gps-denied-onboard/_docs/01_solution/solution_draft01.md

Solution Draft

Product Solution Description

Build an onboard GPS-denied localization service that runs on the companion Jetson, consumes the fixed nadir navigation camera plus flight-controller IMU/attitude/altitude, and emits ArduPilot-compatible GPS_INPUT estimates with honest covariance.

The solution is a hybrid estimator:

Nav camera + FC telemetry
        |
        v
Image quality + calibration + orthorectification
        |
        +--> Steady state: VO/IMU propagation --> ESKF --> GPS_INPUT + QGC + FDR
        |
        +--> Re-anchor triggers: VPR top-K --> local match/RANSAC --> ESKF anchor update
        |
        +--> Tile generation: ortho tile + quality sidecar --> local cache --> post-flight Satellite Service upload

The steady-state path is intentionally lightweight. Heavy global retrieval and cross-domain local matching run only on cold start, VO failure, sharp turns, disconnected segments, covariance growth, or stale-anchor age.

Existing / Competitor Solutions Analysis

Recent fixed-wing GPS-denied research supports the same high-level mechanism: monocular visual odometry alone accumulates scale/drift error, while satellite-image comparison can periodically correct it. Aerial VPR surveys also show why the implementation cannot be naive: weather, season, scale mismatch, repetitive fields, tile overlap, and re-ranking runtime all matter.

The selected design borrows the proven structure but rejects an all-in-one SLAM dependency as the product core. OpenVINS and ORB-SLAM3 are useful benchmark/reference implementations, but their GPL-family licensing and broader SLAM lifecycle do not fit a default production dependency for this project.

Architecture

Component: Camera Ingest, Calibration, And Geometry

Solution	Tools	Pinned Mode/Config	Advantages	Limitations	Requirements	Security	Cost	API Capability Evidence	Fit
OpenCV geometry utility layer	OpenCV 4.x	Camera calibration, undistortion, RANSAC homography, reprojection-error measurement	Mature, local, fast, exact API fit	Not a full estimator	Calibration target, fixed intrinsics/extrinsics, lens/FOV selection	Local-only, no network	Low	MVE: _docs/00_research/02_fact_cards.md; Source #5	Selected

Exact-fit evidence:

• Project constraints checked: fixed nadir camera, calibration, homography, MRE gates.
• Disqualifiers: none.
• Restrictions × AC matrix: _docs/00_research/06_component_fit_matrix.md.

Component: VO / IMU Propagation And Estimator

Solution	Tools	Pinned Mode/Config	Advantages	Limitations	Requirements	Security	Cost	API Capability Evidence	Fit
Custom VO/IMU ESKF	OpenCV + custom estimator	Frame-to-frame homography/features + FC IMU/attitude/altitude fused in a custom ESKF with source modes	Owns covariance, source labels, degraded modes, ArduPilot output semantics	More implementation work than adopting VIO library	Synchronized frames/IMU, calibration, replay tests	No third-party cloud; deterministic local logs	Medium	Facts #1, #4, #5, #16	Selected
OpenVINS reference	OpenVINS	Monocular camera + IMU EKF/MSCKF reference runs	Strong VIO reference and evaluation tools	GPL-3 production dependency risk	Dataset/replay adapter	Local only	Low for benchmark	Source #3	Reference only
ORB-SLAM3 alternative	ORB-SLAM3	Monocular-inertial SLAM	Mature SLAM benchmark	GPLv3, heavier map lifecycle, initialization complexity	Calibration, vocabulary, runtime tuning	Local only	Medium	Source #4	Rejected for production

Exact-fit evidence:

• Project constraints checked: one camera + IMU, frame-by-frame output, covariance labels, blackout/spoofing modes.
• Runtime quality gate: validate drift and covariance on EuRoC-style and representative fixed-wing replay.

Component: Satellite Retrieval And Local Anchor

Solution	Tools	Pinned Mode/Config	Advantages	Limitations	Requirements	Security	Cost	API Capability Evidence	Fit
DINOv2-VLAD + FAISS + LightGlue	DINOv2/AnyLoc-style descriptors, FAISS, DISK/ALIKED+LightGlue, OpenCV RANSAC	Offline precomputed VPR chunk descriptors; conditional query descriptor; top-K FAISS; local matching on candidates	Matches AC-8.6; scalable top-K; local geometry verifies anchors	Needs Jetson profiling and model-size pruning	Fresh satellite cache, descriptors, dynamic K, RANSAC gates	Cache is local; no in-flight provider calls	Medium-high	MVE blocks in 02_fact_cards.md; Sources #6-#9	Selected with runtime gate
SuperPoint + LightGlue	SuperPoint, LightGlue	Same local matching with SuperPoint features	Strong technical baseline	SuperPoint license is restrictive	Legal review	Local only	Medium	Source #6	Needs user decision
Classical SIFT/ORB-only	OpenCV	Handcrafted features and homography	Simple fallback, low compute	Poor cross-domain robustness	Feature-rich scenes	Local only	Low	Source #5	Fallback / regression baseline

Exact-fit evidence:

• Project constraints checked: offline cache, top-K dynamic retrieval, cross-domain local match, <400 ms hot-path constraint.
• Runtime gate: heavy VPR/re-ranking is trigger-based, not per-frame.

