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Revise acceptance criteria and restrictions documentation to clarify recent updates and specifications. Key changes include enhanced definitions for position accuracy, image processing quality, and operational parameters, as well as updates to camera specifications and validation requirements. This revision aims to improve clarity and ensure alignment with project goals.

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Oleksandr Bezdieniezhnykh
2026-05-01 16:24:46 +03:00
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# Question Decomposition
## Classification
- **Original question**: Design a GPS-denied onboard localization system for a fixed-wing UAV using a nadir camera, IMU, preloaded satellite imagery, and ArduPilot `GPS_INPUT`.
- **Active mode**: Mode A Phase 2, initial solution research.
- **Research output class**: Technical-component selection.
- **Question type**: Decision support with knowledge organization.
- **Timeliness sensitivity**: High for VPR, embedded AI inference, and MAVLink/ArduPilot integration; medium for geometry and filtering fundamentals.
## Research Boundary
| Dimension | Boundary |
|-----------|----------|
| Population | Fixed-wing UAV missions; not multirotor hover workflows. |
| Geography | Eastern/southern Ukraine operational areas east/left of the Dnipro River. |
| Timeframe | Current implementation target with 2024-2026 component evidence where possible. |
| Level | Onboard real-time production system, not offline post-processing. |
| Operating context | 8 h flight, 60 km/h, <=1 km AGL, 3 fps nav camera, Jetson Orin Nano Super, GPS denied/spoofed. |
| Required interfaces | Offline Satellite Service cache in; MAVLink `GPS_INPUT`, QGC telemetry, FDR records, and object-coordinate API out. |
| Non-functional envelope | <400 ms p95, <8 GB shared memory, 10 GB persistent cache target, 64 GB FDR cap, safety covariance and false-position budgets. |
## Project Constraint Matrix Summary
| Constraint Area | Binding Constraint |
|-----------------|-------------------|
| Camera | ADTi 20MP 20L V1, APS-C, ~5472 x 3648, fixed nadir, no gimbal stabilization. |
| Sensors | FC IMU/attitude/airspeed/altitude available over MAVLink; sample data lacks synchronized IMU. |
| Reference imagery | Offline cache only, 0.5 m/px minimum and 0.3 m/px ideal, freshness gates, no in-flight provider fetch. |
| Runtime | Jetson Orin Nano Super, CUDA/TensorRT available, 25 W thermal envelope. |
| Autopilot | ArduPilot only, v1 emits `GPS_INPUT` only; ODOMETRY intentionally disabled. |
| Storage | No raw frame retention; tiles + FDR only. Descriptor/index storage must be budgeted. |
| Safety | Reject weak anchors, never under-report covariance, fail/degrade honestly in blackout and spoofing. |
| Hard disqualifiers | Per-frame heavy VPR without profiling, runtime dependence on external network, stale-tile confident anchors, GPL production dependency unless licensing is accepted. |
## Perspectives
| Perspective | Focus |
|-------------|-------|
| Operator / mission user | Does the system keep the UAV navigable and report honest confidence under spoofing/blackout? |
| Embedded implementer | Can the pipeline fit <400 ms p95 and <8 GB on Jetson with maintainable interfaces? |
| Safety reviewer | Are false-position and cache-poisoning paths gated before they can steer the FC or poison future caches? |
| Field practitioner | Will seasonal agricultural repetition, turns, haze/smoke, and stale imagery break the architecture? |
| Contrarian | Which attractive libraries or SOTA models fail because of licensing, memory, latency, or input mismatch? |
