conditional / not-direct-fit**: DPVO / DPV-SLAM (VO not VIO, needs external IMU wrapper), Kimera-VIO (resource overhead unjustified for narrow C1 mandate).
### C1 Open Decisions (to be resolved before SQ3+SQ4 closure)
**Decision D-C1-1 — GPL-3.0 license posture for the onboard binary** (BLOCKING for the GPL-3.0 trio: VINS-Mono / VINS-Fusion / OpenVINS).
- The three most established VIO candidates (VINS-Mono / VINS-Fusion / OpenVINS) are GPL-3.0 (viral copyleft).
- For dual-use UAV deployment, GPL-3 binary distribution to a customer triggers obligations: source-code disclosure for the entire linked binary, anti-tivoization clauses for embedded firmware updates, viral effect on any proprietary code linked into the same binary.
- BSD/MIT alternatives exist (OKVIS2 BSD-3, Kimera BSD-2, DPVO MIT, pure-VO baseline OpenCV-Apache-2.0), but each comes with secondary trade-offs (Jetson MVE risk, missing IMU fusion, resource overhead).
- Three options for the user:
- **(a)** Accept GPL-3.0 — distribution model = release source on customer request; or operate the system as a service rather than transferring binaries. Lowest-risk algorithmic path (most-tested candidates).
- **(b)** Restrict to permissive licenses only (BSD/MIT) — lead candidate becomes OKVIS2; carries Jetson MVE risk.
- **(c)** Keep both options open through the design phase — make the final license decision after the Jetson Orin Nano MVE results are in.
- **Recommended default**: **(c)** — defer the binary commitment until empirical evidence on Jetson Orin Nano. This is recorded as a flagged decision; SQ3+SQ4 candidate matrix will carry both license families to Step 7.5.
**Decision D-C1-2 — Acceptance of Jetson Orin Nano MVE as a Step-7.5 prerequisite** (procedural).
- Per the Per-Mode API Capability Verification rule, every lead candidate library/SDK requires `context7` (or equivalent docs) lookup + a Minimum Viable Example for the project's pinned mode + per-numbered-Restriction × per-numbered-AC sub-matrix.
- The Component Applicability Gate above is **preliminary** — it documents enumeration evidence but does NOT yet contain `context7` per-mode capability verification or the structured sub-matrix.
- **Next session's mandatory work**: `context7` lookup (3 mandatory queries) for OpenVINS / OKVIS2 / VINS-Mono; per-Restriction × per-AC sub-matrix per candidate; the same for the simple-baseline path; record into `../02_fact_cards/C1_vio.md` per the engine template + `../06_component_fit_matrix/C1_vio.md` per Step 7.5.
### C1 Boundary check: candidate enumeration is saturated for this session
Saturation signals observed: (a) all 7 named candidates from `00_question_decomposition.md` C1 row enumerated with at least one canonical L1 source per candidate; (b) Jetson Orin Nano runtime evidence located for OpenVINS (direct) and VINS-Mono (Jetson Nano + RPi CM4); other candidates carry "MVE required" gates explicitly; (c) license diversity covered (GPL-3.0 trio + BSD-permissive duo + MIT + permissive-baseline); (d) explicit disqualifications recorded with cited evidence (DROID-SLAM, RTAB-Map, ORB-SLAM3). **Open**: per-mode `context7` verification (BLOCKING per rule) + Restrictions×AC sub-matrices (BLOCKING per Step 7.5) — explicitly deferred to next session.
---
## C1 — Per-Mode API Capability Verification (engine Step 2 — Mandatory `context7` lookup) [2026-05-08 session]
This section closes the per-mode API capability verification gate for the four C1 lead candidates. Each candidate has a pinned-mode statement, three documentary `context7` (or equivalent) queries answered, an MVE block, and a per-numbered-Restriction × per-numbered-AC sub-matrix. The candidates' final lead-promotion to "Selected" status remains gated by the dedicated Jetson Orin Nano Super hardware MVE (D-C1-2 deferred phase).
### Fact #37 — OpenVINS per-mode API capability verification (mono+IMU on Jetson Orin Nano Super) — DOCUMENTARY PASS; Jetson MVE pending
- **Statement**: OpenVINS (`/rpng/open_vins`, master) exposes monocular / stereo / multi-camera + IMU as first-class launch configurations via `subscribe.launch.py` declared launch arguments `use_stereo` (bool) and `max_cameras` (int). The project's **pinned mode** is monocular + IMU, selected via `use_stereo:=false max_cameras:=1` with `config:=` pointing to a project-tuned `estimator_config.yaml`. **Mode-enumeration query (1/3)**: confirms 3 sensor configurations at the launch layer; supported IMU intrinsic models = KALIBR + RPNG (per `propagation-analytical.dox`). **Pinned-mode runnable example query (2/3)**: confirms `ros2 launch ov_msckf subscribe.launch.py config:=euroc_mav` is the documented runnable example; `euroc_mav` defaults to stereo per `subscribe.launch.py` but `use_stereo:=false max_cameras:=1` selects mono-only at runtime — no source patch required. **Disqualifier-probe query (3/3)**: did NOT surface any documented sub-20-Hz validation, hard frame-rate floor, or hard image-resolution ceiling in the master docs; the documented Xavier-NX latency baseline (~270 ms per rpng/open_vins issue #164) is below the AC-4.1 400 ms p95 budget head-room **at 640×480** but unverified at the project's 5472×3648 nav frames. The Jetson Orin Nano Dev Kit + JetPack 6 + ROS 2 Humble build patch is documented (rpng/open_vins issue #421 + fdcl-gwu/openvins_jetson_realsense). **Pinned-mode sentence**: "We will use **OpenVINS** in **monocular + IMU mode** with inputs `{1× ADTi 20MP nav frame stream + FC IMU via MAVLink/SCALED_IMU2}` and expect outputs `{6-DoF pose at IMU rate with covariance from MSCKF state, source label visual_propagated when no satellite anchor}` on `Jetson Orin Nano Super (8 GB shared, JetPack 6, ROS 2 Humble)`."
- **Source**: Source #54 (context7), Source #45 (canonical OpenVINS), Source #46 (KAIST Jetson benchmark for class-level comparison)
- **Phase**: Phase 2
- **Target Audience**: System architects + C1 implementer + Step-7.5 reviewer
- **Confidence**: ✅ for mode-enumeration and runnable-example documentary evidence; ⚠️ for sub-20-Hz validation and 5472×3648 latency (no documentary evidence — Jetson MVE will resolve)
- **Related Dimension**: SQ3+SQ4 / C1 lead candidate — per-mode API capability verification gate
- **Fit Impact**: **DOCUMENTARY PASS for the per-mode API capability verification gate**; promotes OpenVINS to "lead candidate, documentary verification complete" status in `../06_component_fit_matrix/C1_vio.md` row. License-track decision (D-C1-1) still gates final Selected promotion (OpenVINS = GPL-3.0, lives in track A); Jetson Orin Nano Super hardware MVE (D-C1-2) still gates accuracy/latency/memory empirical promotion.
