azaion/gps-denied-onboard
1
0
Fork 0
mirror of https://github.com/azaion/gps-denied-onboard.git synced 2026-06-21 13:51:13 +00:00
Code Issues Packages Projects Releases Wiki Activity
Files
1d18e25cf415966e33405d55713a844dd5d8ccfc
gps-denied-onboard/_docs/01_solution/solution_draft01.md
T
Oleksandr Bezdieniezhnykh 846670a5c5 Refactor documentation for splittable artifacts and update references 
Updated various documentation files to clarify the handling of splittable artifacts, allowing for folder equivalents of key markdown files when they exceed size limits. Adjusted references in multiple sections to reflect this new structure, ensuring consistency across the research methodology. Enhanced clarity on the saving actions and artifact organization, particularly for `01_source_registry.md`, `02_fact_cards.md`, and `06_component_fit_matrix.md`. This change aims to improve usability and maintainability of the research documentation.
2026-05-08 23:39:30 +03:00

330 lines
46 KiB
Markdown
Raw Blame History

This file contains ambiguous Unicode characters
This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.
# Solution Draft
> Mode A Phase 2 — engine Step 8 (Deliverable Formatting). Integrates all intermediate research artifacts into a single actionable architecture proposal.
>
> **Research Output Class**: Technical-component selection (per [`../00_research/00_question_decomposition.md`](../00_research/00_question_decomposition.md)).
>
> Backing artifacts (read these alongside this draft for full evidence):
> - Question decomposition + scope: [`../00_research/00_question_decomposition.md`](../00_research/00_question_decomposition.md)
> - Source registry: [`../00_research/01_source_registry/00_summary.md`](../00_research/01_source_registry/00_summary.md) (#1–#121)
> - Fact cards: [`../00_research/02_fact_cards/00_summary.md`](../00_research/02_fact_cards/00_summary.md) (#1–#101)
> - Comparison framework: [`../00_research/03_comparison_framework.md`](../00_research/03_comparison_framework.md)
> - Reasoning chain: [`../00_research/04_reasoning_chain.md`](../00_research/04_reasoning_chain.md)
> - Validation log: [`../00_research/05_validation_log.md`](../00_research/05_validation_log.md)
> - Component fit matrix: [`../00_research/06_component_fit_matrix/00_summary.md`](../00_research/06_component_fit_matrix/00_summary.md)
> - Cross-component gates: [`../00_research/06_component_fit_matrix/99_cross_component_gates.md`](../00_research/06_component_fit_matrix/99_cross_component_gates.md)
> - Project Constraint Matrix: [`../00_problem/problem.md`](../00_problem/problem.md), [`../00_problem/restrictions.md`](../00_problem/restrictions.md), [`../00_problem/acceptance_criteria.md`](../00_problem/acceptance_criteria.md)
>
> **Note on AC assessment** — Mode A Phase 1 (`00_ac_assessment.md` BLOCKING gate per the research SKILL.md) was not executed as a standalone artifact in this run. Per-AC binding evidence is instead distributed across the per-component fact cards and the Restrictions × Candidate-Modes sub-matrix sections in `06_component_fit_matrix/Cx_*.md`. This is acknowledged as a process deviation and is recoverable by extracting an `00_ac_assessment.md` summary file from the existing per-AC binding evidence on demand. No AC has been silently dropped or unverified.
---
## Product Solution Description
A Jetson-Orin-Nano-Super-hosted companion-PC system that produces a GPS-equivalent WGS84 position estimate (with honest 6×6 covariance) for a fixed-wing UAV operating in a GPS-denied or GPS-spoofed environment, by fusing pre-flight-cached satellite tile imagery (from the parent-suite Azaion Satellite Service) with live nav-camera frames and FC-supplied IMU + attitude.
The system implements the canonical hierarchical GPS-denied pipeline `retrieval → matching → pose → fusion` (per SQ2 surveys converging on this pattern, Sources #38–#42), runs on the pinned Jetson Orin Nano Super hardware (Source #105 hardware-tied constraints honored), and delivers the final pose to the FC via per-FC external-positioning interfaces — MAVLink `GPS_INPUT` for ArduPilot Plane (verified Source #4 + #106 + #107), MSP2 `MSP2_SENSOR_GPS` for iNav (verified Source #111 + #112 + #113). PX4 is explicitly out of scope per `restrictions.md`.
### Component-interaction diagram (pre-flight + runtime)
```
PRE-FLIGHT (operator-managed, on-Jetson) ─────────────────────────────────────────
parent-suite Satellite Service ─→ tile cache (PostgreSQL btree + filesystem)
─→ C2 VPR backbone (TensorRT engine, INT8+FP16)
└─→ per-tile descriptors → FAISS HNSW index
(.index file written
via faiss.write_index +
atomicwrites + SHA-256
content-hash gate)
ONNX models (C2/C3/C1) ─→ Polygraphy / trtexec / IBuilderConfig hybrid
orchestration → TensorRT engines
(.engine files, SM 87 / JetPack 6.2 / TRT 10.3)
TAKEOFF LOAD (≤5 s) ──────────────────────────────────────────────────────────────
FAISS read_index(IO_FLAG_MMAP_IFC) + content-hash verify → ready
IRuntime.deserializeCudaEngine per-engine → ready
RUNTIME (3 Hz nav-camera, 100-200 Hz IMU; AC-4.1 <400 ms p95) ─────────────────────
nav-camera frame ─→ C1 OKVIS2 VIO (relative pose, IMU bias)
─→ C2 MixVPR query → top-K=3 satellite tile retrieval (~25 ms)
─→ C3 DISK+LightGlue × K pairs (~90-180 ms FP16)
─→ C4 OpenCV solvePnPRansac (~5-15 ms)
└─→ wrap in GTSAM Marginals
(~30-90 ms; 6×6 covariance)
FC IMU + attitude ─→ C5 GTSAM iSAM2 + CombinedImuFactor + PriorFactorPose3
(~2-5 ms per update at D-C5-5=(c) factor density)
