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_docs/00_research/00_ac_assessment.md
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# Acceptance Criteria Assessment
## Scope
- **Mode**: Mode A Phase 1 — acceptance criteria and restrictions assessment.
- **Problem boundary**: fixed-wing UAV, downward fixed navigation camera, high-rate IMU over MAVLink, GPS may be denied or spoofed, offline satellite tile cache, eastern/southern Ukraine operating region, onboard Jetson Orin Nano Super.
- **Novelty sensitivity**: High for onboard AI/VPR, Jetson/TensorRT, and ArduPilot EKF integration; Medium for photogrammetry/GSD and slippy-map math; Low for high-level VO/INS principles.
- **Input data observed**: 60 nadir-like agricultural/steppe frames with sparse landmarks, ground-truth frame-center coordinates, two Google Maps reference screenshots, no real IMU trace.
## Acceptance Criteria
| Criterion | Our Values | Researched Values | Cost/Timeline Impact | Status |
|-----------|------------|-------------------|----------------------|--------|
| AC-1.1 / AC-1.2 frame-center accuracy | 80% <50 m, 50% <20 m | Recent UAV GNSS-denied visual localization work reports roughly 15-20 m mean localization error in favorable datasets, while fixed-wing satellite-aided VO demonstrates drift reduction over >17 km rather than a universal meter-level guarantee. The thresholds are plausible if satellite anchors succeed regularly, but they require dataset-specific validation. | Keep, but budget a full evaluation harness and Monte Carlo replay early. | Keep |
| Expected-results threshold for 20 m accuracy | AC says 50%; `expected_results/results_report.md` says 60% | This is an internal contradiction, not a research issue. Tests will enforce the stricter 60% if left as-is. | Low effort to correct, high risk if unnoticed because it changes pass/fail. | Needs decision |
| AC-1.3 VO drift between anchors | <100 m VO-only, <50 m IMU-fused | Visual-inertial odometry drift below ~1% of distance is realistic in some outdoor tests; at 60 km/h and 3 Hz, anchor cadence and turn handling dominate. The criterion is feasible if satellite relocalization prevents long VO-only intervals. | Keep, but require explicit anchor-age limits in tests. | Keep |
| AC-1.4 confidence score | 95% covariance ellipse + source label | MAVLink `GPS_INPUT` supports horizontal/vertical accuracy fields; producing calibrated covariance is feasible but must be statistically validated against false-position budgets. | Medium effort; requires calibration and reliability testing, not just API plumbing. | Keep |
| AC-2.1 registration rate | >95% normal segments | Plausible only under the definition already scoped: nadir, daylight, season-matched, >=40% overlap. Sparse agricultural frames and active-conflict scene changes make this too optimistic outside that scope. | Keep the scoped definition; add separate degraded-condition metrics later. | Keep |
| AC-2.2 reprojection error | <1.0 px VO, <2.5 px UAV-satellite | VO MRE <1 px is plausible after calibration. Cross-domain UAV-satellite <2.5 px is aggressive because appearance, season, scale, and orthorectification errors dominate. | Keep as a stretch or lab-gated target; do not make it the only production acceptance signal. | Modify |
| AC-3.1 outliers / AC-3.2 sharp turns / AC-3.3 disconnected segments | 350 m outliers; <5% overlap turns; >=3 disconnected segments | These are operationally important and realistic as failure modes. They cannot be solved by VO alone; they require VPR/global retrieval + EKF gating. | High implementation risk; keep because they drive the right architecture. | Keep |
| AC-3.4 relocalization request | >=3 failed frames and >=2 s | At 3 fps, this means the frame-count trigger can fire around 1 s but the time trigger waits 2 s. The combined trigger is coherent. | Low implementation cost; important for operator workflow. | Keep |
| AC-4.1 latency | <400 ms p95 capture-to-FC output, <=10% frame drop | Jetson Orin Nano Super has official 67 TOPS sparse / 8 GB / 25 W mode. DINOv2-base TensorRT examples can run at high FPS, but DINOv2-S GitHub issue data shows ~22-23 ms GPU compute and limited INT8 gain; SuperPoint/LightGlue data on Orin Nano is thin. End-to-end 400 ms is plausible only with conditional VPR and heavy offline preprocessing. | High risk; needs prototype benchmark before implementation decomposition. | Keep with benchmark gate |
| AC-4.2 memory | <8 GB shared LPDDR5 | Feasible only if model set is small, descriptors are tiled/indexed, TensorRT engines are prebuilt, and raw frames are not retained. | Medium risk; memory profiling must be an acceptance test. | Keep |
| AC-4.3 MAVLink output | v1 GPS_INPUT only; ODOMETRY disabled | ArduPilot/MAVProxy documents `GPS1_TYPE=14` for MAVLink GPS input. MAVLink says `GPS_INPUT` is raw sensor input, not global position estimate, so covariance/accuracy fields matter. ArduPilot issue #30076 shows external-nav/GPS fusion risk was real and is now marked closed; v1 GPS_INPUT-only remains conservative, but ODOMETRY should be re-evaluated against the exact ArduPilot release before v1.1. | Keep v1 GPS_INPUT-only; add version-pinned SITL gate before enabling ODOMETRY. | Keep |
| AC-5 startup/failsafe | TTFF <30 s; fail if no estimate >3 s | Cold-start <30 s is plausible only if TensorRT engines and tile indexes are loaded without first-run compilation. >3 s failover matches the 3 fps / sharp-turn recovery intent. | Medium effort; requires boot profiling and watchdog tests. | Keep |
| AC-6 QGC telemetry | 1-2 Hz downsampled summary, commands via MAVLink | Feasible and appropriately scoped; keeps high-rate data local. | Low/medium depending on command dialect choice. | Keep |
| AC-7 object localization | Frame-center-equivalent in level flight; publish bank/pitch bound outside level flight | This is realistic because AI-camera bank/pitch is unavailable. The bound `altitude * sin(angle)` correctly exposes the limitation instead of hiding it. | Keep; API must always return accuracy bound. | Keep |
| AC-8.1 imagery resolution | >=0.5 m/px, ideal 0.3 m/px | Commercial 30 cm imagery is realistic through providers such as Airbus Pléiades Neo and Vantor/Maxar-class constellations, but upstream availability/freshness in active-conflict areas remains a service risk. | Keep; dependency belongs to Suite Satellite Service SLA. | Keep |
| AC-8.2 / AC-NEW-6 freshness | <6 months active sectors, <12 months stable sectors | Operationally justified. It will reduce false positives but increases mission planning/sourcing burden. | Medium/high cost delegated to Satellite Service and cache metadata. | Keep |
| AC-8.3 preprocessing | Offline cache with descriptors | Correct architectural choice for onboard latency and no in-flight network dependency. | Medium offline compute/storage cost. | Keep |
| AC-8.4 / AC-8.5 tile write-back and no raw frame storage | Persist generated tiles, not raw frames | Good storage control and privacy/security posture. Requires tile quality gates to prevent poisoning. | Medium/high because write-back quality scoring is non-trivial. | Keep |
| AC-8.6 VPR chunks | 600-800 m chunks, 40-50% overlap, conditional VPR | Fits the latency evidence: online VPR should be event-triggered, not per-frame. Multi-scale chunks are appropriate for active-conflict scene changes. | Keep; index size and load time need measurement. | Keep |
| AC-NEW-3 FDR | <=64 GB / flight | Feasible because raw frames are excluded. Must include rollover logging so no payload class disappears silently. | Low/medium. | Keep |
| AC-NEW-4 false-position budget | P(error >500 m) <0.1%, P(error >1 km) <0.01% per flight | This is the right safety metric, but it cannot be proven from unit tests alone. It requires calibrated covariance, outlier rejection, and Monte Carlo over representative datasets. | High validation cost; keep as safety gate. | Keep |
| AC-NEW-5 environmental envelope | -20 C to +50 C, 25 W for 8 h, no throttling | DO-160G-style environmental testing is a recognized airborne-equipment pattern; applying it to a small UAV is conservative. The thermal target is realistic only with designed cooling, not a bare dev kit. | Hardware integration and chamber testing required. | Keep |
| AC-NEW-7 cache-poisoning budget | Tile misalignment >30 m <1%, >100 m <0.1% | Correct risk to control, but the thresholds depend on covariance calibration and multi-flight voting. Single-flight promotion in active sectors should remain exceptional and heavily gated. | High validation cost; keep with explicit service-side voting tests. | Keep |
## Restrictions Assessment
| Restriction | Our Values | Researched Values | Cost/Timeline Impact | Status |
|-------------|------------|-------------------|----------------------|--------|
| UAV type and camera pose | Fixed-wing, fixed downward navigation camera | This fits visual odometry and orthorectification assumptions better than gimbaled imagery. Lack of gimbal stabilization means bank/pitch compensation must come from FC attitude. | Keep; camera/FC time sync is critical. | Keep |
| Operational area | Eastern/southern Ukraine, steppe/agricultural terrain | Sample images confirm low-texture fields, tree lines, roads, sparse structures, and seasonal/appearance risk. This is a hard test for cross-view matching. | Keep; research and tests must avoid urban-only datasets. | Keep |
| Camera spec inconsistency | Problem text says ~6200x4100; restrictions say 20 MP 5472x3648; input data says 26 MP 6252x4168 at 400 m | The repo distinguishes target camera from example data, but the solution needs one authoritative v1 navigation camera/lens for GSD, intrinsics, and latency. | Needs confirmation or the plan will produce conflicting calibration tasks. | Needs decision |
| GSD target | 10-20 cm/px at <=1 km AGL | Formula `GSD = sensor width * altitude / (focal length * image width)` supports tuning GSD by lens choice. With 23.5 mm sensor width, 6252 px, 25 mm lens: ~6 cm/px at 400 m and ~15 cm/px at 1 km. | Keep; lens selection must be locked before calibration tasks. | Keep |
| Tile zoom statement | "slippy-XYZ z=20 (~30 cm/px, 512x512)" | OSM zoom table gives z20 = 0.149 m/px at equator for 256 px tiles, about 0.10 m/px at latitude 48; 512 px tile conventions can shift effective zoom. The stated "z=20 ~30 cm/px" is inconsistent unless this uses a custom provider convention. | Needs correction before storage and cache-index tasks. | Needs decision |
| Satellite provider boundary | Onboard consumes Azaion Suite Satellite Service cache only | Correct. Commercial provider contracts, tasking, licensing, and freshness are outside this build but must become Satellite Service SLAs. | Keep. | Keep |
| Hardware | Jetson Orin Nano Super, 8 GB, 25 W | Official specs support the constraint, but thermal and memory limits are tight for multi-model CV. | Keep; prototype benchmark is mandatory. | Keep |
| IMU source | High-rate IMU from FC via MAVLink | Feasible. Test gap remains because current input data has no real IMU trace. Synthetic/SITL IMU is acceptable for early tests but not final validation. | Add real or representative flight IMU capture plan. | Modify |
| Autopilot | ArduPilot only, QGroundControl only | Good scope control. ArduPilot GPS_INPUT path is documented; EKF source/fusion behavior must be version-pinned. | Keep. | Keep |
| Storage | ~10 GB persistent tile cache plus 64 GB FDR | Feasible if tile zoom/resolution is corrected and raw frames remain excluded. | Keep after zoom correction. | Keep |
| No raw photo storage | Tiles only; failure thumbnails <=0.1 Hz | Strong restriction for storage and security; compatible with FDR cap. | Keep. | Keep |
## Key Findings
1. Most existing ACs are directionally sound and should remain, especially the safety budgets, covariance reporting, freshness gates, GPS_INPUT-only v1 scope, and no-raw-frame storage policy.