### Fact #38 — VINS-Mono per-mode API capability verification (mono+IMU on Jetson Orin Nano Super) — DOCUMENTARY PASS WITH FRAME-RATE CAVEAT; Jetson MVE pending
- **Statement**: VINS-Mono (`HKUST-Aerial-Robotics/VINS-Mono`, master) is a single-mode system: "real-time SLAM framework for **Monocular Visual-Inertial Systems**" (README §1) — no mode enumeration is required because the pinned mode IS the only mode. **Mode-enumeration query (1/3)**: VINS-Mono is single-mode = mono+IMU; cross-source documentary evidence from VINS-Fusion `context7` confirms the same authors continue to ship `euroc_mono_imu_config.yaml` as a first-class config in the active fork (per the Per-Mode API rule, VINS-Fusion's mono+IMU mode is a separately-cataloged candidate, but the algorithmic core and required calibration surface are identical — see Fact #29). **Pinned-mode runnable example query (2/3)**: README §3.1.1 — `roslaunch vins_estimator euroc.launch` + EuRoC MH_01 bag is the canonical runnable example; supports online camera-IMU extrinsic calibration (`estimate_extrinsic:=2`), online temporal calibration (`estimate_td:=1`), and rolling-shutter cameras with documented calibration ceiling (`reprojection error <0.5 px`). Pinhole + MEI camera models supported. Camera intrinsics + IMU noise must be calibrated (Kalibr or equivalent). **Disqualifier-probe query (3/3)**: README §5.1 explicitly states *"The image should exceed 20Hz and IMU should exceed 100Hz."* — this is a documentary minimum-rate recommendation and is **below the project's 3 fps nav-camera target by ~6.7×**. See Fact #40 for the geometric analysis and the cross-cutting frame-rate-sensitivity finding. Ceres Solver dependency is pinned to v1.14.0 (build issues at ≥2.0.0 per README §1.2); JetPack-shipped Ceres versions need explicit verification. License: GPLv3 (README §8). **Pinned-mode sentence**: "We will use **VINS-Mono** in **monocular + IMU mode** with inputs `{1× ADTi 20MP nav frame stream (target 3 fps; under documentary 20 Hz floor) + FC IMU via MAVLink/SCALED_IMU2}` and expect outputs `{6-DoF pose at IMU rate via sliding-window optimization with covariance from optimization Hessian, loop closure via DBoW2}` on `Jetson Orin Nano Super (8 GB shared, JetPack 6, Ceres v1.14.0 build)`."
- **Source**: Source #55 (VINS-Mono README + VINS-Fusion context7 cross-source), Source #43 (canonical VINS-Mono), Source #46 (KAIST Jetson benchmark for class-level comparison)
- **Phase**: Phase 2
- **Target Audience**: System architects + C1 implementer + Step-7.5 reviewer
- **Confidence**: ✅ for mode-enumeration (single mode by construction) and runnable-example evidence; ⚠️ for sub-20-Hz operation (documentary minimum-rate recommendation contradicts project frame-rate target); ⚠️ for Ceres v1.14.0 vs JetPack 6 stock Ceres compatibility
- **Related Dimension**: SQ3+SQ4 / C1 lead candidate — per-mode API capability verification gate
- **Fit Impact**: **DOCUMENTARY PASS WITH FRAME-RATE CAVEAT**. Per the engine rule's escalation tier, the candidate is downgraded from "documentary lead" to **"Experimental only — sub-20-Hz operation requires Jetson MVE validation"** until the deferred Jetson hardware MVE explicitly measures VINS-Mono at the project's 3 fps. License-track decision (D-C1-1) still gates final Selected promotion (VINS-Mono = GPL-3.0, lives in track A).
### Fact #39 — OKVIS2 per-mode API capability verification (mono+IMU on Jetson Orin Nano Super) — DOCUMENTARY PASS; Jetson MVE pending
- **Statement**: OKVIS2 (`smartroboticslab/okvis2`, main) is a keyframe-based factor-graph VI-SLAM with multi-camera + IMU support; the README documents coordinate-frame contract (`W` world / `C_i` cameras / `S` IMU / `B` body), state representation (`T_WS` pose + velocity + gyro/accel biases), and a two-callback API (`setOptimisedGraphCallback` for batch updates incl. loop closure + `setImuCallback` for high-rate prediction). **Mode-enumeration query (1/3)**: README + example apps confirm modes = mono / stereo / multi-camera (i-th camera frame `C_i`) — IMU is mandatory (`okvis::ViSensorBase::setImuCallback` is required). The example apps are `okvis_app_synchronous` (dataset replay), `okvis_app_realsense` (live D435i/D455), `okvis_app_realsense_record` (recording). ROS 2 build is opt-in (`BUILD_ROS2=ON`); ROS 2 launch files: `okvis_node_realsense.launch.xml`, `okvis_node_realsense_publisher.launch.xml`, `okvis_node_subscriber.launch.xml`, `okvis_node_synchronous.launch.xml`. **Pinned-mode runnable example query (2/3)**: README "Running the demo application" + "Configuration files" section — `./okvis_app_synchronous <config>.yaml <EuRoC_MH_01_easy_dir>` is the canonical mono dataset-replay example; the EuRoC config in `config/` is the documentary mono+IMU launch reference. Configuration trade-off surface: "various options to trade-off accuracy and computational expense as well as to enable online calibration" — explicit acknowledgement of latency/accuracy tuning surface. **Disqualifier-probe query (3/3)**: README does NOT state an explicit minimum image rate (cf. VINS-Mono's 20 Hz). OKVIS2's keyframe-based architecture inherently selects only "informative" frames for optimization, which is a structural advantage at lower input frame rates compared to sliding-window optimization. Optional LibTorch sky-segmentation CNN (`USE_NN`) can be disabled with `USE_NN=OFF` to remove the Jetson LibTorch dependency. License: 3-clause BSD (README "License" section). Health warning: "good results (or results at all) may only be obtained with appropriate calibration" — Kalibr-based intrinsic + extrinsic + IMU noise + tight time sync mandatory (this is shared with all VI candidates). OKVIS2-X (T-RO 2025) extends with optional GNSS fusion — architecturally aligned with the project's spoof-promotion path (per Fact #31). **Pinned-mode sentence**: "We will use **OKVIS2** (with `BUILD_ROS2=ON USE_NN=OFF`) in **monocular + IMU mode** with inputs `{1× ADTi 20MP nav frame stream + FC IMU via MAVLink/SCALED_IMU2 → re-published to /okvis/cam0/image_raw + /okvis/imu0}` and expect outputs `{6-DoF pose with covariance from factor-graph optimization via setOptimisedGraphCallback + high-rate IMU-predicted state via setImuCallback}` on `Jetson Orin Nano Super (8 GB shared, JetPack 6, ROS 2 Humble)`."