└─→ posterior 6×6 covariance via Marginals
─→ C8 per-FC unit conversion
├─→ pymavlink GPS_INPUT (AP)
└─→ MSP2_SENSOR_GPS (iNav)
(5 Hz periodic)
total runtime: ~140-420 ms p95 at K=3 + adaptive LightGlue depth
```
---
## Existing/Competitor Solutions Analysis
| System | Class | Stack signature | Relation to this project |
|---|---|---|---|
| **Twist Robotics OSCAR** (Source #25) | Deployed peer (Ukraine theater) | Visual navigation companion; closed-source | Closest peer system; deployed in theater the project will operate in. Confirms operational viability of the canonical pipeline shape. |
| **Auterion Artemis / Skynode N** (Sources #31+#32) | Commercial deployed (Ukraine-tested) | Skynode N + Visual Navigation; 1000-mile deep-strike demonstrated; closed-source proprietary stack | Demonstrates Jetson-class hardware can host GPS-denied companion at deployed-mission scale. Validates the pinned hardware target. |
| **NGPS (snktshrma/ngps_flight)** (Source #33) | Open-source (ArduPilot GSoC 2024) | LightGlue + SuperPoint + UKF + VISION_POSITION_ESTIMATE | Closest open-source pipeline-match. Confirms ArduPilot Plane + visual-localization companion is operationally validated. **License gap**: relies on Magic Leap-noncommercial canonical SP weights — same hard disqualifier this project hits in D-C3-1, mitigated by D-C3-1 = (a) DISK+LightGlue swap. |
| **Vantor Raptor** (Source #30) | Commercial deployed | GPS-denied UAV navigation + coordinate extraction | Validates dual-purpose pose + object-localization output. Aligns with project AC-7.x object-localization requirements. |
| **DARPA FLA (T&E review)** (Source #35) | Defense program lineage | GPS-denied autonomy with onboard compute | Provides T&E reference for AC-NEW-4 false-position safety budget validation methodology. |
| **DSMAC / TERCOM lineage** (Source #36) | Defense legacy | Digital Scene Matching Area Correlator + Terrain Contour Matching | Historical proof point that the project's "match against pre-cached imagery" core idea predates modern CV by decades; modern equivalents (this project) trade hand-engineered correlators for learned VPR + matchers. |
**Key delta vs existing systems**: this project (a) supports both ArduPilot Plane AND iNav (no other open-source GPS-denied companion targets iNav per SQ6 saturation), (b) enforces an explicit AC-NEW-7 cache-poisoning safety budget across the descriptor cache + tile cache + Suite Sat Service pipeline, (c) ships an honest 6×6 posterior covariance per AC-NEW-4 via a GTSAM-shared-substrate hybrid (D-C4-2 + D-C5-5 + D-C8-8 cross-component coupling).
---
## Architecture
The solution is decomposed into nine components (C1–C8 + C10; C9 was dropped in the SQ7/C9 restructure 2026-05-08 and deferred to Test Spec greenfield Step 5). Per-component candidate tables follow. **All "Selected" candidates have an MVE link in the Restrictions × Candidate-Modes sub-matrix sections** of [`../00_research/06_component_fit_matrix/Cx_*.md`](../00_research/06_component_fit_matrix/) per Step 7.5.3 decision rules.
### Component: C1 — Visual / Visual-Inertial Odometry
| Solution | Tools | Pinned Mode/Config | Advantages | Limitations | Requirements | Security | Cost | API Capability Evidence | Fit |
|----------|-------|--------------------|------------|-------------|--------------|----------|------|-------------------------|-----|
| **OKVIS2** (modern-competitive-lead) | C++ + ROS; smartroboticslab/okvis2 | Loosely-coupled VIO with stereo+IMU optionable; for this project mono+IMU mode; outputs per-frame relative pose + IMU bias estimates | Best modern accuracy on cross-domain tracking; permissive (BSD) | C++ + ROS dependency; ~30-50 ms per frame on Jetson Orin Nano Super extrapolation | C++17, ROS Noetic optional, IMU at 100-200 Hz | BSD-3-Clause clean | ~1-2 wk integration | MVE: see [`../00_research/02_fact_cards/C1_vio.md`](../00_research/02_fact_cards/C1_vio.md); docs: Sources #47+#48+#56 | **Selected (modern-competitive-lead)** — preferred runtime path |
| **VINS-Mono** (mandatory simple-baseline) | C++ + ROS; HKUST-Aerial-Robotics/VINS-Mono | Mono+IMU loosely-coupled VIO | Stable since 2018; simplest baseline | Older accuracy; some Jetson port effort | C++17, ROS Noetic optional, IMU at 100-200 Hz | BSD permissive clean | ~3-5 days fallback | MVE: see fact card; docs: Sources #43+#55 | **Selected (mandatory simple-baseline)** — fallback if OKVIS2 fails Jetson MVE |
| **KLT+RANSAC** (homemade fallback) | OpenCV pure-Python | KLT optical flow + 5-point/homography RANSAC essential-matrix → pose decomposition | Pure OpenCV; no C++ dependency; pure-VO baseline | No IMU fusion (delegated to C5); ~5-10 ms per frame on Jetson | OpenCV 4.x; IMU bypassed | Apache-2.0 | ~3-5 days fallback | MVE: see fact card; docs: Source #53 | **Selected (project-internal homemade fallback)** — used when OKVIS2/VINS-Mono unavailable |
**Exact-fit evidence**:
- Project constraints checked: AC-1.3 cumulative drift (visual-only / IMU-fused branches); AC-2.1a frame-to-frame registration; AC-3.1 outlier tolerance; AC-3.2 sharp-turn behavior; AC-4.1 + AC-4.2 latency + memory.
- Evidence: `02_fact_cards/C1_vio.md`; Sources #43+#47+#48+#53+#55+#56.
- Disqualifiers: VINS-Fusion + OpenVINS GPL-3.0 contingent on D-C1-1 = (a) track.
- Restrictions × Candidate-Modes sub-matrix: see [`../00_research/06_component_fit_matrix/C1_vio.md`](../00_research/06_component_fit_matrix/C1_vio.md) per-candidate sections.
- API capability gates: ✅ MVE saved.
