gps-denied-onboard/_docs/00_research/00_question_decomposition.md

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.