2. Three items should be resolved before planning proceeds: AC-1.2's 50% vs expected-results 60% contradiction, the authoritative v1 navigation camera/lens, and the tile zoom/resolution convention.
3. The 400 ms p95 latency target is plausible only if VPR is conditional and reference descriptors are precomputed; it should be treated as a benchmark gate before implementation tasks are finalized.
4. Cross-domain reprojection error <2.5 px is an aggressive lab target. Production acceptance should rely on position error, covariance calibration, registration success, and false-position rejection, not pixel error alone.
5. The public evidence supports satellite-aided visual localization as feasible, but the project's operating region is harder than many benchmark datasets because farmland/steppe imagery has sparse stable features and active-conflict areas change quickly.
## Recommended Adjustments Before Phase 2
| Decision | Recommendation | Rationale |
|----------|----------------|-----------|
| AC-1.2 threshold mismatch | Align `expected_results/results_report.md` with AC-1.2 at 50%, unless the user intentionally wants a 60% stretch target. | Avoid hidden test/spec disagreement. |
| Navigation camera | Confirm ADTi 20MP 20L V1 + selected lens as v1 target; keep 26 MP images as sample/test data only. | Calibration, GSD, FOV, latency, and orthorectification depend on intrinsics. |
| Tile zoom | Replace "z=20 (~30 cm/px, 512x512)" with an explicit cache convention: provider pixel size, tile matrix, tile dimension, CRS, and latitude-adjusted resolution. | Prevent wrong storage estimates and matcher scale assumptions. |
| IMU validation data | Add a requirement to obtain real FC MAVLink IMU logs or approved SITL-generated IMU traces before final acceptance. | Current sample data cannot validate fusion, covariance, or latency-to-FC behavior. |
| Cross-domain MRE | Mark <2.5 px as lab/stretched diagnostic; keep position/covariance as production gate. | Pixel error does not fully capture orthorectification, map freshness, and geodetic error. |
## Sources
- ArduPilot MAVProxy GPSInput documentation, accessed 2026-04-29: `https://ardupilot.org/mavproxy/docs/modules/GPSInput.html`
- MAVLink common message spec, `GPS_INPUT`, accessed 2026-04-29: `https://mavlink.io/en/messages/common.html#GPS_INPUT`
- ArduPilot issue #30076, ExternalNav + GPS fusion behavior, accessed 2026-04-29: `https://github.com/ArduPilot/ardupilot/issues/30076`
- NVIDIA Jetson Orin Nano Super technical blog, published 2024-12-17, accessed 2026-04-29: `https://developer.nvidia.com/blog/nvidia-jetson-orin-nano-developer-kit-gets-a-super-boost`
- NVIDIA JetPack 6.2 Super Mode benchmarks, accessed 2026-04-29: `https://developer.nvidia.com/blog/nvidia-jetpack-6-2-brings-super-mode-to-nvidia-jetson-orin-nano-and-jetson-orin-nx-modules/`
- NVIDIA TensorRT issue #4348, DINOv2-S Jetson Orin TensorRT measurements, accessed 2026-04-29: `https://github.com/NVIDIA/TensorRT/issues/4348`
- MDPI Applied Sciences 2024, "Visual Odometry in GPS-Denied Zones for Fixed-Wing Unmanned Aerial Vehicle with Reduced Accumulative Error Based on Satellite Imagery", accessed 2026-04-29: `https://www.mdpi.com/2076-3417/14/16/7420`
- MDPI Sensors 2024, "A Cross-View Geo-Localization Algorithm Using UAV Image and Satellite Image", accessed 2026-04-29: `https://www.mdpi.com/1424-8220/24/12/3719`
- Airbus Pléiades Neo official imagery page, accessed 2026-04-29: `https://www.airbus.com/en/pleiades-neo-satellite-imagery`
- Vantor/Maxar WorldView Legion page, accessed 2026-04-29: `https://www.maxar.com/worldview-legion`
- OpenStreetMap Wiki zoom levels, accessed 2026-04-29: `https://wiki.openstreetmap.org/wiki/Zoom_levels`
- FAA AC 21-16G / RTCA DO-160G reference, accessed 2026-04-29: `https://www.faa.gov/documentLibrary/media/Advisory_Circular/AC_21-16G.pdf`
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# Question Decomposition
## Original Question
Design a GPS-denied onboard system for a fixed-wing UAV that estimates the WGS84 coordinate of each navigation-camera frame center, estimates object coordinates from a separate AI camera, and emits replacement GPS data to ArduPilot using only onboard sensors and a preloaded satellite imagery cache.
## Active Mode
- **Mode**: Mode B — solution assessment of `_docs/01_solution/solution_draft01.md`.
- **Question type**: Problem Diagnosis with Decision Support.
- **Rationale**: The first draft has a plausible architecture, but several component choices need stricter exact-fit checks against licensing, memory, cache-size, validation-data, and ArduPilot integration constraints before planning.
## Problem Context Summary
- Fixed-wing UAV, roughly 60 km/h cruise, 8-hour missions, up to 400 km² persistent satellite cache.
- Navigation camera is fixed and nadir-facing; v1 target is ADTi 20MP 20L V1 with a lens selected for roughly 10-20 cm/px at <=1 km AGL.
- AI camera is separate, gimbaled/zoomed, and only gimbal angle + zoom are available to the GPS-denied system.
- IMU and attitude data arrive from the flight controller over MAVLink.
- Onboard platform is Jetson Orin Nano Super, 8 GB shared memory, 25 W.
- Satellite imagery is preloaded before flight by Azaion Suite Satellite Service; no in-flight network dependency.
- Output to flight controller is v1 `GPS_INPUT` only, with `ODOMETRY` deferred until release/version-specific SITL validation.
## Research Subject Boundary
| Dimension | Boundary |
|-----------|----------|
| Population | Fixed-wing UAVs with downward navigation camera, not multicopter-only or indoor robot-only systems. |
| Geography | Eastern/southern Ukraine style steppe/agricultural terrain, sparse landmarks, active-conflict freshness risk. |
| Timeframe | Current practical open-source/commercial CV stack as of 2024-2026. |
| Level | Onboard production architecture and validation plan, not only offline academic benchmark. |
| Operating context | Real-time edge inference, GPS denied/spoofed, no in-flight satellite fetch, 8-hour thermal duty cycle. |
| Required interfaces | Nav-camera frames, FC IMU/attitude/altitude over MAVLink, offline tile cache, local API, MAVLink `GPS_INPUT`. |
| Non-functional envelope | <400 ms p95 output latency, <8 GB memory, 10 GB cache cap, 64 GB FDR cap, calibrated false-position budget. |
## Project Constraint Matrix Summary
| Constraint Area | Binding Constraint | Hard Disqualifiers |
|-----------------|-------------------|--------------------|
| Camera/VO | Fixed nadir monocular navigation camera, high-res frames, flight attitude from FC. | VO requiring stereo/depth as a mandatory input for v1. |
| Absolute localization | Must anchor to offline satellite cache and handle sparse terrain. | Pure VO/SLAM with no global relocalization. |
| Runtime | Jetson Orin Nano Super, 8 GB shared memory, 25 W. | Per-frame heavy VPR or models that exceed memory/thermal budgets. |
| Safety | Must report covariance and avoid confident false positions. | Matchers that output positions without calibrated uncertainty and outlier rejection. |
| Autopilot | ArduPilot v1, `GPS_INPUT` primary. | PX4-only or ODOMETRY-only integration for v1. |
| Storage | No raw frame persistence; tiles and FDR only. | Architectures depending on raw photo archive during normal flight. |
| Satellite cache | 0.5 m/px min, 0.3 m/px ideal; freshness metadata required. | Untimestamped/stale reference tiles treated as equally trustworthy. |
## Decomposed Sub-Questions
1. Which assumptions in `solution_draft01.md` are weak, under-evidenced, or contradicted by project constraints?
2. Does the selected local matcher have a product-safe licensing path and a realistic Jetson performance path?
3. Does the AnyLoc/DINOv2-VLAD VPR proposal fit the 8 GB memory and 10 GB cache budgets after descriptor/index sizing?
4. Is the 400 km² satellite cache budget plausible when 0.3-0.5 m/px imagery, overviews, manifests, descriptors, and generated tiles are included?