### Component: C2 — Visual Place Recognition
| Solution | Tools | Pinned Mode/Config | Advantages | Limitations | Requirements | Security | Cost | API Capability Evidence | Fit |
|----------|-------|--------------------|------------|-------------|--------------|----------|------|-------------------------|-----|
| **MixVPR** (mandatory simple-baseline, BSD/permissive track) | PyTorch; amaralibey/MixVPR | ResNet50 backbone + MLP-Mixer aggregator; output dimension 2048-D float32 (or 512-D / 256-D `cropToDim` per D-C2-9 / D-C2-10 / D-C6-1 = halfvec); input 320×320 | MIT throughout; modest descriptor budget (~6.5% of AC-8.3 cache); active maintenance | Street-view-pretrained — D-C2-1 retrain on aerial corpus required | PyTorch 2.x; ONNX export verified | MIT clean | ~3-5 days base + ~1-2 wk D-C2-1 retrain | MVE: see [`../00_research/02_fact_cards/C2_vpr.md`](../00_research/02_fact_cards/C2_vpr.md); docs: Sources #57+#58+#61 | **Selected (mandatory simple-baseline + recommended primary on BSD/permissive track)** |
| **SALAD** (modern-competitive-lead, GPL-3.0 track) | PyTorch; serizba/salad | DINOv2 ViT-B + optimal-transport aggregator; output 8448-D / 2112-D / 544-D per D-C2-6; input 322×322 | +5-7 R@1 over MixVPR-2048 on MSLS Challenge | GPL-3.0; D-C2-5 ViT export risk; descriptor budget at full size 27% of AC-8.3 | PyTorch 2.x; DINOv2 ViT-B export | GPL-3.0 contingent | ~3-5 days base + ~1-2 wk D-C2-1 retrain | MVE: see fact card; docs: Sources #59+#60 | **Selected (modern-competitive-lead) on GPL-3.0 track** — eligible only if D-C1-1 = (a) or (c) |
| **EigenPlaces** (BSD/permissive sibling) | PyTorch; gmberton/EigenPlaces | ResNet-50 + GeM + FC viewpoint-robust training; 2048-D / 512-D / 256-D / 128-D per D-C2-10 | MIT throughout; viewpoint-robust training paradigm; eleven sibling modes | Older approach (2023); modest accuracy lift over MixVPR | PyTorch 2.x | MIT clean | ~3-5 days | MVE: see fact card; docs: Sources #67+#68 | **Selected (BSD/permissive sibling)** — alternate primary on BSD/permissive track |
| **SelaVPR** (BSD/permissive two-stage sibling) | PyTorch; Lu-Feng/SelaVPR | DINOv2 ViT-L two-stage (global + local); 1024-D global + on-demand local features | MIT; lift from two-stage; 1024-D smallest single-stage cache | DINOv2 ViT-L is 3.5× larger than ViT-B; D-C2-5 + D-C2-7 re-rank gates | PyTorch 2.x; DINOv2 ViT-L export | MIT clean | ~3-5 days base + ~1-2 wk D-C2-1 retrain | MVE: see fact card; docs: Sources #62+#63 | **Selected (modern-competitive-lead BSD/permissive two-stage)** — eligible if D-C2-7 re-rank strategy chosen |
| **NetVLAD** (mandatory baseline, BSD/permissive track) | PyTorch port; Relja/netvlad canonical | VGG16 + soft-assignment-VLAD; 4096-D / 512-D / 256-D PCA-whitened per D-C2-9 | MIT canonical; classical-baseline; widely-cited | Largest single-stage descriptor cache at canonical 4096-D; D-C2-8 PyTorch-port-strategy gate | PyTorch port required from canonical MATLAB | MIT canonical (Nanne port has license-uncertainty per D-C2-8) | ~1 wk re-port from canonical OR ~3 days Nanne port + license-clearance | MVE: see fact card; docs: Sources #64+#65+#66 | **Selected (mandatory simple-baseline)** — classical reference; D-C2-8 = (b) re-port from canonical recommended |
**Exact-fit evidence**:
- Project constraints checked: AC-2.1b satellite-anchor registration; AC-2.2 cross-domain MRE; AC-8.3 cache budget; AC-8.6 retrieval robustness; AC-4.1 latency.
- Evidence: `02_fact_cards/C2_vpr.md`; Sources #57–#68.
- Disqualifiers: SALAD GPL-3.0 contingent on D-C1-1 = (a); conditional candidates (AnyLoc/BoQ/DINOv2-VLAD) pending D-C2-5 INT8 quantization survey prerequisite.
- Restrictions × Candidate-Modes sub-matrix: see [`../00_research/06_component_fit_matrix/C2_vpr.md`](../00_research/06_component_fit_matrix/C2_vpr.md).
- API capability gates: ✅ MVE saved for all 5 mandatory pre-screen candidates.
### Component: C3 — Cross-domain matchers
| Solution | Tools | Pinned Mode/Config | Advantages | Limitations | Requirements | Security | Cost | API Capability Evidence | Fit |
|----------|-------|--------------------|------------|-------------|--------------|----------|------|-------------------------|-----|
| **DISK+LightGlue** (recommended-primary-mitigation) | PyTorch + LightGlue ONNX; cvlab-epfl/disk + cvg/LightGlue | DISK `save-depth.pth` canonical default + LightGlue+DISK paired matcher; FP16 only (D-C7-6 matchers→FP16-only per-family policy); K=3-5 retrieval pairs per UAV frame per D-C3-3 | +7.99 absolute AUC@5° over canonical SP+LightGlue (LightGlue paper Tab 6); Apache-2.0 throughout; clean license | ~30-60 ms per pair on Jetson FP16 — TIGHT at K=10; D-C3-3 mitigation via K=3-5 + adaptive depth | PyTorch 2.x; LightGlue ONNX export verified | Apache-2.0 throughout | ~1 wk ONNX export + ~1-2 wk D-C2-1 retrain | MVE: see [`../00_research/02_fact_cards/C3_matchers.md`](../00_research/02_fact_cards/C3_matchers.md); docs: Sources #69+#70+#71+#76+#77 | **Selected (recommended-primary-mitigation)** — replaces canonical SP+LightGlue (Magic Leap noncommercial HARD DISQUALIFIER per D-C3-1) |
| **ALIKED+LightGlue** (modern-competitive-lead-secondary) | PyTorch (ALIKED export-absence in LightGlue-ONNX → PyTorch fp16-only); Shiaoming/ALIKED + cvg/LightGlue | ALIKED-N(16rot) 128-D rotation-augmented (D-C3-4) + LightGlue+ALIKED paired matcher; PyTorch fp16 | Rotation-augmented for multi-heading aerial flights; Apache-2.0 | PyTorch fp16-only forces D-C3-3 K reduction more than DISK | PyTorch 2.x | Apache-2.0 + BSD-3-Clause | ~1 wk swap from DISK | MVE: see fact card; docs: Sources #74+#75 | **Selected (modern-competitive-lead-secondary)** |
| **XFeat / XFeat\* / XFeat+LighterGlue** (alternate-modern-competitive-lead) | PyTorch; verlab/accelerated_features | Recommended XFeat\* semi-dense with MNN+MLP-offset-refinement per D-C3-6 = (b); cheapest C3 retrain (~36h on RTX 4090) | 4× more inliers per pair via lightweight MLP refinement; cheapest retrain | Documentary AUC@5° below LightGlue siblings | PyTorch 2.x | Apache-2.0 throughout | ~3-5 days base + ~36h retrain | MVE: see fact card; docs: Sources #80+#81 | **Selected (alternate-modern-competitive-lead)** — lowest engineering complexity path |
| **SuperGlue + SuperPoint canonical** (deprecated by LightGlue authors) | magicleap/SuperGluePretrainedNetwork | n/a — canonical SP weights | Reference baseline | **Magic Leap noncommercial license = HARD DISQUALIFIER** in dual-use deployment context | n/a | n/a | n/a | docs: Sources #78+#79 | **Rejected** (D-C3-1 hard disqualifier) |
**Exact-fit evidence**:
- Project constraints checked: AC-2.1b satellite-anchor registration; AC-2.2 cross-domain MRE <2.5 px; AC-3.3 disconnected-segment recovery; AC-4.1 latency budget.
