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

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.