Component: Satellite Cache And Tile Write-Back

Solution	Tools	Pinned Mode/Config	Advantages	Limitations	Requirements	Security	Cost	API Capability Evidence	Fit
COG + PostgreSQL/PostGIS manifest + descriptor sidecars	GDAL COG, PostgreSQL/PostGIS manifest, FAISS sidecars	Service tiles and generated candidate tiles stored as tiled compressed GeoTIFFs with CRS/date/source/meter-per-pixel metadata	Geospatial standard, supports write-new-tile workflow, descriptor accounting	Needs careful 10 GB budget	STAC-like manifest, freshness gates, descriptor pruning	Local signed manifests recommended	Medium	Source #18	Selected
PMTiles archive	PMTiles	Single-file read archive	Efficient map reads	Read-only; cannot update in place	Archive rebuild for updates	Local file integrity	Low	Source #17	Rejected for live mutable cache

Exact-fit evidence:

• Project constraints checked: offline-only, no raw photo storage, mid-flight generated tiles, Satellite Service ingest metadata.
• External dependency: Satellite Service owns the promotion/voting layer for trusted basemap updates.

Component: MAVLink Integration And Telemetry

Solution	Tools	Pinned Mode/Config	Advantages	Limitations	Requirements	Security	Cost	API Capability Evidence	Fit
MAVSDK telemetry + pymavlink output	MAVSDK, pymavlink	MAVSDK subscribes to telemetry; pymavlink emits GPS_INPUT to ArduPilot with GPS1_TYPE=14	Exact GPS_INPUT field control while keeping high-level telemetry APIs	Plane-specific failsafe/spoof triggers need SITL proof	ArduPilot Plane params, QGC status, FDR tlog	Validate MAVLink source and message rate	Medium	Sources #10-#12	Selected

Exact-fit evidence:

• Project constraints checked: ArduPilot only, v1 GPS_INPUT only, WGS84 output, covariance to horiz_accuracy.
• Validation gate: Plane SITL with production parameters.

Component: Flight Data Recorder

Solution	Tools	Pinned Mode/Config	Advantages	Limitations	Requirements	Security	Cost	API Capability Evidence	Fit
Segmented FDR	PostgreSQL event index + binary/CBOR payloads with Parquet export post-flight	Fixed-size segment files for per-frame estimates, IMU, MAVLink, health, emitted GPS_INPUT, generated-tile metadata	Replayable, bounded, no raw frame retention	Exact format selected during implementation	Rollover policy, monotonic timestamps	Integrity hash per segment recommended	Medium	AC-NEW-3	Selected pattern

Runtime Modes

Mode	Trigger	Behavior	Output Label
Satellite anchored	VPR + local match passes freshness, RANSAC, covariance, and Mahalanobis gates	ESKF absolute update, low covariance, tile generation eligible if sigma gate passes	satellite_anchored
VO extrapolated	Last anchor fresh enough, VO healthy, normal overlap	VO/IMU propagation, covariance grows with anchor age	vo_extrapolated
Dead reckoned	Visual blackout, VO failure without anchor, spoofing while no visual signal	IMU-only propagation, monotonic covariance growth, degraded fix type thresholds	dead_reckoned
Failsafe / no fix	Blackout >30 s or covariance >500 m	GPS_INPUT.fix_type=0, horiz_accuracy=999.0, QGC status	dead_reckoned with failsafe status

Testing Strategy

Integration / Functional Tests

• Replay normal overlapping frames and assert AC-2.1a VO registration rate and AC-2.2 VO MRE.
• Replay satellite-anchor cases and assert AC-1.1/1.2, AC-2.2 cross-domain MRE, freshness gates, and source labels.
• Inject stale tiles and assert no satellite_anchored output.
• Inject sharp turns and disconnected segments and assert VPR relocalization.
• Run ArduPilot Plane SITL with GPS1_TYPE=14 and assert valid GPS_INPUT fields, fix type degradation, and QGC statuses.
• Inject visual blackout + spoofed GPS and assert spoofed GPS is ignored, covariance grows, and thresholds match AC-NEW-8.
• Request AI-camera object coordinates and assert level-flight projection plus maneuver error bound.

Non-Functional Tests

• Jetson Orin Nano Super profiling: <400 ms p95, <8 GB shared memory, 25 W no-throttle hot-soak.
• Cache build test for 400 km²: imagery + manifests + descriptors fit within budget or fail with explicit budget report.
• 8-hour FDR load test: <=64 GB, rollover logged, no silent data loss.
• Monte Carlo false-anchor and over-confidence tests for AC-NEW-4 and AC-NEW-7.
• Cold boot 50x: first valid GPS_INPUT <30 s p95.

References

Detailed source registry: _docs/00_research/01_source_registry.md.

Key sources:

• Fixed-wing satellite-aided VO: https://www.mdpi.com/2076-3417/14/16/7420
• Aerial VPR survey: https://arxiv.org/abs/2406.00885
• OpenVINS docs: https://docs.openvins.com/
• ORB-SLAM3 README: https://raw.githubusercontent.com/UZ-SLAMLab/ORB_SLAM3/master/README.md
• LightGlue README: https://raw.githubusercontent.com/cvg/LightGlue/main/README.md
• FAISS docs: https://faiss.ai/index.html
• ArduPilot GPSInput: https://ardupilot.org/mavproxy/docs/modules/GPSInput.html
• MAVLink GPS_INPUT: https://mavlink.io/en/messages/common.html#GPS_INPUT
• Jetson Orin Nano Super: https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/nano-super-developer-kit/
• PMTiles: https://docs.protomaps.com/pmtiles/
• GDAL COG: https://gdal.org/en/stable/drivers/raster/cog.html

Related Artifacts

• AC assessment: _docs/00_research/00_ac_assessment.md
• Question decomposition: _docs/00_research/00_question_decomposition.md
• Source registry: _docs/00_research/01_source_registry.md
• Fact cards: _docs/00_research/02_fact_cards.md
• Comparison framework: _docs/00_research/03_comparison_framework.md
• Reasoning chain: _docs/00_research/04_reasoning_chain.md
• Validation log: _docs/00_research/05_validation_log.md
• Component fit matrix: _docs/00_research/06_component_fit_matrix.md