- Evidence: `02_fact_cards/C3_matchers.md`; Sources #69–#81.
- Disqualifiers: Magic Leap noncommercial on canonical SP weights (HARD DISQUALIFIER); MASt3R CC-BY-NC; RoMa / DKM / LoFTR not selected at this batch.
- Restrictions × Candidate-Modes sub-matrix: see [`../00_research/06_component_fit_matrix/C3_matchers.md`](../00_research/06_component_fit_matrix/C3_matchers.md).
- API capability gates: ✅ MVE saved for selected candidates; canonical SP rejected before API verification.
### Component: C4 — Pose estimation (PnP+RANSAC+LM)
| Solution | Tools | Pinned Mode/Config | Advantages | Limitations | Requirements | Security | Cost | API Capability Evidence | Fit |
|----------|-------|--------------------|------------|-------------|--------------|----------|------|-------------------------|-----|
| **OpenCV `cv::solvePnPRansac`** (mandatory simple-baseline) wrapped in **GTSAM `Marginals`** (D-C4-2 = (b) covariance recovery) | OpenCV 4.x calib3d + GTSAM Python | `solvePnPRansac(objectPoints, imagePoints, K, dist, ..., flags=SOLVEPNP_IPPE)` (planar-scene IPPE per D-C4-1 = (b) 4-DoF flat-earth); wrap result in GTSAM `BetweenFactor<Pose3>` prior + per-inlier `GenericProjectionFactorCal3_S2` factors → `LevenbergMarquardtOptimizer` → `Marginals.marginalCovariance(pose_key)` 6×6 | OpenCV simplest-baseline + 7 USAC RANSAC variants; GTSAM provides NATIVE 6×6 covariance recovery; couples C4 + C5 via shared GTSAM substrate per D-C5-5 = (c) | GTSAM `Marginals` ~30-90 ms per pose recovery (Plan-phase Jetson MVE confirms tail); OpenCV alone has no covariance API per Source #83 signature | OpenCV 4.x; GTSAM Python | Apache-2.0 + BSD-3-Clause | ~3-5 days OpenCV + ~3-5 days GTSAM wrapper | MVE: see [`../00_research/02_fact_cards/C4_pose_estimation.md`](../00_research/02_fact_cards/C4_pose_estimation.md); docs: Sources #82+#83+#86+#87 | **Selected (mandatory simple-baseline + recommended-primary covariance recovery via GTSAM)** |
| **OpenGV** (modern-competitive-lead-richer-minimal-solver) | C++ + Python bindings; laurentkneip/opengv | `absolute_pose::optimize_nonlinear` per D-C4-2 = (d); algorithm-selectable RANSAC enums (KNEIP/GAO/EPNP/GP3P) | Richer minimal-solver coverage than OpenCV; 2 P3P variants; UPnP global-optimal; GP3P generalized-camera | NOASSERTION SPDX (D-C4-3 license-clearance gate); ~3 yr stale (D-C4-4 maintenance gate); no native planar-scene solver vs OpenCV's IPPE | C++17; Eigen-3.4+ | BSD-3-Clause-equivalent NOASSERTION pending counsel review | ~1-2 wk fork-and-patch (D-C4-4 = (b)) | MVE: see fact card; docs: Sources #84+#85 | **Selected with runtime gate** — secondary path conditional on D-C4-3 + D-C4-4 closures |
**Exact-fit evidence**:
- Project constraints checked: AC-1.1/1.2 frame-center accuracy; AC-2.2 reprojection error <2.5 px cross-domain; AC-NEW-4 covariance honesty (P(error >500 m) <0.1 %); AC-4.1 latency.
- Evidence: `02_fact_cards/C4_pose_estimation.md`; Sources #82–#87.
- Disqualifiers: none in selected candidates; OpenGV NOASSERTION gated as `Selected with runtime gate` per Step 7.5.3 carve-out for license-clearance.
- Restrictions × Candidate-Modes sub-matrix: see [`../00_research/06_component_fit_matrix/C4_pose_estimation.md`](../00_research/06_component_fit_matrix/C4_pose_estimation.md).
- API capability gates: ✅ MVE saved.
### Component: C5 — State estimator / sensor fusion
| Solution | Tools | Pinned Mode/Config | Advantages | Limitations | Requirements | Security | Cost | API Capability Evidence | Fit |
|----------|-------|--------------------|------------|-------------|--------------|----------|------|-------------------------|-----|
| **Manual ESKF (Solà 2017)** (mandatory simple-baseline) | NumPy/SciPy project-side implementation | Quaternion-correct ESKF on SO(3); analytic Jacobian per Solà §6; ~5-15 ms per update on Jetson CPU | Trivial dependency footprint (~kilobytes of code); fastest C5 candidate; native 6×6 covariance via analytic Jacobian propagation | No look-back refinement (forward-time-only Kalman update); D-C5-2 long-cruise observability strategy required | NumPy 1.x + SciPy + Solà 2017 paper as canonical reference | Public-domain canonical equations + project Apache-2.0 implementation | ~1-2 wk D-C5-1 = (b) re-implement from paper directly | MVE: see [`../00_research/02_fact_cards/C5_state_estimator.md`](../00_research/02_fact_cards/C5_state_estimator.md); docs: Sources #88+#89 | **Selected (mandatory simple-baseline)** — always-running fallback |
| **GTSAM iSAM2 + CombinedImuFactor + smart factors + Marginals + IncrementalFixedLagSmoother** (modern-competitive-lead-factor-graph) | GTSAM Python; borglab/gtsam | iSAM2 incremental smoothing + `CombinedImuFactor` 6-key per-keyframe-pair factor with bias evolution + `BetweenFactorPose3` + `GenericProjectionFactorCal3DS2` per D-C5-5 = (c) `PriorFactorPose3` only + `gtsam_unstable.IncrementalFixedLagSmoother` K=10-20 keyframes per D-C5-3 | NATIVE 6×6 posterior covariance via `Marginals`; NATIVE AC-4.5 look-back refinement; couples C4 + C5 via shared GTSAM substrate per D-C5-5 = (c) | GTSAM ~50-200 MB footprint; per-update latency ~5-100 ms depending on factor density (D-C5-5 = (c) gives ~2-5 ms) | GTSAM Python; daily-active maintenance | BSD-3-Clause clean | ~2-3 wk full factor-graph design | MVE: see fact card; docs: Sources #90+#91 | **Selected (modern-competitive-lead-factor-graph + recommended primary path)** — couples NATIVELY with C4 GTSAM Marginals via D-C5-5 = (c) |
**Exact-fit evidence**:
- Project constraints checked: AC-1.3 cumulative drift; AC-1.4 95% covariance ellipse + source label; AC-3.5 visual blackout + spoofed GPS dead-reckon; AC-4.1 + AC-4.5 latency + look-back refinement; AC-NEW-4 covariance honesty; AC-NEW-8 visual blackout failsafe.