- **Source**: Source #56 (OKVIS2 README), Source #47 (canonical OKVIS2 paper arXiv:2202.09199), Source #48 (OKVIS2-X T-RO 2025)
- **Phase**: Phase 2
- **Target Audience**: System architects + C1 implementer + Step-7.5 reviewer
- **Confidence**: ✅ for mode-enumeration, runnable-example, and lower-frame-rate-tolerance arguments; ⚠️ for direct 3 fps validation (no documentary measurement — Jetson MVE will resolve); ⚠️ for direct Jetson Orin Nano measurement (Fact #31 noted no direct measurement; community evidence less abundant than OpenVINS)
- **Related Dimension**: SQ3+SQ4 / C1 lead candidate — per-mode API capability verification gate
- **Fit Impact**: **DOCUMENTARY PASS for the per-mode API capability verification gate**; promotes OKVIS2 to "lead candidate, documentary verification complete" status in `../06_component_fit_matrix/C1_vio.md` row. OKVIS2's keyframe-based architecture is the **only candidate** of the four leads with a structural argument for tolerating sub-20-Hz operation — this re-orders the per-license-track lead ranking (see Fact #41 locked-in defaults). License-track decision (D-C1-1) does NOT gate OKVIS2 (BSD-3 already permissive); Jetson Orin Nano Super hardware MVE (D-C1-2) still gates empirical accuracy/latency/memory promotion.
### Fact #40 — Cross-cutting C1 finding: project's 3 fps nav-camera target is below VINS-Mono's documented 20 Hz minimum-rate recommendation; affects all sliding-window VIO candidates; OKVIS2's keyframe architecture is the structural mitigant
- **Statement**: VINS-Mono README §5.1 documents "The image should exceed 20Hz and IMU should exceed 100Hz" as the recommended minimum-rate operating envelope (Source #55). The project's nav-camera processing target is 3 fps per `00_question_decomposition.md` Project Constraint Matrix. **Geometric analysis**: at 60 km/h cruise = 16.7 m/s × (1/3 s) = 5.5 m of forward motion between consecutive nav frames; at 1 km AGL with 12 cm/px GSD, that motion projects to ~46 px of in-image displacement (~0.84% of the 5472 px frame width) — **well within KLT-trackable range** for the nadir-down camera geometry, so the rate floor is NOT geometrically unreachable. **However**: the documented recommendation is about temporal-stability assumptions (motion-blur tolerance, IMU pre-integration noise growth, sliding-window optimisation Jacobian conditioning), not about geometric trackability. **Cross-candidate impact**: (a) **VINS-Mono** — sliding-window optimisation, full graph re-linearisation per keyframe, 20 Hz documentary recommendation explicitly violated by 6.7× → ⚠️ Experimental only until Jetson MVE measures actual sub-20-Hz behaviour; (b) **VINS-Fusion** — same algorithmic core as VINS-Mono mono+IMU mode, same caveat applies; (c) **OpenVINS** — MSCKF-based with sliding-window state + sparse feature constraints, has documented variable-rate tolerance via `init_imu_thresh`/`init_window_time` config, but no documentary sub-20-Hz validation surfaced in `context7` queries → ⚠️ Verify via Jetson MVE; (d) **OKVIS2** — keyframe-based, structurally selects only informative frames for optimization; the architecture is more naturally tolerant of variable / lower input rates → preferred candidate at low input frame rates; ✅ structural argument; (e) **Pure VO baseline** (KLT+RANSAC) — requires sufficient feature overlap between consecutive frames; at 0.84% in-image displacement this is well within KLT capture range; ✅ no rate-floor concern. **Architectural alternative for design-phase consideration**: instead of binding all C1 candidates to 3 fps, the nav-camera input pipeline could fork — full-resolution 5472×3648 at 3 fps for VPR/satellite-anchor (C2/C3) and a binned/cropped 1368×912 (or 640×480) at higher rate (≥10 fps) into the VIO front-end. ADTi 20MP 20L V1 (APS-C) bandwidth at full-res caps near 5–7 fps over USB 3 (≈2–3 GB/s raw); binned modes typically 3–10× the rate. This is a Plan-time decision, not a research-time one, but the option must be carried into Plan and the Jetson MVE must measure both single-rate and dual-rate paths.
- **Source**: Source #55 (VINS-Mono README §5.1), Source #43 (canonical), restrictions.md "Cameras" section + `00_question_decomposition.md` Project Constraint Matrix (3 fps target)
- **Phase**: Phase 2
- **Target Audience**: System architects + C1 implementer + Plan-phase reviewer + Jetson MVE owner
- **Confidence**: ✅ for the documentary 20 Hz minimum-rate recommendation; ✅ for geometric trackability calculation; ⚠️ for the binned/dual-rate pipeline option (camera-bandwidth estimate is plausible but needs ADTi datasheet verification at Plan time)
- **Related Dimension**: SQ3+SQ4 / C1 frame-rate sensitivity (cross-candidate); SQ4 (per-candidate runtime envelope binding)
- **Fit Impact**: **(a)** Re-orders the per-license-track candidate ranking — within the BSD/permissive track, OKVIS2 strengthens its lead via structural keyframe argument; within the GPL-3.0 track, OpenVINS retains lead over VINS-Mono on this specific dimension because MSCKF's variable-rate tolerance is more documented than VINS-Mono's full-window optimisation. **(b)** Adds a Plan-phase decision: **single-rate (3 fps to all consumers) vs dual-rate (binned high-rate to VIO + full-res 3 fps to VPR/satellite)** — this becomes an explicit deliverable for the Plan phase, not the Jetson MVE phase, because the nav-camera input pipeline shape feeds into both C1 and C2/C3 candidate scoring. **(c)** Marks all VINS-* candidates as ⚠️ Experimental-only until the deferred Jetson hardware MVE explicitly measures sub-20-Hz behaviour.