5. Are the public validation datasets sufficient, or does final acceptance require project-specific IMU/camera timing traces?
6. Does v1 `GPS_INPUT`-only ArduPilot integration remain safer than dual `GPS_INPUT` + `ODOMETRY` emission?
7. Are any selected components still only experimental and therefore blocked by the exact-fit gate?
## Chosen Perspectives
- **Implementer / Engineer**: exact sensor fit, timing, memory, calibration, and integration risks.
- **Domain expert / Academic**: public evidence for UAV-to-satellite localization and VO drift behavior.
- **Contrarian / Safety**: false positive matches, stale tiles, cache poisoning, and EKF double-fusion.
- **Operator / Field**: no in-flight network, QGC visibility, reboot recovery, and failure requests.
## Search Query Variants
| Sub-Question | Query Variants |
|--------------|----------------|
| VO/VIO | `fixed wing UAV visual odometry GPS denied satellite imagery`, `monocular IMU visual inertial odometry UAV`, `cuVSLAM monocular IMU requirements`, `ORB-SLAM3 monocular inertial license`, `VINS-Fusion monocular IMU fixed wing UAV` |
| VPR | `DINOv2 visual place recognition UAV satellite localization`, `AnyLoc DINOv2 visual place recognition license`, `SatLoc dataset GNSS denied UAV`, `NaviLoc trajectory visual localization UAV`, `DINOv2 Jetson Orin latency` |
| Local matching | `SuperPoint LightGlue TensorRT Jetson Orin`, `cross-view geo-localization UAV satellite imagery 2024`, `UAV satellite image local feature matching RANSAC homography`, `SuperPoint license LightGlue license` |
| Fusion | `error state Kalman filter visual inertial GPS denied UAV`, `MAVLink GPS_INPUT horizontal accuracy covariance`, `ArduPilot external navigation GPS double fusion`, `ArduPilot EKF3 GPS_INPUT ODOMETRY` |
| Cache | `Cloud Optimized GeoTIFF offline raster cache`, `MBTiles offline raster tile cache`, `slippy map zoom meters per pixel`, `satellite imagery 30 cm resolution official`, `Maxar Airbus 30 cm CE90` |
| API/FDR | `FastAPI OpenAPI automatic documentation`, `pymavlink GPS_INPUT send message`, `MAVProxy GPS_INPUT GPS1_TYPE 14`, `flight data recorder UAV MAVLink logs` |
| Mode B weak points | `SuperPoint pretrained weights license commercial use`, `LightGlue license extractor weights`, `AnyLoc DINOv2 VLAD descriptor dimension memory`, `COG JPEG compression satellite imagery storage estimate`, `public UAV dataset GPS IMU satellite imagery AerialVL`, `ArduPilot GPS_INPUT external navigation ODOMETRY EKF3 2026` |
| Mode B round 2 weak points | `ALIKED feature extractor license LightGlue`, `DISK local feature extractor license LightGlue`, `DeDoDe local features license TensorRT`, `OpenCV SIFT patent expired commercial use`, `real-time frame queue latency latest frame processing visual odometry` |
## Completeness Audit
| Probe | Result |
|-------|--------|
| Cost / resources | Covered through Jetson, storage, model, and satellite-service constraints. |
| Physical/legal/environmental constraints | Covered through camera, thermal, DO-160-style environment, and imagery provider boundary. |
| Dependencies and assumptions | Covered through Suite Satellite Service, ArduPilot version, TensorRT/JetPack, and model licensing. |
| Operating environment | Covered: daytime, steppe/agricultural terrain, active-conflict freshness, sparse landmarks. |
| Failure modes | Covered: stale tiles, sharp turns, VO loss, false positives, cache poisoning, reboot, EKF fusion bugs. |
| Practitioner lessons | Covered through ArduPilot issue evidence and Jetson/TensorRT deployment constraints. |
| Change over time | Covered through high novelty sensitivity for VPR models, JetPack/TensorRT, and ArduPilot EKF behavior. |
| Mode B exact-fit gaps | Covered through new checks on SuperPoint licensing, LightGlue/extractor separation, AnyLoc descriptor size, COG storage measurement, and dataset realism. |
| Planning specificity | Covered through named local-matcher candidates and a scheduler/drop-policy component for AC-4.1 / AC-4.4. |
## Timeliness Sensitivity Assessment
- **Research topic**: GPS-denied UAV visual localization using VO/IMU, VPR, cross-view matching, Jetson inference, and ArduPilot MAVLink output.
- **Sensitivity level**: High.
- **Rationale**: Hardware, TensorRT, JetPack, VPR foundation models, and ArduPilot EKF behavior change across releases; algorithmic principles are more stable.
- **Source time window**: Prefer 2024-2026 for implementation/tooling; older foundational VO/SLAM papers are acceptable only as background.
- **Priority official sources**: ArduPilot/MAVLink docs, NVIDIA Jetson/TensorRT/Isaac ROS docs, official provider imagery pages, official library repos.
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# Source Registry
## Source #1
- **Title**: ArduPilot MAVProxy GPS Input documentation
- **Link**: https://ardupilot.org/mavproxy/docs/modules/GPSInput.html
- **Tier**: L1
- **Publication Date**: n/a
- **Timeliness Status**: Currently valid as of access date
- **Version Info**: ArduPilot/MAVProxy docs accessed 2026-04-29
- **Target Audience**: ArduPilot developers/operators
- **Research Boundary Match**: Full match
- **Summary**: GPSInput forwards JSON GPS data to the flight controller and requires `GPS1_TYPE=14` for MAVLink GPS input.
- **Related Sub-question**: Flight-controller output path
## Source #2
- **Title**: MAVLink common message spec — GPS_INPUT
- **Link**: https://mavlink.io/en/messages/common.html#GPS_INPUT
- **Tier**: L1
- **Publication Date**: n/a
- **Timeliness Status**: Currently valid as of access date
- **Version Info**: MAVLink common.xml accessed 2026-04-29
- **Target Audience**: MAVLink integrators
- **Research Boundary Match**: Full match
- **Summary**: `GPS_INPUT` is a raw GPS sensor input message, not the global position estimate; all non-ignored fields must be provided.
- **Related Sub-question**: MAVLink output and covariance
## Source #3
- **Title**: ArduPilot issue #30076 — Fixing ExternalNav + GPS
- **Link**: https://github.com/ArduPilot/ardupilot/issues/30076
- **Tier**: L4
- **Publication Date**: 2025-05-15
- **Timeliness Status**: Currently relevant, but version-specific
- **Version Info**: ArduPilot 4.6 beta context; issue marked closed by 2025-12-03
- **Target Audience**: ArduPilot EKF developers
- **Research Boundary Match**: Full match
- **Summary**: Documents a real EKF3 issue where GPS and external navigation could be fused unexpectedly, motivating version-pinned SITL before enabling `ODOMETRY` alongside GPS.
- **Related Sub-question**: Safe ArduPilot integration
## Source #4
- **Title**: NVIDIA Jetson Orin Nano Developer Kit Gets a Super Boost
- **Link**: https://developer.nvidia.com/blog/nvidia-jetson-orin-nano-developer-kit-gets-a-super-boost
- **Tier**: L2
- **Publication Date**: 2024-12-17
- **Timeliness Status**: Currently valid as of access date
- **Version Info**: JetPack 6.1 Super mode
- **Target Audience**: Jetson edge AI developers
- **Research Boundary Match**: Full match
- **Summary**: Confirms Jetson Orin Nano Super 67 sparse TOPS, 8 GB LPDDR5, 102 GB/s memory bandwidth, and 25 W mode.
- **Related Sub-question**: Onboard runtime feasibility
## Source #5
- **Title**: NVIDIA JetPack 6.2 Super Mode benchmarks
- **Link**: https://developer.nvidia.com/blog/nvidia-jetpack-6-2-brings-super-mode-to-nvidia-jetson-orin-nano-and-jetson-orin-nx-modules/
- **Tier**: L2
- **Publication Date**: 2025-01
- **Timeliness Status**: Currently valid as of access date
- **Version Info**: JetPack 6.2
- **Target Audience**: Jetson AI developers
- **Research Boundary Match**: Partial overlap
- **Summary**: Provides ViT benchmark rates for Orin Nano 8 GB in Super Mode, including DINOv2-base-patch14.
- **Related Sub-question**: VPR runtime feasibility
## Source #6
- **Title**: NVIDIA Isaac ROS cuVSLAM documentation
- **Link**: https://nvidia-isaac-ros.github.io/concepts/visual_slam/cuvslam/index.html
- **Tier**: L1
- **Publication Date**: n/a
- **Timeliness Status**: Currently valid as of access date
- **Version Info**: Isaac ROS latest docs accessed 2026-04-29
- **Target Audience**: Robotics developers
- **Research Boundary Match**: Partial overlap
- **Summary**: cuVSLAM is GPU-accelerated stereo-visual-inertial SLAM and can use IMU fallback, but its primary fit is stereo, not v1 monocular nadir camera.