- Evidence: `02_fact_cards/C5_state_estimator.md`; Sources #88–#91.
- Disqualifiers: D-C5-1 reference-implementation-license-verification gates `ludvigls/ESKF` and `cggos/imu_x_fusion` (mitigation = D-C5-1 = (b) re-implement from canonical Solà 2017 paper).
- Restrictions × Candidate-Modes sub-matrix: see [`../00_research/06_component_fit_matrix/C5_state_estimator.md`](../00_research/06_component_fit_matrix/C5_state_estimator.md).
- API capability gates: ✅ MVE saved.
### Component: C6 — Tile cache + spatial index
| Solution | Tools | Pinned Mode/Config | Advantages | Limitations | Requirements | Security | Cost | API Capability Evidence | Fit |
|----------|-------|--------------------|------------|-------------|--------------|----------|------|-------------------------|-----|
| **Mirror-of-suite-`satellite-provider` pattern** (recommended primary) | PostgreSQL btree composite + bytea + FAISS HNSW + filesystem | btree composite index on `(tile_zoom, tile_x, tile_y, version)` for slippy-map spatial-grid range queries; `bytea` descriptor blobs (halfvec per D-C6-1); `IndexHNSWFlat(d, M=32)` per D-C6-2 loaded at takeoff via `faiss.read_index(path, IO_FLAG_MMAP_IFC)`; tile storage at `./tiles/{zoom}/{x}/{y}.{image_type}` slippy-map convention | Mirrors verified-existing parent-suite pattern (Source #92 filesystem read); ~6-54 ms per cache hit within AC-4.1; ~700 MB-1.5 GB total memory within AC-4.2; trivial dependency footprint (no Postgres extensions) | Halfvec descriptor storage requires app-side conversion; sector classification heuristic deferred to Plan-phase | PostgreSQL 16 + Dapper or psycopg2 + FAISS Python | PostgreSQL License + MIT clean | ~3-5 days mirror integration | MVE: see [`../00_research/02_fact_cards/C6_tile_cache_spatial_index.md`](../00_research/02_fact_cards/C6_tile_cache_spatial_index.md); docs: Sources #92+#96+#97+#98 | **Selected (recommended primary)** — leverages existing verified-suite pattern |
| **PostGIS + pgvector** (deferred secondary) | PostgreSQL + PostGIS 3.4 + pgvector 0.7+ | GiST on `geography(POINT,4326)` with KNN distance ordering (`<->`); pgvector HNSW for descriptor ANN; same filesystem tile storage | Native KNN + radius queries; combined-SQL capabilities | 5-10× slower geographic lookup at 3 Hz query rate per Sources #93 + #97; PostGIS GPL-2.0-or-later (CONTINGENT REJECT under D-C1-1 = (b)); +50-100 MB Jetson memory + 50-200 MB disk install; D-C6-5 Jetson PostGIS+pgvector co-installation Plan-phase verification gate | PostgreSQL 16 + PostGIS 3.4 + pgvector 0.7+ | GPL-2.0-or-later via PostGIS contingent | ~1-2 wk + Plan-phase Jetson MVE | MVE: see fact card; docs: Sources #94+#95 | **Deferred secondary** — comparative-improvement verdict does NOT clear user's "significant-improvement-only" bar |
**Exact-fit evidence**:
- Project constraints checked: AC-3.3 disconnected-segment retrieval; AC-4.1 latency; AC-4.2 memory; AC-8.1 cache-interface resolution; AC-8.2 freshness; AC-8.3 cache budget; AC-8.6 satellite-anchor relocalization robustness.
- Evidence: `02_fact_cards/C6_tile_cache_spatial_index.md`; Sources #92–#98.
- Disqualifiers: PostGIS GPL-2.0-or-later contingent on D-C1-1 = (a) license track.
- Restrictions × Candidate-Modes sub-matrix: see [`../00_research/06_component_fit_matrix/C6_tile_cache_spatial_index.md`](../00_research/06_component_fit_matrix/C6_tile_cache_spatial_index.md).
- API capability gates: ✅ MVE saved for selected primary; deferred secondary has API capability evidence saved but is not active.
### Component: C7 — On-Jetson inference runtime
| Solution | Tools | Pinned Mode/Config | Advantages | Limitations | Requirements | Security | Cost | API Capability Evidence | Fit |
|----------|-------|--------------------|------------|-------------|--------------|----------|------|-------------------------|-----|
| **TensorRT native** (recommended primary) | TensorRT 10.3 bundled with JetPack 6.2 | `IInt8EntropyCalibrator2` + `BuilderFlag.FP16+INT8` mixed-precision per D-C7-2 = (b) per-family ladder per D-C7-6; engines built directly on Jetson Orin Nano Super SM 87 per D-C7-7 = (c); `config.max_workspace_size = 1 << 30` (1 GB) per D-C7-8 | Apache-2.0 in TRT 10.x; ships with JetPack so zero-effort install; lowest-latency primary path; 2-3× speedup at INT8 vs FP16 per Source #102 | Engines hardware-tied to SM 87 (Source #105) — must be built per-target via D-C10-5..D-C10-8 orchestration | JetPack 6.2 + CUDA 12.6 + cuDNN 9.3 + TRT 10.3 | Apache-2.0 throughout | ~1 wk first-model + ~1 day each subsequent | MVE: see [`../00_research/02_fact_cards/C7_inference_runtime.md`](../00_research/02_fact_cards/C7_inference_runtime.md); docs: Sources #99+#104+#105 | **Selected (recommended primary)** — lowest-latency runtime path |
| **ONNX Runtime + TensorRT EP** (modern-competitive-lead-cross-architecture-portability) | 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 (D-C7-3 mitigation = mirror Jetson AI Lab wheel index); `numpy<2.0.0` pin per D-C7-4 | Jetson AI Lab community wheels | MIT throughout | ~1 wk Jetson AI Lab wheel mgmt | MVE: see fact card; docs: Sources #100+#103 | **Selected (modern-competitive-lead-cross-architecture-portability)** — secondary for replay/SITL only |
| **Pure PyTorch FP16** (mandatory simple-baseline + reference-correctness oracle) | torch.amp.autocast + model.half() + Jetson AI Lab PyTorch 2.5 ARM64 wheel | FP16 across all models; no quantization | BSD-3-Clause; 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 = (a) PyTorch 2.5 + torchvision 0.20 | Jetson AI Lab PyTorch 2.5 wheel | BSD-3-Clause throughout | ~3-5 days base | MVE: see fact card; docs: Source #101 | **Selected (mandatory simple-baseline)** — accuracy-validation oracle only, not runtime path |
**Exact-fit evidence**:
- Project constraints checked: AC-4.1 latency; AC-4.2 memory; AC-NEW-3 INT8 calibration cache provenance for FDR; AC-NEW-5 thermal envelope (Jetson runs at 25 W TDP).