### Fact #41 — D-C1-1 + D-C1-2 locked-in research-time defaults (after user-skipped clarification, 2026-05-08)
- **Statement**: The user invoked `/autodev` and was presented with structured AskQuestion prompts for D-C1-1 (GPL-3.0 license posture) and D-C1-2 (Jetson MVE schedule); the user **skipped the questions with the directive "continue with the information you already have"**. Per autodev meta-rule "Critical Thinking" — locked-in research-time defaults selected to preserve maximum future optionality and to honour the documentary evidence already gathered: **D-C1-1 = (c) "Keep both license tracks open"** — rank GPL-3.0 leads (OpenVINS, VINS-Mono, VINS-Fusion) in parallel with BSD-permissive OKVIS2/OKVIS2-X; **carry both license tracks through Plan**; final license decision deferred to post-Jetson-MVE/Plan time when empirical evidence is available. **D-C1-2 = (b) "Defer Jetson MVE to a dedicated bring-up phase between research and Plan"** — research closes with documentary ranking + explicit "Jetson MVE pending" gates per candidate; the dedicated Jetson Orin Nano Super hardware MVE phase produces a single MVE artifact that promotes leads to "Selected" before Plan starts. The Plan phase MUST NOT lock a final C1 candidate before the deferred Jetson MVE artifact is produced and reviewed. **These defaults are explicitly tagged as user-deferred** — the user retains the right to revisit either decision at Plan time without losing the research artifact (both license tracks fully cataloged; both lead candidates carry full per-mode evidence).
- **Source**: User clarification skip during 2026-05-08 `/autodev` invocation; autodev meta-rule "Critical Thinking"; greenfield-flow Step 14 (Plan) precondition rule
- **Phase**: Phase 2 — process decision
- **Target Audience**: System architects + Plan-phase reviewer + Step-7.5 reviewer
- **Confidence**: ✅ (defaults selected and tagged as user-deferred; user can override at any later prompt)
- **Related Dimension**: SQ3+SQ4 / C1 process gate; cross-cutting onto C2–C10 (license posture decision is project-wide, not C1-specific)
- **Fit Impact**: **PROCESS GATE CLOSURE for C1**. Allows research to proceed past C1 to C2 (VPR) candidate enumeration without requiring user input now. The Plan phase MUST surface D-C1-1 again as a structured A/B/C decision before any C1 candidate is locked, AND MUST require the deferred Jetson MVE artifact as a precondition.
---
## C1 — Minimum Viable Example (MVE) Blocks
### MVE — OpenVINS in monocular + IMU mode
- **Source**: Source #54 (context7 → `https://github.com/rpng/open_vins/blob/master/docs/gs-tutorial.dox` ROS 2 launch + `https://github.com/rpng/open_vins/blob/master/docs/gs-datasets.dox` EuRoC config), accessed 2026-05-08
- **Inputs in the example**: EuRoC MAV stereo VI dataset (default `config:=euroc_mav` is stereo 2× cameras + IMU); the launch file declares `use_stereo` (default `true`) and `max_cameras` (default `2`) as runtime overrides; setting `use_stereo:=false max_cameras:=1` selects monocular operation against the same `estimator_config.yaml` parameter file with ROS topics `/cam0/image_raw` + `/imu0`
- **Outputs in the example**: 6-DoF pose at IMU rate; ROS 1 publishes `/ov_msckf/poseimu`, `/ov_msckf/odomimu`, `/ov_msckf/pathimu`; ROS 2 publishes equivalent topics under the configured namespace
- **Project inputs**: 1× ADTi 20MP nav frame stream (5472×3648, target 3 fps) + FC IMU via MAVLink (SCALED_IMU2 at ≥100 Hz)
- **Project outputs required**: 6-DoF pose at IMU rate with metric scale + 6×6 covariance + source label `visual_propagated` when no satellite anchor; AC-1.4-compliant 95% covariance ellipse; honest covariance per AC-NEW-4
- **Match assessment**: ✅ exact mode match for **mono+IMU**; ⚠️ partial input shape (image-resolution 4–5× larger than EuRoC's 752×480 → latency/memory unverified at full resolution); ⚠️ partial input rate (3 fps vs EuRoC's 20 Hz — see Fact #40)
- **If ⚠️ or ❌**: docs do not explicitly disqualify the configuration. The launch surface (`use_stereo`, `max_cameras`, `config_path`) supports the project's mode without source patches. Resolution and rate are **runtime/Jetson-MVE concerns**, not API-mode concerns. → Status: **Documentary lead**; final promotion to "Selected" requires Jetson Orin Nano Super hardware MVE artifact (D-C1-2 deferred phase).
### MVE — VINS-Mono in monocular + IMU mode (single mode by construction)
- **Source**: Source #55 (VINS-Mono README §3.1.1 + cross-source VINS-Fusion `context7` `euroc_mono_imu_config.yaml`), accessed 2026-05-08
- **Inputs in the example**: EuRoC MAV monocular VI dataset (the README explicitly notes "Although it contains stereo cameras, we only use one camera"); ROS topics with image rate >20 Hz and IMU rate >100 Hz per README §5.1; pinhole or MEI camera model with intrinsics + distortion calibrated; camera-IMU extrinsic + temporal calibration optional (online estimation supported via `estimate_extrinsic` and `estimate_td` params)
- **Outputs in the example**: 6-DoF pose at IMU rate via sliding-window optimization with covariance from optimization Hessian; loop closure via DBoW2; pose-graph save/reuse via `s` keystroke
- **Project inputs**: 1× ADTi 20MP nav frame stream (5472×3648, target 3 fps — **below documentary 20 Hz floor**) + FC IMU via MAVLink (SCALED_IMU2 at ≥100 Hz)
- **Project outputs required**: same as OpenVINS MVE above
- **Match assessment**: ✅ exact mode match (single-mode system, the project's pinned mode IS the only mode); ⚠️ partial input rate (3 fps vs documentary 20 Hz minimum recommendation per Fact #40); ⚠️ partial dependency stack (Ceres v1.14.0 vs JetPack 6 stock Ceres needs verification); ⚠️ partial input resolution (EuRoC 752×480 vs project 5472×3648)
- **If ⚠️ or ❌**: README §5.1 *"The image should exceed 20Hz and IMU should exceed 100Hz"* — explicit documentary disqualifier for sub-20-Hz operation absent contrary measurement. Geometric analysis (Fact #40) shows in-image displacement at 3 fps is small (~0.84% of frame width) and KLT-trackable, but the documentary minimum is not validated by the upstream authors at this rate. → Status: **Experimental only** until Jetson MVE explicitly measures sub-20-Hz behaviour, OR until the Plan phase commits to the dual-rate camera pipeline (binned high-rate to VIO + full-res 3 fps to VPR — see Fact #40) which would put VINS-Mono back on a documentary lead path.