- **Related Sub-question**: VO/VIO candidate fit
## Source #7
- **Title**: ORB-SLAM3 repository/license
- **Link**: https://github.com/UZ-SLAMLab/ORB_SLAM3
- **Tier**: L1
- **Publication Date**: n/a
- **Timeliness Status**: Requires license review
- **Version Info**: GPLv3; release v1.0, active repo updates observed in search
- **Target Audience**: SLAM researchers/developers
- **Research Boundary Match**: Partial overlap
- **Summary**: Supports monocular-inertial SLAM but GPLv3 is a commercial/product integration risk.
- **Related Sub-question**: VO/VIO candidate fit
## Source #8
- **Title**: VINS-Fusion repository/license
- **Link**: https://github.com/HKUST-Aerial-Robotics/VINS-Fusion
- **Tier**: L1
- **Publication Date**: n/a
- **Timeliness Status**: Requires license review
- **Version Info**: GPL-3.0
- **Target Audience**: Robotics/VIO developers
- **Research Boundary Match**: Partial overlap
- **Summary**: Supports monocular + IMU visual-inertial estimation, but GPL-3.0 and ROS-centric integration make it unsuitable as a direct embedded production dependency without approval.
- **Related Sub-question**: VO/VIO candidate fit
## Source #9
- **Title**: LightGlue / SuperPoint TensorRT ecosystem
- **Link**: https://github.com/cvg/LightGlue and https://github.com/yuefanhao/SuperPoint-LightGlue-TensorRT
- **Tier**: L1/L4
- **Publication Date**: n/a
- **Timeliness Status**: Needs license and Jetson benchmark verification
- **Version Info**: LightGlue Apache-2.0; SuperPoint weights have separate MagicLeap license
- **Target Audience**: CV developers
- **Research Boundary Match**: Full match for local matching, partial for licensing
- **Summary**: LightGlue/SuperPoint is a strong local matching candidate, but SuperPoint weight licensing and Orin Nano performance must be validated before product selection.
- **Related Sub-question**: Local cross-view matcher
## Source #10
- **Title**: AnyLoc visual place recognition
- **Link**: https://github.com/AnyLoc/AnyLoc and https://arxiv.org/pdf/2308.00688
- **Tier**: L1
- **Publication Date**: 2023
- **Timeliness Status**: Currently useful, but benchmark against project data required
- **Version Info**: BSD-3-Clause, DINOv2-based VPR
- **Target Audience**: Robotics/VPR researchers
- **Research Boundary Match**: Partial overlap
- **Summary**: DINOv2 + VLAD VPR generalizes across domains and is suitable for offline descriptor generation plus event-triggered online retrieval.
- **Related Sub-question**: VPR strategy
## Source #11
- **Title**: FAISS installation and GPU package support
- **Link**: https://github.com/facebookresearch/faiss/blob/master/INSTALL.md
- **Tier**: L1
- **Publication Date**: n/a
- **Timeliness Status**: Needs direct build validation on Jetson
- **Version Info**: Search result notes GPU packages are x86-64 only; ARM64 GPU requires source build
- **Target Audience**: Vector search developers
- **Research Boundary Match**: Full match
- **Summary**: FAISS CPU is viable on ARM64; FAISS GPU on Jetson may require source build and should not be assumed for v1.
- **Related Sub-question**: VPR index runtime
## Source #12
- **Title**: Fixed-wing satellite-aided visual odometry paper
- **Link**: https://www.mdpi.com/2076-3417/14/16/7420
- **Tier**: L1
- **Publication Date**: 2024
- **Timeliness Status**: Currently relevant
- **Version Info**: Applied Sciences 14(16), 7420
- **Target Audience**: UAV navigation researchers
- **Research Boundary Match**: Full match
- **Summary**: Demonstrates fixed-wing GPS-denied visual odometry aided by satellite imagery over >17 km and >1000 m altitude, reducing accumulated error.
- **Related Sub-question**: Accuracy and architecture feasibility
## Source #13
- **Title**: Cross-view UAV/satellite geolocalization paper
- **Link**: https://www.mdpi.com/1424-8220/24/12/3719
- **Tier**: L1
- **Publication Date**: 2024
- **Timeliness Status**: Currently relevant
- **Version Info**: Sensors 24(12), 3719
- **Target Audience**: UAV visual localization researchers
- **Research Boundary Match**: Partial overlap
- **Summary**: Shows current UAV-to-satellite localization research addressing cross-source appearance and similar-scene interference.
- **Related Sub-question**: Cross-view matching risks
## Source #14
- **Title**: Airbus Pléiades Neo imagery
- **Link**: https://www.airbus.com/en/pleiades-neo-satellite-imagery
- **Tier**: L1
- **Publication Date**: n/a
- **Timeliness Status**: Currently valid as of access date
- **Version Info**: Official Airbus page
- **Target Audience**: Satellite imagery customers
- **Research Boundary Match**: Full match for imagery resolution
- **Summary**: Confirms 30 cm native imagery and 3.5 m CE90 location accuracy.
- **Related Sub-question**: Satellite cache SLA
## Source #15
- **Title**: Vantor/Maxar WorldView Legion page
- **Link**: https://www.maxar.com/worldview-legion
- **Tier**: L1
- **Publication Date**: n/a
- **Timeliness Status**: Currently valid as of access date
- **Version Info**: Official Vantor/Maxar page
- **Target Audience**: Satellite imagery customers
- **Research Boundary Match**: Full match for imagery resolution
- **Summary**: Confirms 30 cm-class imagery capacity and native <5 m CE90 accuracy.
- **Related Sub-question**: Satellite cache SLA
## Source #16
- **Title**: OpenStreetMap zoom levels
- **Link**: https://wiki.openstreetmap.org/wiki/Zoom_levels
- **Tier**: L2
- **Publication Date**: n/a
- **Timeliness Status**: Currently valid for WebMercator math
- **Version Info**: Accessed 2026-04-29
- **Target Audience**: Map tile developers
- **Research Boundary Match**: Full match for tile math
- **Summary**: Documents meters-per-pixel by zoom and latitude correction, showing zoom alone is not a physical resolution contract.
- **Related Sub-question**: Tile cache convention
## Source #17
- **Title**: FastAPI first steps and OpenAPI docs
- **Link**: https://fastapi.tiangolo.com/tutorial/first-steps/
- **Tier**: L1
- **Publication Date**: n/a
- **Timeliness Status**: Currently valid as of access date
- **Version Info**: FastAPI docs accessed 2026-04-29
- **Target Audience**: Python API developers
- **Research Boundary Match**: Full match
- **Summary**: FastAPI automatically exposes Swagger UI, ReDoc, and `/openapi.json`.
- **Related Sub-question**: Local API and OpenAPI
## Source #18
- **Title**: GDAL Cloud Optimized GeoTIFF driver
- **Link**: https://gdal.org/en/stable/drivers/raster/cog.html
- **Tier**: L1
- **Publication Date**: n/a
- **Timeliness Status**: Currently valid as of access date
- **Version Info**: GDAL stable docs
- **Target Audience**: Geospatial developers
- **Research Boundary Match**: Partial overlap
- **Summary**: COG is a standard GeoTIFF profile optimized for tiled/ranged access and geospatial processing, useful for preprocessing and service exchange.
- **Related Sub-question**: Cache format
## Source #19
- **Title**: NVIDIA TensorRT issue #4348 — DINOv2-S Jetson Orin measurements
- **Link**: https://github.com/NVIDIA/TensorRT/issues/4348
- **Tier**: L4
- **Publication Date**: 2025-02-05
- **Timeliness Status**: Currently useful as deployment-risk evidence
- **Version Info**: TensorRT 10.4 on JetPack 6.1, Jetson Orin
- **Target Audience**: TensorRT/Jetson developers
- **Research Boundary Match**: Partial overlap
- **Summary**: Reports DINOv2-S TensorRT runs around 22-23 ms GPU compute on Jetson Orin with limited INT8 speedup, showing project-specific benchmarking is required.
- **Related Sub-question**: VPR runtime feasibility
## Source #20
- **Title**: Magic Leap SuperPoint pretrained network license
- **Link**: https://github.com/magicleap/SuperPointPretrainedNetwork/blob/master/LICENSE
- **Tier**: L1
- **Publication Date**: n/a
- **Timeliness Status**: Current as of access date
- **Version Info**: Repository license accessed 2026-04-29
- **Target Audience**: Product dependency/legal reviewers
- **Research Boundary Match**: Full match for local-feature dependency selection
- **Summary**: The official SuperPoint pretrained package is licensed for academic or non-profit noncommercial research use, so the weights cannot be treated as a product dependency without a separate commercial grant.
- **Related Sub-question**: Local matcher licensing
## Source #21
- **Title**: LightGlue license and extractor-license discussion
- **Link**: https://github.com/cvg/LightGlue/blob/main/LICENSE and https://github.com/cvg/LightGlue/issues/38
- **Tier**: L1/L4
- **Publication Date**: n/a
- **Timeliness Status**: Current as of access date
- **Version Info**: LightGlue repository accessed 2026-04-29
- **Target Audience**: CV developers and legal reviewers
- **Research Boundary Match**: Full match for local matcher selection
- **Summary**: LightGlue itself is Apache-2.0, but extractor weights such as SuperPoint carry their own license; the matching layer and extractor license must be reviewed separately.