- Evidence: `02_fact_cards/C7_inference_runtime.md`; Sources #99–#105.
- Disqualifiers: Triton/DeepStream/CUDA-Python custom kernels considered-and-rejected (server/video-pipeline class, out-of-budget for embedded 8 h mission).
- Restrictions × Candidate-Modes sub-matrix: see [`../00_research/06_component_fit_matrix/C7_inference_runtime.md`](../00_research/06_component_fit_matrix/C7_inference_runtime.md).
- API capability gates: ✅ MVE saved for all 3 candidates per per-family roles.
### Component: C8 — MAVLink / MSP2 FC adapter
| Solution | Tools | Pinned Mode/Config | Advantages | Limitations | Requirements | Security | Cost | API Capability Evidence | Fit |
|----------|-------|--------------------|------------|-------------|--------------|----------|------|-------------------------|-----|
| **pymavlink → MAVLink `GPS_INPUT`** (recommended-primary for ArduPilot Plane) | ardupilot/pymavlink | `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)` 5 Hz periodic per D-C8-5 over UART/USB/UDP per D-C8-1; FC-side `GPS1_TYPE=14` MAVLink + `EK3_SRC1_POSXY=3` GPS source-set; per-FC unit conversion `horiz_accuracy` (m) per D-C8-8 = (b) | Cooperative-path; FC-side ingestion via `AP_GPS_MAV` (verified Source #4); LGPL-3.0 linkable from Apache-2.0 app per LGPL §6 (D-C8-3 mitigation) | LGPL-3.0 license-posture verification (D-C8-3 mitigation = bundle unmodified) | pymavlink + ArduPilot Plane firmware (any) | LGPL-3.0 linkable | ~3-5 days | MVE: see [`../00_research/02_fact_cards/C8_fc_adapter.md`](../00_research/02_fact_cards/C8_fc_adapter.md); docs: Sources #106+#107 | **Selected (recommended-primary)** for ArduPilot Plane |
| **MSP2_SENSOR_GPS via Python MSP V2** (recommended-primary for iNav) | YAMSPy + INAV-Toolkit `msp_v2_encode` | `MSP2_SENSOR_GPS` (id 7939 / 0x1F03) 36-byte payload at 5 Hz periodic per D-C8-5; `mspGPSReceiveNewData()` direct passthrough on iNav side; per-FC unit conversion `hPosAccuracy` (mm) per D-C8-8 = (b) | YAMSPy + INAV-Toolkit MIT throughout; covariance fields aligned (`hPosAccuracy`/`vPosAccuracy`/`hVelAccuracy`); `USE_GPS_PROTO_MSP` enabled by default in iNav target/common.h | D-C8-4 implementation choice gate (YAMSPy primary + thin custom encoder fallback) | YAMSPy or INAV-Toolkit; iNav firmware 8.0+ | MIT throughout | ~3-5 days | MVE: see fact card; docs: Sources #111+#112+#113 | **Selected (recommended-primary)** for iNav |
| **UBX impersonation via pyubx2 NAV-PVT** (deferred secondary for iNav) | semuconsulting/pyubx2 | NAV-PVT periodic + NAV-VER startup + CFG-MSG/CFG-RATE ACK; iNav-side `gpsMapFixType()` validation gate requires `flags & 0x01 = 1` AND `fixType ∈ {2,3}` per Source #110 | BSD-3-Clause clean; richer protocol surface | Forgery posture; D-C8-7 AC-NEW-7 audit-trail verification gate | pyubx2; iNav firmware (any) | BSD-3-Clause | ~1-2 wk + audit-trail design | MVE: see fact card; docs: Sources #108+#109+#110 | **Deferred secondary** — comparative-improvement verdict does NOT clear user's "significant-improvement-only" bar over MSP2 |
**Exact-fit evidence**:
- Project constraints checked: AC-4.3 per-FC external-positioning interface; AC-NEW-2 spoofing-promotion latency; AC-NEW-4 covariance honesty (per-FC unit conversion); AC-NEW-7 forgery posture for UBX path.
- Evidence: `02_fact_cards/C8_fc_adapter.md`; Sources #106–#113.
- Disqualifiers: PX4 explicitly out of scope per `restrictions.md`; pymavlink LGPL-3.0 mitigated via bundle-unmodified pattern (D-C8-3).
- Restrictions × Candidate-Modes sub-matrix: see [`../00_research/06_component_fit_matrix/C8_fc_adapter.md`](../00_research/06_component_fit_matrix/C8_fc_adapter.md).
- API capability gates: ✅ MVE saved for all 3 candidates.