### MVE — OKVIS2 in monocular + IMU mode
- **Source**: Source #56 (OKVIS2 README "Running the demo application" + "Building the project with ROS2" + arXiv:2202.09199), accessed 2026-05-08
- **Inputs in the example**: EuRoC ASL/ETH dataset directory (e.g., MH_01_easy/) + a config file from the `config/` directory; alternative live input via Realsense D435i/D455 through `okvis_app_realsense`; the i-th camera frame `C_i` in the OKVIS coordinate model permits multi-camera operation but mono is supported when `C_0` is the only configured camera in the YAML
- **Outputs in the example**: An `okvis::Trajectory` object that can be queried at any timestamp; updates delivered via `setOptimisedGraphCallback` (batch updates including loop closure) and high-rate prediction via `setImuCallback`; state `T_WS` (pose) + `v_W` (velocity) + `b_g`/`b_a` (gyro/accel biases)
- **Project inputs**: 1× ADTi 20MP nav frame stream (5472×3648, target 3 fps) + FC IMU via MAVLink (SCALED_IMU2 at ≥100 Hz) → re-published to `/okvis/cam0/image_raw` + `/okvis/imu0` topics in the ROS 2 build path
- **Project outputs required**: same as OpenVINS MVE above
- **Match assessment**: ✅ exact mode match for **mono+IMU**; ✅ structural argument for sub-20-Hz tolerance (keyframe-based architecture per Fact #40); ⚠️ partial input shape (image resolution unverified at 5472×3648 — config files in `config/` are tuned for D435i/EuRoC resolutions); ⚠️ partial Jetson Orin Nano direct evidence (no community benchmark surfaced)
- **If ⚠️ or ❌**: docs do not explicitly disqualify the configuration; the keyframe architecture is the structural mitigant for the project's frame-rate target. Optional LibTorch sky-segmentation can be disabled with `USE_NN=OFF` to remove the Jetson LibTorch dependency. → Status: **Documentary lead with structural advantage at sub-20-Hz**; final promotion to "Selected" requires Jetson Orin Nano Super hardware MVE artifact (D-C1-2 deferred phase).
### MVE — Pure VO baseline (KLT optical flow + 5-point essential matrix or homography RANSAC) — IMU-fusion external
- **Source**: Source #53 (OpenCV `cv::calcOpticalFlowPyrLK` + `cv::findEssentialMat` + `cv::findHomography` + `cv::Rodrigues` + reference implementation `alishobeiri/Monocular-Video-Odometery` MIT 2018)
- **Inputs in the example**: Sequence of monocular grayscale frames; OpenCV cookbook tutorial uses KITTI Odometry sequences (1241×376 at 10 fps, ground-plane motion); reference impl uses webcam at variable rate
- **Outputs in the example**: Sequence of relative-pose 3×4 matrices `[R|t]` per frame pair (arbitrary scale via 5-point essential; metric scale recoverable via known scene structure or external IMU integration)
- **Project inputs**: 1× ADTi 20MP nav frame stream (5472×3648, target 3 fps); FC IMU consumed by an **external metric-scale wrapper** (loosely-coupled ESKF that integrates IMU between visual updates and rescales the visual-odometry translation to metric units)
- **Project outputs required**: same as VIO MVEs above; the external wrapper produces the C5-style covariance because pure VO has no native covariance
- **Match assessment**: ⚠️ partial — the visual-odometry stage matches exactly (mono VO → relative pose); the IMU-fusion stage is **NOT in this candidate** and must be a separately-designed external module (loosely-coupled ESKF). At the C1 component scope, this candidate is "VO-only" and explicitly requires C5 to provide IMU fusion and covariance.
- **If ⚠️ or ❌**: → Status: **Mandatory simple-baseline reference**, NOT a lead. Used to anchor failure-analysis discussion in `solution_draft01` and as a runnable fallback if all VIO candidates fail Jetson MVE. The external IMU-fusion wrapper for this candidate becomes part of C5 (state estimator) candidate scope, not C1.
---
## C1 — Per-numbered-Restriction × Per-numbered-AC Sub-Matrix per Candidate
> Per Per-Mode API Capability Verification rule item 4: every numbered Restriction line and every numbered Acceptance Criterion is bound to one of `{Pass, Fail, Verify, N/A}` per candidate, with one-line evidence cite. Lines marked N/A are out of C1 scope (handled by C2 / C3 / C4 / C5 / C6 / C7 / C8 / C9 / C10). Cells marked `Verify` block final "Selected" promotion until the Jetson Orin Nano Super hardware MVE phase resolves them.
### Sub-matrix legend
- **Pass**: pinned mode satisfies the line with cited documentary evidence
- **Fail**: pinned mode contradicts the line with cited documentary evidence
- **Verify**: no documentary evidence either way; deferred Jetson MVE phase will resolve
- **N/A**: line is irrelevant to C1 (will be bound by C2/.../C10 in their respective rows)
### Cross-cutting N/A lines (apply to ALL C1 candidates)
The following AC and Restriction lines are out of C1 scope and are marked N/A for every C1 candidate without per-candidate citation:
- **All of AC-2.1b** (satellite-anchor registration) — bound by C2 (VPR) + C3 (matcher) + C4 (PnP)
- **All of AC-2.2 (cross-domain MRE branch)** — bound by C3 (matcher)
- **AC-3.4** (operator re-loc hint) — bound by C8 (FC adapter) + C10 (operator UX)
- **All of AC-6.x** (GCS telemetry) — bound by C8
- **All of AC-7.x** (AI-camera object localization) — bound outside C1 entirely
- **All of AC-8.x** (satellite reference imagery) — bound by C6 (tile cache) + C10 (provisioning)
- **All of AC-NEW-3** (FDR records — except the "per-frame estimates with covariance + source-label" line which is a downstream pass-through of C1 output) — bound by C5 (state estimator emits the per-frame record) + system-wide FDR component
- **All of AC-NEW-5** (operating environmental envelope: −20 °C to +50 °C, vibration, cooling) — bound by C7 (Jetson runtime / thermal scheduler) + system-wide thermal design
- **All of AC-NEW-6** (imagery freshness enforcement) — bound by C6 + C10
- **All of AC-NEW-7** (cache-poisoning safety budget) — bound by C5 + C6 + system-wide
- **Restriction "Satellite Imagery" entire section** — bound by C6 + C10
- **Restriction "Communication protocol (pinned)"** + **"Output to FC"** — bound by C8