- **Related Sub-question**: Local matcher licensing and deployment
## Source #22
- **Title**: AnyLoc DINOv2 VLAD descriptor release
- **Link**: https://github.com/AnyLoc/DINO
- **Tier**: L1
- **Publication Date**: n/a
- **Timeliness Status**: Current as of access date
- **Version Info**: AnyLoc/DINO README and release metadata accessed 2026-04-29
- **Target Audience**: VPR developers
- **Research Boundary Match**: Full match for VPR descriptor sizing
- **Summary**: AnyLoc DINOv2 VLAD examples produce 49,152-dimensional global descriptors and provide pretrained cluster centers, making descriptor compression/index sizing a first-order Jetson/cache concern.
- **Related Sub-question**: VPR memory and cache footprint
## Source #23
- **Title**: NVIDIA Isaac ROS cuVSLAM documentation
- **Link**: https://nvidia-isaac-ros.github.io/concepts/visual_slam/cuvslam/index.html
- **Tier**: L1
- **Publication Date**: n/a
- **Timeliness Status**: Current as of access date
- **Version Info**: Isaac ROS latest docs accessed 2026-04-29
- **Target Audience**: Robotics developers
- **Research Boundary Match**: Partial overlap
- **Summary**: cuVSLAM is described as stereo-visual-inertial SLAM/odometry, supports multiple stereo cameras, and uses IMU-only propagation only for short degraded intervals around one second.
- **Related Sub-question**: VO/VIO candidate fit
## Source #24
- **Title**: GDAL Cloud Optimized GeoTIFF driver and OGC COG standard
- **Link**: https://gdal.org/en/stable/drivers/raster/cog.html and http://www.opengis.net/doc/is/COG/1.0
- **Tier**: L1
- **Publication Date**: n/a
- **Timeliness Status**: Current as of access date
- **Version Info**: GDAL stable docs and OGC COG 1.0 accessed 2026-04-29
- **Target Audience**: Geospatial/cache engineers
- **Research Boundary Match**: Full match for cache exchange format
- **Summary**: COG supports tiled imagery, overviews, and compression options including JPEG/DEFLATE/ZSTD/WEBP; actual bytes per pixel must be measured on representative provider imagery rather than assumed from zoom level.
- **Related Sub-question**: Satellite cache storage feasibility
## Source #25
- **Title**: AerialVL public aerial visual localization dataset
- **Link**: https://github.com/hmf21/AerialVL and https://udspace.udel.edu/items/338c0b7c-993b-476c-a095-6820c6f1c031
- **Tier**: L1/L2
- **Publication Date**: 2024
- **Timeliness Status**: Current as of access date
- **Version Info**: Dataset/repository accessed 2026-04-29
- **Target Audience**: UAV localization researchers
- **Research Boundary Match**: Partial overlap
- **Summary**: Provides long aerial visual localization trajectories, RGB imagery, GNSS, reference satellite patches, and VPR/sequence localization/VO evaluation tasks; terrain and sensor details still differ from the fixed-wing deployment target.
- **Related Sub-question**: Validation data availability
## Source #26
- **Title**: UAV-VisLoc dataset
- **Link**: https://arxiv.org/html/2405.11936v1
- **Tier**: L1
- **Publication Date**: 2024
- **Timeliness Status**: Current as of access date
- **Version Info**: arXiv version accessed 2026-04-29
- **Target Audience**: UAV geo-localization researchers
- **Research Boundary Match**: Partial overlap
- **Summary**: Provides UAV images and satellite maps with metadata including coordinates, altitude, heading, and capture dates; useful for cross-view retrieval evaluation but not a complete substitute for the project's FC IMU traces.
- **Related Sub-question**: Validation data availability
## Source #27
- **Title**: LightGlue feature extractor support for ALIKED, DISK, and SIFT
- **Link**: https://github.com/cvg/LightGlue
- **Tier**: L1
- **Publication Date**: n/a
- **Timeliness Status**: Current as of access date
- **Version Info**: LightGlue repository accessed 2026-04-29
- **Target Audience**: CV developers
- **Research Boundary Match**: Full match for local matcher productization
- **Summary**: LightGlue supports multiple extractor families including ALIKED, DISK, SuperPoint, and SIFT; ALIKED/LightGlue ONNX deployment examples exist, and the LightGlue repository is Apache-2.0.
- **Related Sub-question**: License-cleared local feature candidates
## Source #28
- **Title**: DeDoDe local feature matcher and deployment ports
- **Link**: https://github.com/Parskatt/DeDoDe
- **Tier**: L1
- **Publication Date**: 2024
- **Timeliness Status**: Current as of access date
- **Version Info**: Repository and release metadata accessed 2026-04-29
- **Target Audience**: CV developers
- **Research Boundary Match**: Partial overlap
- **Summary**: DeDoDe is MIT-licensed, provides detector/descriptor models, and has ONNX/TensorRT ports; larger DINOv2-based descriptors still require model-size and runtime validation.
- **Related Sub-question**: License-cleared local feature candidates
## Source #29
- **Title**: OpenCV SIFT patent expiration and main-module availability
- **Link**: https://github.com/opencv/opencv/blob/4.x/modules/features2d/src/sift.dispatch.cpp
- **Tier**: L1/L3
- **Publication Date**: n/a
- **Timeliness Status**: Current as of access date
- **Version Info**: OpenCV 4.x source and patent-expiration references accessed 2026-04-29
- **Target Audience**: CV developers/legal reviewers
- **Research Boundary Match**: Full match for classical feature baseline
- **Summary**: SIFT is available in OpenCV's main features2d module after patent expiration, making SIFT a practical commercial-safe classical baseline.
- **Related Sub-question**: License-cleared local feature candidates
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# Fact Cards
## Fact #1
- **Statement**: ArduPilot MAVProxy GPSInput requires `GPS1_TYPE=14` to accept MAVLink GPS input.
- **Source**: Source #1
- **Phase**: Phase 2
- **Target Audience**: ArduPilot v1 integration
- **Confidence**: High
- **Related Dimension**: Flight-controller output
- **Fit Impact**: Supports `GPS_INPUT` v1 selection
## Fact #2
- **Statement**: MAVLink `GPS_INPUT` is a raw GPS sensor input message, not the global position estimate of the system.
- **Source**: Source #2
- **Phase**: Phase 2
- **Target Audience**: MAVLink integrators
- **Confidence**: High
- **Related Dimension**: Output semantics and covariance
- **Fit Impact**: Requires careful accuracy/covariance fields and FC EKF configuration
## Fact #3
- **Statement**: ArduPilot issue #30076 documented EKF3 instability when external navigation and GPS were fused unexpectedly; the issue is version-specific and marked closed, but it proves the risk class is real.
- **Source**: Source #3
- **Phase**: Phase 2
- **Target Audience**: ArduPilot v1/v1.1 release planning
- **Confidence**: Medium
- **Related Dimension**: Autopilot source fusion
- **Fit Impact**: Supports GPS_INPUT-only v1 and SITL gate before ODOMETRY
## Fact #4
- **Statement**: Jetson Orin Nano Super officially provides 67 sparse TOPS, 8 GB LPDDR5, 102 GB/s memory bandwidth, and a 25 W mode.
- **Source**: Source #4
- **Phase**: Phase 2
- **Target Audience**: Onboard runtime sizing
- **Confidence**: High
- **Related Dimension**: Hardware envelope
- **Fit Impact**: Supports feasibility, but memory/thermal profiling remains mandatory
## Fact #5
- **Statement**: NVIDIA reports TensorRT FP16 ViT benchmark rates on Orin Nano 8 GB Super Mode, including DINOv2-base-patch14 around 126 FPS in the published table.
- **Source**: Source #5
- **Phase**: Phase 2
- **Target Audience**: VPR runtime planning
- **Confidence**: Medium
- **Related Dimension**: VPR model feasibility
- **Fit Impact**: Supports conditional DINOv2 VPR, not per-frame full pipeline guarantee
## Fact #6
- **Statement**: A TensorRT issue report measured DINOv2-S on Jetson Orin at roughly 22-23 ms GPU compute with limited INT8 speedup.
- **Source**: Source #19
- **Phase**: Phase 2
- **Target Audience**: Jetson VPR deployment
- **Confidence**: Medium
- **Related Dimension**: Model optimization risk
- **Fit Impact**: Requires project benchmark; INT8 speedup must not be assumed
## Fact #7
- **Statement**: NVIDIA cuVSLAM is a GPU-accelerated stereo-visual-inertial SLAM and odometry library; it can use IMU fallback but is designed around stereo camera input.
- **Source**: Source #6
- **Phase**: Phase 2
- **Target Audience**: VO/VIO component selection
- **Confidence**: High
- **Related Dimension**: Required camera inputs
- **Fit Impact**: Reject as lead v1 VO for a single fixed monocular nav camera
## Fact #8
- **Statement**: ORB-SLAM3 and VINS-Fusion support monocular-inertial modes, but both are GPL-family licensed.
- **Source**: Source #7, Source #8
- **Phase**: Phase 2
- **Target Audience**: Product dependency selection
- **Confidence**: High
- **Related Dimension**: Licensing and integration
- **Fit Impact**: Experimental/reference only unless legal approval is obtained
## Fact #9
- **Statement**: A 2024 fixed-wing UAV study used satellite imagery to reduce visual odometry accumulated error over missions above 1000 m and over 17 km.