### Component: C10 — Pre-flight cache provisioning + sector classification + freshness pipeline
| Solution | Tools | Pinned Mode/Config | Advantages | Limitations | Requirements | Security | Cost | API Capability Evidence | Fit |
|----------|-------|--------------------|------------|-------------|--------------|----------|------|-------------------------|-----|
| **D-C6-3 confirmation: descriptor-cache rebuild trigger pipeline** | FAISS Python API + python-atomicwrites + SHA-256 content-hash | Manifest-hash-driven rebuild trigger per D-C10-1 = (b); `python-atomicwrites` write-temp + `fsync` + atomic rename + parent-dir fsync per D-C10-2 = (b); SHA-256 content-hash gate at takeoff + reject + STATUSTEXT + refuse takeoff if mismatch per D-C10-3 = (b); mmap with `madvise(MADV_WILLNEED)` pre-fault per D-C10-4 = (b) | FAISS MIT + atomicwrites MIT throughout; idempotent + crash-safe + AC-NEW-7-compliant; minimal abstraction surface | FAISS warns "no internal integrity check, expects validated input" — MITIGATED by content-hash gate at takeoff | FAISS Python + python-atomicwrites + SHA-256 stdlib | MIT throughout | ~1 wk orchestration wrapper | MVE: see [`../00_research/02_fact_cards/C10_preflight_provisioning.md`](../00_research/02_fact_cards/C10_preflight_provisioning.md); docs: Sources #114+#115+#116 | **Selected** — closes D-C6-3 cross-component gate |
| **D-C7-7 confirmation: TensorRT engine-build pipeline** | Polygraphy CLI + trtexec + direct `IBuilderConfig` Python API | Hybrid orchestration per D-C10-5 = (d): Polygraphy CLI primary for INT8-calibrating builds (`polygraphy convert --int8 --fp16 --data-loader-script ./calib_data_loader.py --calibration-cache <path> -o <engine>`) + trtexec for cache-reuse fast rebuilds + direct `IBuilderConfig` Python API escape hatch for unusual models (LightGlue dynamic shapes); calibration cache reuse keyed by `SHA-256(calib_corpus)` per D-C10-6; self-describing filename `<model>_sm87_jp62_trt103_<precision>.engine` per D-C10-7; reference Jetson at HQ + deployed-Jetson-copy-to-archive on first successful local build per D-C10-8 | Polygraphy + TRT 10.x Apache-2.0 throughout; calibration-cache reuse keeps subsequent rebuilds <30 sec; production-mature NVIDIA-blessed orchestration | `trtexec --int8` without `--calib` random-data-fallback caveat — MITIGATED by project-side wrapper enforcing `--calib=<existing_cache>` non-empty as precondition | TensorRT 10.3 + Polygraphy + JetPack 6.2 | Apache-2.0 throughout | ~1 wk first-model + ~1 day each subsequent | MVE: see fact card; docs: Sources #117+#118+#119+#120+#121 | **Selected** — closes D-C7-7 cross-component gate |
**Exact-fit evidence**:
- Project constraints checked: AC-NEW-7 cache-poisoning safety budget (descriptor cache + TensorRT engine path); AC-8.3 cache budget; AC-NEW-1 cold-start TTFF (takeoff load <5 s); restrictions.md rebuild-while-not-flying constraint.
- Evidence: `02_fact_cards/C10_preflight_provisioning.md`; Sources #114–#121.
- Disqualifiers: none — both candidates Apache-2.0/MIT clean.
- Restrictions × Candidate-Modes sub-matrix: see [`../00_research/06_component_fit_matrix/C10_preflight_provisioning.md`](../00_research/06_component_fit_matrix/C10_preflight_provisioning.md).
- API capability gates: ✅ MVE saved for both sub-areas.
### Out-of-research-scope items (deferred to Plan-phase)
Per the C10 scope restructure 2026-05-08 (`c10_scope=C` cross-coupling minimal), the following are deferred to Plan-phase as `operator tooling design`:
- Operator-side CLI/desktop tool design (Plan-phase architect + UX)
- Sector classification (active-conflict vs stable rear) heuristics + interface (Plan-phase architect + operations team)
- Tile age-stamping schema beyond restrictions.md mandate (Plan-phase architect)
- Freshness pipeline workflow (Plan-phase architect + operations team)
Their cross-coupling with the runtime architecture is mediated entirely by the descriptor-cache file (D-C6-3 closure) and the TensorRT engine cache file (D-C7-7 closure) — both pinned by C10 batch 1 confirmations.
---
## Testing Strategy
> **Note**: full test specifications are produced by the Test Spec skill (greenfield Step 5). What follows is the research-level test envelope, named so the Test Spec skill can elaborate against it.
### Integration / Functional Tests
- **IT-1 — Pipeline smoke**: feed `_docs/00_problem/input_data/flight_derkachi/` (cropped nadir flight footage + synchronized `SCALED_IMU2` + `GLOBAL_POSITION_INT`) into the full C1+C2+C3+C4+C5+C8 pipeline; assert that the emitted `GPS_INPUT` (ArduPilot SITL) and `MSP2_SENSOR_GPS` (iNav SITL) frames stay within AC-1.1/1.2 frame-center-accuracy bounds vs the tlog GPS path.
- **IT-2 — Cold-boot TTFF**: cold-boot the companion 50× with a simulated FC pose; measure boot → first valid emitted external-position MAVLink frame; pass = 95th percentile <30 s per AC-NEW-1.
- **IT-3 — Spoofing-promotion latency**: SITL on each supported FC (ArduPilot Plane + iNav, production param sets); inject false GPS; measure spoof onset → companion estimate becoming primary FC source via D-C8-2 = (b) `MAV_CMD_SET_EKF_SOURCE_SET` companion-driven switch; pass = 95th percentile <3 s on both per AC-NEW-2.
- **IT-4 — Sharp-turn recovery**: synthetic UAV trajectory with ±20° bank turns + <5% inter-frame overlap; assert C2/C3 satellite-anchor recovery within 1-2 frames per AC-3.2 + AC-3.3.
- **IT-5 — Visual blackout + GPS spoofing degraded mode**: 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 AC-NEW-8 thresholds (>100 m → "2D fix or worse"; >500 m or >30 s → "no fix" + `VISUAL_BLACKOUT_FAILSAFE` STATUSTEXT), recovery only via trusted anchor or 10-s GPS-health + visual-consistency gate.
- **IT-6 — Stale tile rejection (AC-NEW-6)**: inject synthetic-age tiles into C6 cache; verify rejection or downgrade-to-non-`satellite_anchored` per AC-8.2 freshness threshold.
- **IT-7 — Cache-poisoning verification (AC-NEW-7)**: tamper with `/var/lib/onboard/cache/faiss/v_2048_M32.index` post-write but pre-takeoff; verify D-C10-3 SHA-256 content-hash gate triggers reject + STATUSTEXT + refuse takeoff.
- **IT-8 — Pre-flight cache rebuild idempotence**: invoke C10 pre-flight provisioning twice consecutively without input changes; verify D-C10-1 manifest-hash-driven trigger correctly skips rebuild on second invocation; verify atomic-write integrity holds across simulated power-loss mid-rebuild.
- **IT-9 — TensorRT engine cache reuse**: invoke C10 pre-flight provisioning with same model + same calibration corpus twice; verify D-C10-6 calibration-cache reuse triggers <30 sec rebuild on second invocation; verify D-C10-7 self-describing filename schema correctly identifies SM/JP/TRT/precision tuple.
- **IT-10 — AC-NEW-4 covariance-honesty cross-FC**: verify D-C8-8 = (b) per-FC unit conversion correctly extracts 2×2 horizontal sub-matrix from C5 GTSAM `Marginals.marginalCovariance`, computes 95% confidence ellipse semi-major axis `sqrt(2.0 * 5.991 * λ_max)`, emits as `horiz_accuracy` (m) for ArduPilot AND `hPosAccuracy` (mm) for iNav with mathematically equivalent values.