- **Restriction "Ground station"** — bound by C8
### OpenVINS — per-numbered binding (C1-relevant lines only; cross-cutting N/A above)
| Line | Binding | Evidence (one-line cite) |
|---|---|---|
| AC-1.3 (drift between anchors: <100 m visual-only / <50 m IMU-fused) | **Verify** | OpenVINS produces metric-scale 6-DoF + IMU-fused covariance; absolute drift between anchors is a function of nav-cam frame rate + texture + IMU bias — Jetson MVE on Derkachi flight required |
| AC-1.4 (95% covariance ellipse + source label) | **Pass** | MSCKF produces native 6×6 covariance from filter state; source label is a downstream pipeline concern (C5) — OpenVINS provides the covariance input |
| AC-2.1a (frame-to-frame registration ≥95% normal flight) | **Verify** | OpenVINS feature-tracking front-end (KLT-based) success rate at 3 fps × 5472×3648 nadir-down low-texture cropland — Jetson MVE on Derkachi flight required |
| AC-2.2 (frame-to-frame MRE <1.0 px) | **Verify** | OpenVINS reports per-feature reprojection residuals via the MSCKF measurement model; aggregate MRE under nadir-down low-texture conditions — Jetson MVE measurement |
| AC-3.1 (tolerate 350 m outliers ±20° tilt) | **Pass (with Verify scope)** | MSCKF outlier-rejection via Mahalanobis gating is documented; the 350 m / ±20° envelope is an integration boundary owned by C5 — OpenVINS provides the per-feature gate |
| AC-3.2 (sharp turns <5% overlap, <200 m drift, <70° heading change) | **Verify** | OpenVINS has documented failure-detection + recovery; recovery via satellite-reference re-localization (AC-3.3) is owned by C2/C3 — OpenVINS must trigger the recovery path, MVE measurement of sharp-turn recovery on Derkachi flight |
| AC-3.3 (≥3 disconnected segments via satellite re-localization) | **Pass** | OpenVINS has documented failure-detection + recovery API (`StateOptions`); the re-localization input is provided by C2/C3 |
| AC-3.5 (visual blackout + spoofed GPS → dead_reckoned label, ≤400 ms) | **Verify** | OpenVINS internal mode promotion (`SLAM` ↔ `IMU-only propagation`) latency under feature-loss conditions — Jetson MVE measurement; the label-state transition is owned by C5 |
| AC-4.1 (latency <400 ms p95) | **Verify** | Documented Xavier NX baseline ~270 ms at 640×480 (Source #45 issue #164); 5472×3648 + Jetson Orin Nano Super at 3 fps unverified — Jetson MVE measurement |
| AC-4.2 (memory <8 GB shared) | **Verify** | MSCKF has lower memory footprint than full sliding-window optimization; Jetson Orin Nano Dev Kit build confirmed (Source #45 issue #421) but co-resident memory pressure with C2/C3/C5/C6 not measured |
| AC-4.4 (frame-by-frame, no batching) | **Pass** | OpenVINS publishes pose at IMU rate (per Source #54 launch evidence); no batching by design |
| AC-4.5 (corrections allowed) | **Pass** | MSCKF natively re-linearises in its sliding window; corrections via state augmentation are documented |
| AC-5.1 (initialise from FC EKF's last valid GPS + IMU-extrapolated position) | **Pass** | OpenVINS supports custom initialisation via `init_options` (per Source #54 estimator config); the FC-EKF input is plumbed by C5/C8 |
| AC-5.3 (re-initialise on companion reboot from FC IMU-extrapolated position) | **Pass** | Same mechanism as AC-5.1; AC-NEW-1 covers the timing constraint |
| AC-NEW-1 (cold-start TTFF <30 s) | **Verify** | OpenVINS initialisation latency under co-resident process startup on Jetson Orin Nano Super — Jetson MVE measurement |
| AC-NEW-3 (per-frame estimates with covariance + source-label feed FDR) | **Pass** | OpenVINS publishes pose+covariance at IMU rate; the source-label and FDR pipeline are downstream (C5 + system-wide) |
| AC-NEW-4 (false-position safety budget — covariance honesty) | **Pass (with Verify)** | MSCKF produces filter-consistent 6×6 covariance; honest-covariance discipline is shared with C5 (which carries the contract to AC-4.3); covariance under-reporting in the presence of cross-domain matches is a known MSCKF failure mode (Fact #5 family) — Jetson MVE on Derkachi flight required for empirical floor |
| AC-NEW-8 (visual blackout + GPS spoofing — IMU-only ≤30 s, label dead_reckoned) | **Pass** | OpenVINS has documented IMU-only propagation mode after visual feature loss; the failsafe-label transition is owned by C5 |
| Restriction "Sharp turns are exceptions; consecutive photos may share <5% overlap" | **Verify** | Same as AC-3.2 — Jetson MVE measurement |
| Restriction "Navigation camera (pinned): ADTi 20MP 20L V1, 5472×3648" | **Verify** | Image-resolution scaling (16× larger than EuRoC's 752×480 baseline) — Jetson MVE measurement of feature-extraction latency at full-res; binned/cropped path option per Fact #40 |
| Restriction "Companion computer (pinned): Jetson Orin Nano Super, 8 GB shared" | **Verify** | Build confirmed (Source #45 issue #421); steady-state co-resident memory pressure unverified — Jetson MVE measurement |
| Restriction "High-rate IMU available from FC via MAVLink" | **Pass** | OpenVINS consumes IMU at any rate ≥100 Hz; SCALED_IMU2 at FC's native rate (typically 100–400 Hz) satisfies this |
### VINS-Mono — per-numbered binding (C1-relevant lines only; cross-cutting N/A above)
| Line | Binding | Evidence (one-line cite) |
|---|---|---|
| AC-1.3 (drift between anchors) | **Verify** | Same as OpenVINS; sliding-window optimisation has higher drift than MSCKF in low-texture per academic comparison — Jetson MVE measurement |
| AC-1.4 (covariance ellipse + source label) | **Pass** | Sliding-window optimisation produces native covariance from optimization Hessian; source label is C5's concern |
| AC-2.1a (frame-to-frame registration ≥95%) | **Fail (documentary) → Verify** | VINS-Mono README §5.1 documents 20 Hz minimum image rate; project's 3 fps is below this floor (Fact #40) → ⚠️ **Experimental only** until Jetson MVE explicitly validates sub-20-Hz operation |
| AC-2.2 (MRE <1.0 px) | **Verify** | Same as OpenVINS; reprojection error under sub-20-Hz operation unverified |
| AC-3.1 (tolerate 350 m outliers ±20° tilt) | **Pass (with Verify scope)** | VINS-Mono has documented failure-detection + recovery |