- **Source**: Source #12
- **Phase**: Phase 2
- **Target Audience**: Architecture feasibility
- **Confidence**: High
- **Related Dimension**: Satellite-aided VO
- **Fit Impact**: Supports hybrid VO + satellite anchor architecture
## Fact #10
- **Statement**: Recent UAV-to-satellite cross-view localization research targets source-domain appearance differences and similar-scene interference, confirming these are core risks.
- **Source**: Source #13
- **Phase**: Phase 2
- **Target Audience**: Cross-view matching design
- **Confidence**: High
- **Related Dimension**: False-match risk
- **Fit Impact**: Requires top-K VPR, local geometric verification, covariance gating, and stale-tile controls
## Fact #11
- **Statement**: Airbus Pléiades Neo advertises native 30 cm imagery and 3.5 m CE90 location accuracy.
- **Source**: Source #14
- **Phase**: Phase 2
- **Target Audience**: Satellite Service SLA
- **Confidence**: High
- **Related Dimension**: Reference imagery resolution
- **Fit Impact**: Supports 0.3 m/px ideal cache target
## Fact #12
- **Statement**: Vantor/Maxar states its constellation provides 30 cm-class imagery and native <5 m CE90 accuracy.
- **Source**: Source #15
- **Phase**: Phase 2
- **Target Audience**: Satellite Service SLA
- **Confidence**: High
- **Related Dimension**: Reference imagery resolution
- **Fit Impact**: Supports 0.3 m/px ideal cache target
## Fact #13
- **Statement**: OpenStreetMap zoom-level math gives meters-per-pixel at the equator and requires multiplying by cosine(latitude); zoom alone does not define physical pixel size.
- **Source**: Source #16
- **Phase**: Phase 2
- **Target Audience**: Cache engineering
- **Confidence**: High
- **Related Dimension**: Tile resolution convention
- **Fit Impact**: Supports explicit pixel-size cache contract
## Fact #14
- **Statement**: FastAPI automatically exposes interactive API docs and an OpenAPI schema.
- **Source**: Source #17
- **Phase**: Phase 2
- **Target Audience**: Local API design
- **Confidence**: High
- **Related Dimension**: OpenAPI documentation
- **Fit Impact**: Supports Python/FastAPI for local health/session/object API
## Fact #15
- **Statement**: FAISS GPU packages should not be assumed on Jetson ARM64; CPU FAISS or source-built GPU FAISS must be validated.
- **Source**: Source #11
- **Phase**: Phase 2
- **Target Audience**: VPR index deployment
- **Confidence**: Medium
- **Related Dimension**: Index runtime
- **Fit Impact**: Select CPU FAISS/HNSW-flat as v1 baseline; GPU FAISS is optimization only
## Fact #16
- **Statement**: COG is a standard GeoTIFF profile useful for geospatial processing, while MBTiles-style SQLite tile packages are better aligned with local offline tile lookup.
- **Source**: Source #18 plus offline cache search
- **Phase**: Phase 2
- **Target Audience**: Cache storage
- **Confidence**: Medium
- **Related Dimension**: Cache format
- **Fit Impact**: Select COG/GeoTIFF for Satellite Service exchange and SQLite/MBTiles-like package for onboard lookup/index sidecars
## Fact #17
- **Statement**: Official Magic Leap SuperPoint pretrained weights are restricted to academic or non-profit noncommercial research use.
- **Source**: Source #20
- **Phase**: Mode B Assessment
- **Target Audience**: Product dependency selection
- **Confidence**: High
- **Related Dimension**: Local matcher licensing
- **Fit Impact**: Reject official SuperPoint weights as a v1 product dependency unless a commercial license is obtained
## Fact #18
- **Statement**: LightGlue's Apache-2.0 license does not automatically license upstream feature extractors; extractor weights must be reviewed separately.
- **Source**: Source #21
- **Phase**: Mode B Assessment
- **Target Audience**: Product dependency selection
- **Confidence**: High
- **Related Dimension**: Local matcher licensing
- **Fit Impact**: Select LightGlue only behind a license-cleared extractor interface
## Fact #19
- **Statement**: AnyLoc DINOv2 VLAD examples produce 49,152-dimensional descriptors, so a large multi-scale VPR gallery can become a memory/storage problem if descriptors are stored uncompressed.
- **Source**: Source #22
- **Phase**: Mode B Assessment
- **Target Audience**: VPR/cache developers
- **Confidence**: High
- **Related Dimension**: VPR descriptor footprint
- **Fit Impact**: Requires PCA/quantization or smaller descriptors before the approach can satisfy the 8 GB memory and 10 GB cache budgets
## Fact #20
- **Statement**: NVIDIA describes cuVSLAM as stereo-visual-inertial SLAM/odometry and documents IMU-only degraded tracking as suitable only for short intervals around one second.
- **Source**: Source #23
- **Phase**: Mode B Assessment
- **Target Audience**: VO/VIO component selection
- **Confidence**: High
- **Related Dimension**: Required camera inputs and fallback duration
- **Fit Impact**: Keep cuVSLAM rejected as a v1 lead dependency for the fixed monocular navigation camera, but keep it as a Jetson benchmark/reference if hardware changes
## Fact #21
- **Statement**: COG supports tiled storage, overviews, and multiple compression profiles, but the docs do not define a universal bytes-per-pixel budget for 0.3 m satellite imagery.
- **Source**: Source #24
- **Phase**: Mode B Assessment
- **Target Audience**: Cache engineers
- **Confidence**: High
- **Related Dimension**: Satellite cache storage
- **Fit Impact**: Treat the 10 GB cache as a measured acceptance gate, not as proven by zoom-level math
## Fact #22
- **Statement**: AerialVL provides public aerial localization trajectories with RGB imagery, GNSS, and satellite reference patches, but it is only a partial match for the fixed-wing/ArduPilot/IMU deployment target.
- **Source**: Source #25
- **Phase**: Mode B Assessment
- **Target Audience**: Validation planning
- **Confidence**: Medium
- **Related Dimension**: Dataset realism
- **Fit Impact**: Use public datasets for early VPR and cross-view benchmarks, but require SITL or real FC IMU traces for final fusion validation
## Fact #23
- **Statement**: UAV-VisLoc provides UAV images, satellite maps, and metadata such as coordinates, altitude, heading, and capture date, but does not replace the need for project-specific IMU/camera timing traces.
- **Source**: Source #26
- **Phase**: Mode B Assessment
- **Target Audience**: Validation planning
- **Confidence**: Medium
- **Related Dimension**: Dataset realism
- **Fit Impact**: Add dataset adapters for retrieval/localization tests while keeping final acceptance tied to project replay and ArduPilot SITL
## Fact #24
- **Statement**: LightGlue supports ALIKED, DISK, SIFT, and other extractors, so the local matcher can name concrete license-cleared candidates instead of an abstract "license-cleared extractor."
- **Source**: Source #27
- **Phase**: Mode B Round 2
- **Target Audience**: Local matcher productization
- **Confidence**: High
- **Related Dimension**: Local feature selection
- **Fit Impact**: Select ALIKED + LightGlue and OpenCV SIFT/AKAZE as concrete v1 candidates; keep SuperPoint rejected unless licensed
## Fact #25
- **Statement**: DeDoDe is MIT-licensed and has ONNX/TensorRT deployment ports, making it a plausible learned-feature fallback, but its model size and DINOv2-related variants still require Jetson validation.
- **Source**: Source #28
- **Phase**: Mode B Round 2
- **Target Audience**: Local matcher productization
- **Confidence**: Medium
- **Related Dimension**: Local feature selection
- **Fit Impact**: Mark DeDoDe as experimental fallback until runtime and cross-domain accuracy are measured
## Fact #26
- **Statement**: SIFT is available in OpenCV's main features2d module after patent expiration, supporting its use as a commercial-safe classical baseline.
- **Source**: Source #29
- **Phase**: Mode B Round 2
- **Target Audience**: Local matcher productization
- **Confidence**: High
- **Related Dimension**: Classical matching baseline
- **Fit Impact**: Select OpenCV SIFT/AKAZE as the legal baseline for local geometric verification and regression tests
## Fact #27
- **Statement**: With 3 Hz camera input and <400 ms p95 output latency, a FIFO frame queue can violate latency even when every component is individually fast enough.
- **Source**: Derived from AC-4.1 and AC-4.4 timing constraints
- **Phase**: Mode B Round 2
- **Target Audience**: Real-time pipeline design
- **Confidence**: High
- **Related Dimension**: Runtime scheduling
- **Fit Impact**: Add a bounded latest-frame scheduler and explicit drop/backpressure policy to the architecture
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# Comparison Framework
## Selected Framework Type
Mode B weak-point assessment with exact-fit validation.