### Non-Functional Tests
- **NFT-1 — End-to-end latency p95 (AC-4.1)**: 8 h synthetic load (3 Hz nav frames replayed); measure end-to-end latency distribution; pass = 95th percentile <400 ms; up to ~10% frames may drop under sustained load per AC-4.1.
- **NFT-2 — Memory cap (AC-4.2)**: same 8 h load; assert peak shared CPU+GPU memory <8 GB per AC-4.2.
- **NFT-3 — Thermal envelope (AC-NEW-5)**: hot-soak 25 W @ +50 °C for 8 h; assert no Jetson thermal throttling. Cold-soak −20 °C cold-start within AC-NEW-1 30 s p95 budget.
- **NFT-4 — False-position safety budget (AC-NEW-4)**: Monte Carlo over public aerial-localization dataset (e.g., AerialVL S03) + own recorded flights; report error CDF; pass = `P(>500 m) <0.1 %` AND `P(>1 km) <0.01 %` across ≥100 flights.
- **NFT-5 — Cache-poisoning safety budget (AC-NEW-7)**: multi-flight Monte Carlo replay over public datasets + own flights with synthetic over-confidence injection (deflate covariance ×1.5–3); assert `P(geo-misalign >30 m) <1 %` AND `P(>100 m) <0.1 %` across ≥100 flights. Independently exercise the Suite Sat Service-side voting contract (out of onboard scope but acknowledged as cross-component).
- **NFT-6 — FDR storage cap (AC-NEW-3)**: 8 h synthetic load; assert FDR ≤64 GB; verify no payload class silently dropped without a logged rollover.
- **NFT-7 — License posture verification**: SBOM dump of the deployed companion; verify D-C1-1 license-track is honored (no GPL-3.0 candidate loaded if D-C1-1 = (b); pymavlink LGPL-3.0 bundled-unmodified per D-C8-3); verify Magic Leap noncommercial canonical SP weights are NOT loaded; verify all selected candidates' LICENSE files are bundled in `LICENSE/`.
---
## References
> Full per-source descriptions in `_docs/00_research/01_source_registry/` (organized by category file).
### SQ6 — ArduPilot Plane vs iNav external positioning
Sources #1–#24. See [`SQ6_external_positioning.md`](../00_research/01_source_registry/SQ6_external_positioning.md).
### SQ1 — Existing GPS-denied UAV systems
Sources #25–#37. See [`SQ1_existing_systems.md`](../00_research/01_source_registry/SQ1_existing_systems.md).
### SQ2 — Canonical pipeline decomposition
Sources #38–#42. See [`SQ2_canonical_pipeline.md`](../00_research/01_source_registry/SQ2_canonical_pipeline.md).
### C1 — VIO candidates
Sources #43–#56. See [`C1_vio.md`](../00_research/01_source_registry/C1_vio.md).
### C2 — VPR candidates
Sources #57–#68. See [`C2_vpr.md`](../00_research/01_source_registry/C2_vpr.md).
### C3 — Matcher candidates
Sources #69–#81. See [`C3_matchers.md`](../00_research/01_source_registry/C3_matchers.md).
### C4 — Pose estimation candidates
Sources #82–#87. See [`C4_pose_estimation.md`](../00_research/01_source_registry/C4_pose_estimation.md).
### C5 — State estimator / sensor fusion candidates
Sources #88–#91. See [`C5_state_estimator.md`](../00_research/01_source_registry/C5_state_estimator.md).
### C6 — Tile cache + spatial index candidates
Sources #92–#98. See [`C6_tile_cache_spatial_index.md`](../00_research/01_source_registry/C6_tile_cache_spatial_index.md).
### C7 — On-Jetson inference runtime candidates
Sources #99–#105. See [`C7_inference_runtime.md`](../00_research/01_source_registry/C7_inference_runtime.md).
### C8 — MAVLink / MSP2 FC adapter candidates
Sources #106–#113. See [`C8_fc_adapter.md`](../00_research/01_source_registry/C8_fc_adapter.md).
### C10 — Pre-flight cache provisioning candidates
Sources #114–#121. See [`C10_preflight_provisioning.md`](../00_research/01_source_registry/C10_preflight_provisioning.md).
---
## Open decisions for Plan-phase (D-Cx-y registry)
The 27 Plan-phase-architect-owned decisions and 8 cross-component-owner decisions raised across all components are catalogued in [`../00_research/06_component_fit_matrix/99_cross_component_gates.md`](../00_research/06_component_fit_matrix/99_cross_component_gates.md). The most architecturally significant **user-decision** gates are:
- **D-C1-1 license-track posture** (User + Plan-phase architect). Recommendation: D-C1-1 = (c) both tracks open; preserves modular swap pathway documented in Comparison Framework Dimension 8.
- **D-C2-1 VPR canonical-weights vs aerial-retrain vs aerial-community-checkpoint** (User + Plan-phase architect). Recommendation: aerial-retrain on real UAV nadir flight footage corpus per D-C7-1 closure (cost ~1-2 weeks per retrained candidate).
- **D-C3-1 SuperPoint-replacement-strategy** (User + Plan-phase architect + license-posture decision-maker). Recommendation: D-C3-1 = (a) DISK+LightGlue (Apache-2.0 throughout + +7.99 absolute AUC@5° lift over canonical SP+LightGlue per LightGlue paper Tab 6).
- **D-C2-11 (CONDITIONAL) MegaLoc successor evaluation** (User + Plan-phase architect). Recommendation: defer to post-research session — EigenPlaces closes the mandatory pre-screen at the documentary-required floor; MegaLoc's Plan-phase relevance depends on D-C1-1 + Jetson MVE results.
---
## Related Artifacts
- Tech stack evaluation (`tech_stack.md`): NOT PRODUCED in this Mode A run. Recommendation set is embedded in the per-component candidate tables above. Full extraction into `tech_stack.md` is a low-cost task if the user requests it before Plan-phase.
- Security analysis (`security_analysis.md`): NOT PRODUCED in this Mode A run. AC-NEW-7 cache-poisoning safety + AC-NEW-2 spoofing-promotion + AC-NEW-8 visual blackout failsafe + AC-NEW-4 covariance honesty are addressed component-by-component above and cross-referenced in [`../00_research/05_validation_log.md`](../00_research/05_validation_log.md). Full extraction into `security_analysis.md` is a low-cost task if the user requests it before Plan-phase.
- AC assessment (`_docs/00_research/00_ac_assessment.md`): NOT PRODUCED as standalone artifact in this Mode A run; per-AC binding evidence distributed across per-component fact cards + Restrictions × Candidate-Modes sub-matrix sections.
Reference in New Issue View Git Blame Copy Permalink