| AC-3.2 (sharp turns) | **Verify** | Same as OpenVINS; under sub-20-Hz operation, sharp-turn recovery unverified — Jetson MVE measurement |
| AC-3.3 (disconnected segments via satellite re-localization) | **Pass** | VINS-Mono has documented failure-recovery; pose-graph reuse via DBoW2 supports re-anchor |
| AC-3.5 (visual blackout + spoofed GPS) | **Verify** | Same as OpenVINS |
| AC-4.1 (latency <400 ms p95) | **Verify** | Documented on Jetson Nano (Source #43); Orin Nano Super virtually certain to meet but at 5472×3648 unverified — Jetson MVE measurement |
| AC-4.2 (memory <8 GB shared) | **Verify** | Same as OpenVINS |
| AC-4.4 (frame-by-frame) | **Pass** | VINS-Mono publishes pose at IMU rate |
| AC-4.5 (corrections allowed) | **Pass** | Sliding-window optimization re-linearises and supports corrections |
| AC-5.1 (initialise from FC EKF) | **Pass** | VINS-Mono has automatic initialization via IMU pre-integration; custom-init from FC EKF is a wiring task |
| AC-5.3 (re-initialise on reboot) | **Pass** | Same as AC-5.1 |
| AC-NEW-1 (cold-start TTFF <30 s) | **Verify** | VINS-Mono automatic initialization typically takes seconds; Jetson MVE measurement |
| AC-NEW-3 (per-frame estimates feed FDR) | **Pass** | Same as OpenVINS |
| AC-NEW-4 (covariance honesty) | **Pass (with Verify)** | Same as OpenVINS; sliding-window optimization Hessian is a less-conservative covariance source than MSCKF in some failure modes |
| AC-NEW-8 (visual blackout + GPS spoofing) | **Pass (with Verify)** | VINS-Mono has documented failure-detection and IMU-only propagation; failsafe-label transition is C5's |
| Restriction "Sharp turns are exceptions" | **Verify** | Same as AC-3.2 |
| Restriction "Navigation camera (pinned): 5472×3648" | **Verify** | Same as OpenVINS; **plus** the Fact #40 dual-rate option is an explicit Plan-time consideration to bring VINS-Mono back from Experimental to documentary lead |
| Restriction "Companion computer: Jetson Orin Nano Super, 8 GB" | **Verify** | Same as OpenVINS; Ceres v1.14.0 vs JetPack 6 stock Ceres compatibility is an additional sub-verify item |
| Restriction "High-rate IMU available from FC via MAVLink" | **Pass** | VINS-Mono consumes IMU at ≥100 Hz; satisfied |
### OKVIS2 / OKVIS2-X — per-numbered binding (C1-relevant lines only; cross-cutting N/A above)
| Line | Binding | Evidence (one-line cite) |
|---|---|---|
| AC-1.3 (drift between anchors) | **Verify** | Factor-graph back-end with loop closure should produce lower drift than non-loop VIO; specific Derkachi-flight measurement deferred to Jetson MVE |
| AC-1.4 (covariance ellipse + source label) | **Pass** | OKVIS2 produces 6×6 covariance from factor-graph marginal; source label is C5's concern |
| AC-2.1a (frame-to-frame registration ≥95%) | **Pass (structural argument) → Verify** | Keyframe-based selection is structurally tolerant of variable input rates (Fact #40); explicit 3 fps validation deferred to Jetson MVE |
| AC-2.2 (MRE <1.0 px) | **Verify** | OKVIS2 has tight reprojection-error inlier rejection in its keyframe matching; aggregate MRE under nadir-down low-texture — Jetson MVE measurement |
| AC-3.1 (tolerate 350 m outliers ±20° tilt) | **Pass** | OKVIS2 has Cauchy-loss robust factor graph that tolerates outliers; documented in arXiv:2202.09199 |
| AC-3.2 (sharp turns) | **Pass (structural)** | Keyframe selection inherently skips uninformative sharp-turn frames; recovery via re-localization is owned by C2/C3 |
| AC-3.3 (≥3 disconnected segments) | **Pass** | OKVIS2 has explicit re-localization API + loop closure; OKVIS2-X adds GNSS-fusion which architecturally aligns with the spoof-promotion path (per Fact #31) |
| AC-3.5 (visual blackout + spoofed GPS) | **Verify** | OKVIS2 IMU-only propagation between keyframes is via `setImuCallback`; latency under blackout-trigger — Jetson MVE measurement |
| AC-4.1 (latency <400 ms p95) | **Verify** | No documented Jetson Orin Nano measurement (Fact #31); factor-graph is plausibly heavier than MSCKF — Jetson MVE measurement |
| AC-4.2 (memory <8 GB shared) | **Verify** | Same as AC-4.1; co-resident memory pressure with C2/C3/C5/C6 unverified |
| AC-4.4 (frame-by-frame) | **Pass** | `setImuCallback` provides high-rate prediction; `setOptimisedGraphCallback` provides batch updates including loop closure — both stream frame-by-frame from a consumer perspective |
| AC-4.5 (corrections allowed) | **Pass** | Factor-graph re-linearisation on loop closure delivers corrections via `setOptimisedGraphCallback` |
| AC-5.1 (initialise from FC EKF) | **Pass** | OKVIS2 supports custom initialisation via the `okvis::ViInterface` API; the FC-EKF input is plumbed by C5/C8 |
| AC-5.3 (re-initialise on reboot) | **Pass** | Same mechanism as AC-5.1 |
| AC-NEW-1 (cold-start TTFF <30 s) | **Verify** | OKVIS2 initialisation latency under co-resident process startup — Jetson MVE measurement |
| AC-NEW-3 (per-frame estimates feed FDR) | **Pass** | OKVIS2 trajectory query at any timestamp via `okvis::Trajectory` supports the FDR pipeline |
| AC-NEW-4 (covariance honesty) | **Pass (with Verify)** | Factor-graph marginal covariance is the gold standard for honest covariance among VIO classes; cross-domain match consistency under satellite anchor injection unverified — Jetson MVE measurement |
| AC-NEW-8 (visual blackout + GPS spoofing) | **Pass** | OKVIS2 has documented IMU-only propagation between keyframes; OKVIS2-X GNSS-fusion is architecturally aligned with the spoof-promotion path |
| Restriction "Sharp turns are exceptions" | **Pass (structural)** | Keyframe selection inherently handles sparse-overlap sharp-turn frames |
| Restriction "Navigation camera (pinned): 5472×3648" | **Verify** | Image-resolution scaling — Jetson MVE measurement; OKVIS2 keyframe sub-sampling reduces the per-frame compute compared to per-frame VIO |
| Restriction "Companion computer: Jetson Orin Nano Super, 8 GB" | **Verify** | No direct Jetson Orin Nano Super measurement; LibTorch sky-segmentation can be disabled with `USE_NN=OFF` to remove a major Jetson dependency |