## Selected Dimensions
1. Functional fit against the Project Constraint Matrix.
2. Licensing and productization risk.
3. Jetson runtime and memory fit.
4. Cache/storage fit.
5. Validation evidence quality.
6. Security/safety impact.
7. Selection status.
## Initial Population
| Component Area | Candidate / Decision | Functional Fit | Runtime / Storage Fit | Evidence | Status |
|----------------|----------------------|----------------|-----------------------|----------|--------|
| Runtime scheduling | Bounded latest-frame scheduler with explicit drop/backpressure policy | Fits AC-4.1 frame-drop allowance and AC-4.4 no-batching requirement | Prevents stale FIFO backlog when a frame takes near the 400 ms p95 budget | Fact #27 | Selected |
| Relative motion | Custom planar VO/IMU | Fits fixed nadir monocular camera, flat terrain, FC attitude/altitude, and covariance output | Must run on downsampled/ROI imagery and skip under load | Facts #4, #9, #20 | Selected |
| Relative motion | NVIDIA cuVSLAM | Official docs emphasize stereo-visual-inertial SLAM; not exact-fit for one fixed monocular nav camera | Good Jetson stack, but wrong input assumptions for v1 | Facts #7, #20 | Rejected for v1 |
| Coarse VPR | AnyLoc/DINOv2-VLAD over chunks | Good retrieval shape for cross-domain top-K, conditional invocation only | 49,152-d descriptors require PCA/quantization/index-size proof | Facts #5, #6, #19 | Selected with compression gate |
| Local refinement | Official Magic Leap SuperPoint weights | Good technical candidate for local features | Product license blocks commercial use without separate grant | Fact #17 | Rejected for product v1 |
| Local refinement | ALIKED + LightGlue | Names a concrete license-cleared learned-feature candidate | Needs Jetson benchmark, ONNX/TensorRT proof, and cross-domain accuracy proof | Facts #18, #24 | Selected candidate |
| Local refinement | OpenCV SIFT/AKAZE + classical matching | Commercial-safe baseline for geometric verification and regression tests | May be weaker on cross-domain sparse fields | Facts #10, #26 | Selected baseline |
| Local refinement | DeDoDe | MIT-licensed learned-feature fallback with ONNX/TensorRT ports | Model size/runtime and DINOv2-related variants need validation | Fact #25 | Experimental only |
| State estimation | ESKF in local NED/ENU | Owns covariance, source labels, outlier rejection, and false-position budget | CPU feasible if bounded; needs Monte Carlo calibration | Facts #1, #2, #9, #10 | Selected |
| Flight-controller output | pymavlink `GPS_INPUT` only for v1 | Matches ArduPilot replacement-GPS framing | Low runtime load; fields must be honest raw-GPS-sensor values | Facts #1, #2, #3 | Selected |
| Flight-controller output | Dual `GPS_INPUT` + `ODOMETRY` | Richer covariance/yaw semantics, but overlaps source fusion | Version-specific EKF risk remains | Fact #3 | Deferred to v1.1 |
| Cache | 0.3-0.5 m/px COG/GeoTIFF exchange + SQLite package | Correct service/onboard split | 10 GB budget requires measured compression + descriptor/index proof | Facts #13, #16, #21 | Selected with storage gate |
| Validation | Public datasets only | Useful for VPR/cross-view early proof | Does not cover project FC IMU timing and ArduPilot injection | Facts #22, #23 | Insufficient alone |
| API | FastAPI local service | Fits OpenAPI/local control/object localization | Keep out of hot path; default docs at `/docs`, `/redoc`, OpenAPI path configurable | Fact #14, Context7 FastAPI docs | Selected |
## Rejected / Deferred Candidates
| Candidate | Reason |
|-----------|--------|
| Official SuperPoint pretrained weights as direct product dependency | Noncommercial research license blocks product use without separate commercial permission. |
| Uncompressed AnyLoc VLAD descriptors in the runtime cache | 49,152-dimensional descriptors can consume hundreds of MB to GB once multi-scale chunks and variants are included. |
| Per-frame DINOv2 VPR | Wastes latency/thermal budget and conflicts with AC-8.6 conditional VPR. |
| GPS_INPUT + ODOMETRY dual emission in v1 | Still too risky without version-pinned ArduPilot SITL proving no source double-fusion. |
| Public datasets as final validation substitute | They do not replace FC IMU, camera timing, thermal, and MAVLink injection evidence from the actual deployment stack. |
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# Reasoning Chain
## Dimension 1: Local Matcher Product Fit
### Fact Confirmation
SuperPoint-style features remain technically attractive for local geometric verification, but the official Magic Leap pretrained weights are noncommercial research-only (Fact #17). LightGlue itself is Apache-2.0, but it does not license upstream extractors (Fact #18). LightGlue supports ALIKED, DISK, SIFT, and other extractors (Fact #24), DeDoDe is MIT-licensed with deployment ports (Fact #25), and OpenCV SIFT is now a commercial-safe classical baseline (Fact #26).
### Reference Comparison
`solution_draft02.md` fixed the SuperPoint licensing issue but left "license-cleared extractor" too abstract for planning. The architecture can keep the local-verification stage, but planning needs named candidates so benchmark and licensing tasks can be decomposed.
### Conclusion
Reject official SuperPoint pretrained weights for product v1 unless a commercial license is obtained. Select ALIKED + LightGlue as the first learned-feature candidate, OpenCV SIFT/AKAZE as the legal baseline, and DeDoDe as an experimental fallback pending Jetson/model-size validation.
### Confidence
High for licensing; Medium for final extractor accuracy until benchmarked.
---
## Dimension 1.5: Real-Time Scheduling
### Fact Confirmation
The camera produces frames at 3 Hz, AC-4.1 allows <400 ms p95 end-to-end latency with up to ~10% dropped frames, and AC-4.4 forbids batching or delaying output (Fact #27).
### Reference Comparison
A FIFO queue can accumulate stale frames whenever a heavy VPR or local-matching event exceeds the 333 ms camera interval. That would make the system accurate on old images while violating the flight-controller output latency budget.
### Conclusion
Add a bounded latest-frame scheduler: camera queue size 1, explicit drop accounting, IMU propagation continues between image fixes, VPR/local matching run under deadlines, and every emitted `GPS_INPUT` references the freshest state timestamp.
### Confidence
High.
---
## Dimension 2: VPR Descriptor and Cache Footprint
### Fact Confirmation
AnyLoc DINOv2 VLAD examples produce 49,152-dimensional descriptors (Fact #19). The operational area can be up to 400 km² with multi-scale, overlapping chunks.
### Reference Comparison
Event-triggered VPR is still the right architecture, but uncompressed VLAD descriptors can quietly consume a large fraction of RAM/cache. For example, 4,000-10,000 chunks at 49,152 float32 values each is roughly 0.8-2.0 GB before multi-scale variants, indexes, metadata, and model/runtime memory.
### Conclusion
Keep AnyLoc/DINOv2-style VPR as the lead retrieval family only with a mandatory descriptor-compression gate: PCA/float16/product quantization or a smaller descriptor must be chosen before implementation freeze. CPU FAISS/HNSW remains the v1 baseline until Jetson GPU indexing is proven.
### Confidence
High for the footprint risk; Medium for the best compression/index choice.
---
## Dimension 3: Satellite Cache Storage
### Fact Confirmation
COG supports tiled imagery, overviews, and multiple compression profiles, but docs do not provide a universal bytes-per-pixel budget for the target imagery (Fact #21). Zoom level alone does not prove physical resolution (Fact #13).
### Reference Comparison
The 10 GB persistent cache budget may be plausible with lossy compressed 0.3-0.5 m/px imagery and careful indexing, but it is not proven until representative Suite Satellite Service imagery is packaged with overviews, manifests, descriptors, and generated-tile sidecars.
### Conclusion
Treat cache size as a hard measurement gate. The architecture should preserve the 10 GB budget but require a cache-packing benchmark before task decomposition commits to descriptor formats or chunk overlap settings.
### Confidence
Medium-high.
---
## Dimension 4: Relative Motion and cuVSLAM
### Fact Confirmation
NVIDIA describes cuVSLAM as stereo-visual-inertial SLAM/odometry, with IMU-only degraded tracking suitable only for short intervals around one second (Fact #20). The project has one fixed downward navigation camera for v1.
### Reference Comparison
cuVSLAM is a strong Jetson stack, but the selected v1 camera geometry does not match its documented primary input assumptions. A custom planar VO/IMU module can exploit nadir imagery, flat terrain, camera intrinsics, altitude, and FC attitude directly.
### Conclusion
Keep custom planar VO/IMU as the lead. Keep cuVSLAM rejected for v1 product use, but preserve it as a benchmark/reference if the hardware changes to stereo or if NVIDIA documents an exact monocular deployment path matching the project.
### Confidence
High.
---
## Dimension 5: Validation Data
### Fact Confirmation
AerialVL and UAV-VisLoc provide useful public aerial localization data, but they only partially match the fixed-wing, ArduPilot, high-rate IMU, camera-timing, and Ukraine steppe deployment context (Facts #22, #23).
### Reference Comparison
Public datasets can validate VPR/cross-view ideas and regression-test retrieval. They cannot prove ESKF covariance, MAVLink timing, companion reboot, or false-position budgets without representative IMU and FC traces.
### Conclusion
Use public datasets for early VPR/local-matcher benchmarking, then require ArduPilot SITL-generated IMU traces and at least one real FC/camera timing capture before final acceptance.
### Confidence
High.
---
## Dimension 6: ArduPilot Output
### Fact Confirmation
ArduPilot documents MAVLink GPS input with `GPS1_TYPE=14` (Fact #1), MAVLink defines `GPS_INPUT` as raw GPS sensor input rather than the global position estimate (Fact #2), and external-nav/GPS source-fusion issues are version-specific (Fact #3).
### Reference Comparison
`ODOMETRY` is semantically richer but increases EKF source-interaction risk. v1 `GPS_INPUT` only is narrower and forces honest accuracy fields, but it matches the "GPS substitute" framing and avoids dual-source overlap.
### Conclusion
Keep v1 `GPS_INPUT` only. Add a v1.1 research/testing backlog item for `ODOMETRY`, gated by exact ArduPilot release, params, and SITL proof.
### Confidence
High.
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# Validation Log
## Validation Scenario
Mode B validates the revised architecture against four weak-point scenarios:
1. Package a representative 400 km² cache slice at 0.3-0.5 m/px with COG exchange files, onboard SQLite/MBTiles-like tiles, manifests, overviews, VPR descriptors, and sidecars.