## Sub-Questions And Query Variants
1. What architecture bounds drift while GPS is denied?
- fixed-wing UAV GPS-denied satellite image matching visual odometry
- visual odometry satellite imagery accumulated error fixed wing UAV
- monocular VIO aerial navigation scale ambiguity satellite anchor
- GPS spoofed UAV visual inertial navigation covariance failover
2. Which VO/VIO approach fits one nadir camera + IMU?
- OpenVINS monocular visual inertial odometry Jetson
- ORB-SLAM3 monocular inertial Jetson UAV limitations
- VINS-Fusion fixed wing monocular IMU outdoor aerial
- homography visual odometry nadir UAV IMU fusion
3. Which satellite retrieval and matching approach fits offline cache + <400 ms?
- aerial visual place recognition survey DINOv2 FAISS
- DINOv2 VLAD aerial VPR embedded memory
- LightGlue SuperPoint DISK ALIKED TensorRT Jetson
- cross-view UAV satellite matching failure modes farmland
4. How should the estimator and safety modes work?
- ESKF visual inertial GPS denied UAV covariance
- GPS_INPUT horiz_accuracy covariance external GPS ArduPilot
- visual blackout IMU dead reckoning UAV covariance growth
- false position rejection Mahalanobis gate visual localization
5. What cache format and data contract fit the onboard/Satellite Service boundary?
- COG PMTiles MBTiles offline raster cache embedded
- satellite tile descriptor index storage FAISS PMTiles
- cloud optimized geotiff local update limitations
- PMTiles read only update PostgreSQL/PostGIS-backed raster cache
6. How should MAVLink output integrate with ArduPilot Plane?
- ArduPilot GPS_INPUT GPS1_TYPE 14 Plane SITL
- pymavlink gps_input_send external GPS example
- MAVSDK GPS_INPUT support raw MAVLink
- ArduPilot EKF GPS glitch spoof failsafe Plane parameters
7. What validation datasets and tests are needed?
- AerialVL UAV satellite visual localization dataset
- VPAir aerial visual place recognition dataset
- EuRoC MAV visual inertial odometry dataset
- ArduPilot Plane SITL fake GPS spoofing simulation
## Component Option Search Plan
| Component Area | Option Families / Candidates | Evidence Needed |
|----------------|------------------------------|-----------------|
| Camera calibration and geometry | OpenCV calibration/homography; custom NumPy geometry; ROS camera pipeline | Official API for intrinsics, distortion, homography, RANSAC; permissive licensing; Jetson compatibility. |
| VO / VIO propagation | OpenVINS, ORB-SLAM3, VINS-Fusion, custom homography+IMU ESKF | Exact monocular+IMU input fit, output pose/covariance, licensing, runtime, initialization behavior. |
| VPR global retrieval | DINOv2-VLAD/AnyLoc, MixVPR/SALAD/SelaVPR, classical NetVLAD/BoW | Aerial benchmark evidence, descriptor size, offline index fit, embedded feasibility. |
| Local cross-domain matching | LightGlue + DISK/ALIKED, SuperPoint+LightGlue, LoFTR/XFeat, SIFT/ORB baseline | Inputs/outputs, match coordinates, license, runtime knobs, TensorRT/Jetson feasibility. |
| Vector index | FAISS CPU/GPU, PostgreSQL/pgvector metadata-assisted search, Annoy/HNSWLIB | Top-K retrieval, saved index, memory/compression knobs, ARM/Jetson feasibility. |
| Estimator | Custom ESKF, factor graph, robot_localization | Covariance output, mode labels, Mahalanobis gates, source-specific update control. |
| Cache/storage | COG, PostgreSQL/PostGIS manifest, PMTiles, MBTiles, raw tile folders | Offline read/update behavior, storage efficiency, metadata/manifest support. |
| MAVLink integration | pymavlink, MAVSDK, MAVProxy bridge | `GPS_INPUT` support, ArduPilot `GPS1_TYPE=14`, telemetry subscriptions, QGC status. |
| FDR | PostgreSQL event index, Parquet export, CBOR segment files | Streaming writes, rollover, compact typed records, replayability. |
## Completeness Audit
- **Cost/resources**: covered by Jetson, cache, thermal, and descriptor storage constraints.
- **Legal/licensing**: covered; GPL VIO/SLAM tools are not selected for production.
- **Dependencies**: Satellite Service cache contract, ArduPilot Plane SITL, and synchronized validation data are explicit dependencies.
- **Operating environment**: fixed-wing, altitude, terrain, seasonal/visibility classes, and blackout cases covered.
- **Failure modes**: VO failure, stale tiles, spoofing, blackout, thermal throttling, false anchors, cache poisoning covered.
- **Practitioner concerns**: real-time embedded performance and dataset mismatch covered through survey and benchmark sources.
- **Change over time**: DINOv2/VPR models and Jetson/TensorRT assumptions require version-pinned profiling during implementation.
## Mode B Round 2 Addendum — User-Requested Technology Check
### Research Output Class
Technical-component selection. The addendum verifies two implementation choices before autodev proceeds to planning:
1. Whether OpenVINS should replace the custom OpenCV-based VO/ESKF direction.
2. Whether DINOv2-VLAD + ALIKED/LightGlue is still the right satellite retrieval and anchor-verification stack.
### Boundary Clarification
"Custom OpenCV" is treated as OpenCV for calibration, undistortion, feature geometry, homography/RANSAC, and MRE measurement, plus a project-owned ESKF/mode machine. It is not treated as a naive OpenCV-only replacement for VIO.
### Additional Query Variants Executed
- OpenVINS GPL-3 license MSCKF visual inertial odometry documentation monocular IMU 2026
- OpenVINS visual inertial odometry GPS denied UAV MSCKF limitations monocular high altitude nadir camera
- why not use OpenVINS production GPL ROS dependency visual inertial odometry limitations
- OpenCV license BSD 3-Clause camera calibration findHomography RANSAC documentation 4.x
- custom visual odometry OpenCV homography IMU EKF fixed wing UAV satellite imagery GPS denied 2024
- DINOv2 VLAD AnyLoc visual place recognition aerial satellite retrieval benchmark 2024 2025
- DINOv2 VLAD limitations visual place recognition storage compute AnyLoc limitations
- DINOv2 TensorRT Jetson performance issue embedding accuracy visual place recognition
- ALIKED LightGlue license local feature matching aerial image registration 2024 2025
- ALIKED LightGlue ONNX TensorRT Jetson performance benchmark local feature matching
- aerial visual place recognition survey 2024 runtime memory re-ranking SuperGlue LightGlue satellite UAV retrieval
### Addendum Conclusion
OpenVINS is better than a pure custom OpenCV-only VIO implementation, but the production architecture should keep OpenCV as the utility layer and keep the project-owned ESKF/mode machine as the shipped estimator. OpenVINS becomes a mandatory benchmark/reference because it does not own the satellite anchor, spoofing/blackout, source-label, cache-write, and MAVLink semantics required by the acceptance criteria, and GPLv3 remains a production dependency blocker.
DINOv2-VLAD + CPU-first FAISS + ALIKED/LightGlue remains the preferred anchor stack, with two non-negotiable constraints: retrieval is trigger-based rather than per-frame, and TensorRT/ONNX optimizations are accepted only after descriptor-fidelity and Jetson latency tests.