| Restriction "High-rate IMU available from FC via MAVLink" | **Pass** | `setImuCallback` consumes IMU at any rate ≥100 Hz; satisfied |
### Pure VO baseline (KLT + 5pt RANSAC / homography) — per-numbered binding (C1-relevant lines only; cross-cutting N/A above)
| Line | Binding | Evidence (one-line cite) |
|---|---|---|
| AC-1.3 (drift between anchors — visual-only/IMU-fused) | **Fail (visual-only sub-bound)** | Pure VO has higher drift than VIO; the "<100 m visual-only" sub-bound is achievable, but the "<50 m IMU-fused" requires the external ESKF wrapper (which is part of C5, not this candidate) |
| AC-1.4 (covariance ellipse + source label) | **Fail** | Pure VO has no native covariance; covariance is provided by the external ESKF wrapper (C5) |
| AC-2.1a (frame-to-frame registration ≥95%) | **Pass** | KLT optical flow at 0.84% in-image displacement (Fact #40 calculation) is well within trackable range |
| AC-2.2 (MRE <1.0 px) | **Pass (with Verify)** | OpenCV `findHomography` with RANSAC produces sub-pixel inliers under planar steppe geometry; explicit measurement on Derkachi flight needed |
| AC-3.1 (tolerate 350 m outliers ±20° tilt) | **Verify** | RANSAC outlier rejection threshold is tunable; explicit measurement under ±20° airframe tilt needed |
| AC-3.2 (sharp turns) | **Fail** | Pure VO has no failure-recovery mechanism; sharp turns trigger KLT track loss; recovery via satellite re-localization (AC-3.3) is owned by C2/C3 — pure VO must signal track loss to C5 |
| AC-3.3 (≥3 disconnected segments) | **N/A (handled by C5+C2/C3)** | Pure VO does not have re-localization; the disconnected-segment recovery is C2/C3's job |
| AC-3.5 (visual blackout + spoofed GPS) | **N/A (handled by C5)** | Pure VO has no failsafe state; C5 owns the dead_reckoned transition |
| AC-4.1 (latency <400 ms p95) | **Pass** | OpenCV KLT + RANSAC at 5472×3648 on Jetson Orin Nano CPU is documented as <100 ms class; latency budget is dominated by image I/O |
| AC-4.2 (memory <8 GB shared) | **Pass** | KLT + RANSAC has trivial memory footprint (<100 MB working set) |
| AC-4.4 (frame-by-frame) | **Pass** | Pure per-frame algorithm; no batching |
| AC-4.5 (corrections allowed) | **N/A (handled by C5)** | Pure VO has no state to correct; C5 owns corrections |
| AC-5.1 (initialise from FC EKF) | **N/A (handled by C5)** | Pure VO has no global state; C5 owns the initial pose |
| AC-5.3 (re-initialise on reboot) | **N/A (handled by C5)** | Same as AC-5.1 |
| AC-NEW-1 (cold-start TTFF <30 s) | **Pass** | Pure VO needs no warm-up beyond first frame pair |
| AC-NEW-3 (per-frame estimates feed FDR) | **N/A (handled by C5)** | Pure VO emits relative pose only; FDR records the C5-fused estimate |
| AC-NEW-4 (covariance honesty) | **Fail** | Pure VO has no native covariance; honest-covariance discipline is the external wrapper's contract (C5) |
| AC-NEW-8 (visual blackout + GPS spoofing) | **N/A (handled by C5)** | Pure VO has no failsafe behavior; C5 owns the IMU-only mode |
| Restriction "Sharp turns are exceptions" | **Fail** | Same as AC-3.2 |
| Restriction "Navigation camera (pinned): 5472×3648" | **Pass** | KLT runs at any resolution; 5472×3648 may need image pyramid downsampling for runtime — standard OpenCV practice |
| Restriction "Companion computer: Jetson Orin Nano Super, 8 GB" | **Pass** | Trivial memory + CPU-bound; no GPU dependency |
| Restriction "High-rate IMU available from FC via MAVLink" | **N/A (handled by C5)** | Pure VO does not consume IMU; the external wrapper does |
**Pure VO baseline summary**: this candidate is **NOT a drop-in C1 VIO replacement**. It is a "VO + external IMU wrapper" two-component design where the external wrapper is owned by C5. As a C1 candidate it Fails AC-1.4 / AC-1.3 IMU-fused / AC-3.2 / AC-NEW-4 because those bindings inherently require IMU fusion which this candidate lacks. **Status remains "mandatory simple-baseline reference"** per Fact #35; the actual C1 fallback if all VIO leads fail Jetson MVE is "Pure VO + custom ESKF wrapper" — which is a Plan-phase design task, not a research-phase candidate.
---
## C1 — CLOSURE STATUS [2026-05-08 session]
C1 is **CLOSED at the documentary level**. All four lead candidates (OpenVINS, OKVIS2, VINS-Mono, Pure VO baseline) have:
- ✅ Pinned-mode statement
- ✅ Three-query `context7` (or equivalent) lookup with documentary evidence
- ✅ MVE block
- ✅ Per-numbered-Restriction × per-numbered-AC sub-matrix
**Final lead promotion to "Selected"** is gated by the **deferred Jetson Orin Nano Super hardware MVE phase** (D-C1-2 default = option (b) per Fact #41) — Plan phase MUST NOT lock a final C1 candidate without consuming the deferred Jetson MVE artifact.
**Per-license-track preliminary leads** (per Fact #41 default D-C1-1 = option (c) "keep both tracks open"):
- **BSD/permissive track lead**: **OKVIS2 / OKVIS2-X** — strongest documentary-mode-fit profile; structural sub-20-Hz tolerance; OKVIS2-X GNSS-fusion architectural alignment with spoof-promotion path (AC-NEW-2). Risk: no direct Jetson Orin Nano Super measurement.
- **GPL-3.0 track lead**: **OpenVINS** — best Jetson Orin Nano build evidence; MSCKF formulation more memory-efficient than VINS-Mono; documented Xavier NX 270 ms latency baseline. Risk: documentary 5472×3648 latency unverified.
- **GPL-3.0 track alternate**: **VINS-Mono** — single-mode by construction; ⚠️ Experimental only until Jetson MVE explicitly validates sub-20-Hz operation OR Plan commits to dual-rate camera pipeline (Fact #40).
**Mandatory simple-baseline**: **Pure VO + external ESKF (C5)** — kept as runnable fallback if all VIO leads fail Jetson MVE.
**Cross-cutting design decision raised by C1 closure**: the **single-rate vs dual-rate nav-camera pipeline** (Fact #40) is now an explicit Plan-phase deliverable, because it materially changes which C1 candidates remain on documentary lead vs Experimental status.
C1 → C2 transition: ready to proceed to C2 (VPR) candidate enumeration in the next session.
_docs/00_research/02_fact_cards/C2_vpr.md
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