2. Build a VPR index for 600-800 m chunks with 40-50% overlap, using raw AnyLoc/DINOv2-VLAD descriptors and at least one compressed descriptor variant.
3. Benchmark local verification on the provided 60-frame sequence plus AerialVL/UAV-VisLoc-style public data using ALIKED + LightGlue, OpenCV SIFT/AKAZE, and DeDoDe by default.
4. Replay a GPS-denied flight in ArduPilot SITL with synthetic IMU and camera timestamps, then repeat with real FC/camera logs once available.
5. Stress the scheduler with synthetic heavy VPR/local-matcher events and verify stale frames are dropped rather than queued.
## Expected Based on Conclusions
- Cache package stays under 10 GB only if representative compression, overviews, descriptor compression, and metadata are measured together.
- VPR remains event-triggered and can load/query within the 8 GB memory envelope after descriptor compression.
- Official Magic Leap SuperPoint weights are absent from product builds unless commercial licensing is obtained.
- ALIKED + LightGlue, OpenCV SIFT/AKAZE, and DeDoDe are benchmarked as distinct local-matcher candidates instead of hiding behind a generic "license-cleared extractor" label.
- The camera queue remains bounded; frame drops are counted; emitted `GPS_INPUT` messages reference fresh estimator timestamps.
- Local matcher acceptance is based on measured geodetic error, inlier quality, covariance consistency, and false-positive rejection, not only pixel MRE.
- Public datasets are used for early retrieval/matching proof; final fusion and MAVLink acceptance uses SITL or real FC IMU/camera timing traces.
- v1 emits `GPS_INPUT` only; no `ODOMETRY` message appears on the wire.
## Actual Validation Results
Not executed in research phase. This log defines the validation plan for implementation and test decomposition.
## Counterexamples
- A technically accurate SuperPoint benchmark is not product-usable if it depends on noncommercial weights.
- A VPR demo can fit latency while still violating memory/cache budgets if uncompressed 49,152-dimensional descriptors are used at all chunk scales.
- A 400 km² imagery package can fit compressed raster storage but fail once descriptors, indexes, generated tiles, and metadata are counted.
- Public datasets can pass retrieval metrics but still miss FC timing, IMU covariance, thermal, and MAVLink source behavior.
- A FIFO image queue can meet throughput on average while still violating the p95 freshness requirement under bursty VPR/matcher load.
## Review Checklist
- [x] Draft conclusions consistent with fact cards.
- [x] No important Mode B weak-point dimensions missed for v1 architecture.
- [x] No over-extrapolation from urban-only cross-view datasets.
- [x] Selected components checked against the Project Constraint Matrix.
- [x] Mismatches recorded as disqualifiers or gates.
- [x] Noncommercial SuperPoint weights rejected for product v1 unless licensed.
- [x] Concrete local-matcher candidates named for planning.
- [x] Real-time scheduler/drop policy added as an architectural component.
- [ ] Cache-packing benchmark still required with Suite Satellite Service sample imagery.
- [ ] VPR descriptor compression benchmark still required on Jetson Orin Nano Super.
- [ ] Real IMU/FC logs still required for final validation.
## Conclusions Requiring Revision
- Replace direct SuperPoint dependency language with named license-cleared matcher candidates; official Magic Leap weights are rejected for product v1 unless separately licensed.
- Replace generic "license-cleared extractor" with named candidates: ALIKED + LightGlue, OpenCV SIFT/AKAZE, and DeDoDe fallback.
- Add a bounded latest-frame scheduler and scheduler stress tests to the plan.
- Add an explicit VPR descriptor compression/index-size gate before implementation freeze.
- Add an explicit cache-packing benchmark before accepting the 10 GB persistent-cache budget.
- If CPU FAISS/HNSW retrieval exceeds latency, benchmark source-built GPU FAISS or a smaller descriptor/index design.
- If the selected lens fails 10-20 cm/px at <=1 km AGL, recalibrate footprint, VPR chunk size, and matcher scale assumptions.
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# Component Fit Matrix
| Candidate | Intended Role | Project Constraints Checked | Evidence | Mismatches / Disqualifiers | Status | Decision Rationale |
|-----------|---------------|-----------------------------|----------|----------------------------|--------|--------------------|
| Bounded latest-frame scheduler | Runtime flow control and deadline ownership | 3 Hz input, <400 ms p95 latency, <=10% frame drop allowance, no batching | Fact #27 | Must account for dropped frames and preserve timestamp correctness | Selected | Prevents stale FIFO backlog and makes AC-4.1/AC-4.4 implementable |
| Custom planar VO/IMU module | Relative motion between satellite anchors | Fixed nadir monocular camera, flat-terrain assumption, FC attitude/altitude, 400 ms p95, no raw frame storage | Facts #4, #9, #20 | Requires calibration, time sync, covariance model, and Jetson benchmark | Selected | Best exact fit for v1 sensor geometry without stereo/GPL dependency |
| NVIDIA cuVSLAM | Relative VO/VIO | Jetson support, IMU fallback, visual odometry | Facts #7, #20 | Official docs describe stereo-visual-inertial assumptions; IMU-only degraded tracking is short-duration | Rejected | Good Jetson stack but wrong v1 camera/input assumptions |
| ORB-SLAM3 | Mono-inertial SLAM baseline | Monocular+IMU support | Fact #8 | GPLv3 product risk; generic SLAM assumptions not tailored to nadir fixed-wing | Experimental only | Useful offline benchmark, not selected product dependency |
| VINS-Fusion | Mono-inertial estimator baseline | Monocular+IMU support | Fact #8 | GPL-3.0 product risk; ROS/research integration burden | Experimental only | Useful reference, not selected product dependency |
| AnyLoc/DINOv2-VLAD style descriptors | Coarse VPR over precomputed chunks | Offline descriptor generation, event-triggered online query, sparse terrain, cache cap | Facts #5, #6, #10, #15, #19 | Raw 49,152-dimensional descriptors can violate memory/cache budgets unless compressed | Selected with compression gate | Strong fit as conditional top-K retrieval layer only after descriptor/index-size proof |
| FAISS CPU/HNSW/Flat | VPR vector retrieval | Jetson ARM64, local cache, bounded chunk index | Fact #15 | GPU packages not assumed; source-built GPU optional only | Selected | Conservative v1 baseline |
| FAISS GPU/cuVS | Accelerated VPR retrieval | CUDA Jetson, lower latency | Fact #15 | ARM64 GPU support requires source build and validation | Experimental only | Optimization path, not v1 assumption |
| Official Magic Leap SuperPoint pretrained weights | Local feature extraction | Cross-view candidate refinement | Fact #17 | Noncommercial research-only license blocks product use without separate commercial permission | Rejected | Do not select as v1 product dependency by default |
| ALIKED + LightGlue + RANSAC | Learned local satellite/UAV geometric match | Cross-view candidate refinement, inlier/covariance gate, license separation | Facts #18, #24 | Jetson speed and sparse-steppe accuracy still need benchmark | Selected candidate | Concrete license-cleared learned-feature path for v1 planning |
| SIFT/AKAZE + classical matching fallback | License-safe local matching fallback | No special model weights, CPU/GPU fallback | Fact #10 | May be weaker on cross-domain imagery and sparse fields | Selected as fallback | Keeps implementation unblocked and provides a legal baseline |
| DeDoDe | Learned local feature fallback | MIT-licensed model family and deployment ports | Fact #25 | Model size/runtime and DINOv2-related variants need validation | Experimental only | Useful fallback if ALIKED/SIFT miss accuracy or robustness targets |
| ESKF in local NED/ENU | State fusion and covariance owner | IMU propagation, VO, satellite anchors, false-position budget, GPS_INPUT accuracy | Facts #1, #2, #9, #10 | Requires calibration and Monte Carlo validation | Selected | Necessary to satisfy confidence and safety ACs |
| pymavlink `GPS_INPUT` emitter | Primary FC output | ArduPilot-only v1, GPS substitute framing, WGS84 output | Facts #1, #2, #3 | Must configure EKF/GPS params and validate in SITL | Selected | Safest v1 output path |
| MAVLink `ODOMETRY` auxiliary | Rich covariance/yaw external nav | ArduPilot EKF3 external nav | Fact #3 | Source-fusion/version risk; can double-fuse if misconfigured | Needs user decision for v1.1 | Deferred until SITL confirms exact release behavior |
| FastAPI local API | Health/session/object localization API | Python CV stack, OpenAPI docs, local-only service | Fact #14 | Must stay out of hot frame path | Selected | Satisfies API documentation and integration needs |
| SQLite/MBTiles-like package | Onboard tile lookup and metadata | Offline cache, random lookup, 10 GB cap, freshness metadata | Facts #13, #16, #21 | Must handle corruption, sidecar schema, and measured descriptor/index budget | Selected with storage gate | Good embedded local cache shape, but the 10 GB budget needs a packing benchmark |
| COG/GeoTIFF exchange | Satellite Service import/export | Geospatial processing, provider imagery, tile write-back ingestion | Facts #16, #21 | Not selected as sole hot lookup format; compression ratio must be measured | Selected | Good boundary format with Satellite Service |
| AerialVL / UAV-VisLoc adapters | Early VPR and cross-view validation data | Public aerial localization data with satellite references | Facts #22, #23 | Partial match only; not enough for FC IMU/MAVLink acceptance | Selected for early validation | Useful benchmark inputs, not final acceptance evidence |