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

Tech Stack Evaluation

Requirements Analysis

Area	Requirement
Runtime	Jetson Orin Nano Super, Ubuntu/JetPack, CUDA/TensorRT available, 8 GB shared memory, 25 W thermal envelope.
Language	Python is acceptable for orchestration/prototyping; C++/TensorRT paths likely needed for hot vision loops.
Vision	Calibration, undistortion, homography, VPR descriptors, local matching, RANSAC verification.
Estimation	BASALT VIO for relative camera+IMU propagation, wrapped by project-owned safety/anchor estimator logic for covariance calibration, source labels, blackout/spoofing modes, and MAVLink output mapping.
Storage	Offline satellite cache, descriptor index, FDR with no raw frame retention.
Autopilot	ArduPilot Plane, GPS_INPUT through pymavlink, MAVSDK for telemetry.

Technology Evaluation

Layer	Selected	Alternatives Considered	Rationale	Risk
OS / GPU stack	JetPack Ubuntu + CUDA + TensorRT	Plain Ubuntu without JetPack	Required for Jetson acceleration and profiling.	Thermal/performance tuning.
Calibration / geometry	OpenCV 4.x	Custom NumPy-only geometry	Mature APIs for calibration, undistortion, homography, RANSAC.	Version pin and calibration quality.
VO / estimator	BASALT + project-owned safety/anchor wrapper	OpenVINS, Kimera-VIO, custom OpenCV/ESKF, ORB-SLAM3, VINS-Fusion	BASALT is the selected production VIO candidate; wrapper owns source labels, covariance gates, degraded modes, satellite-anchor acceptance, and MAVLink semantics.	BASALT confidence/covariance must be calibrated; nadir fixed-wing replay required.
VPR descriptors	DINOv2-VLAD / AnyLoc-style, model-size profiled, TensorRT only after fidelity check	MixVPR, SALAD, NetVLAD, classical BoW	Strong retrieval evidence; good offline descriptor model.	Memory/latency and embedding drift if optimized incorrectly.
Vector search	FAISS CPU-first on Jetson ARM64	HNSWLIB, PostgreSQL/pgvector metadata-assisted search, brute-force NumPy, custom FAISS GPU build	Mature top-K search, save/load, PQ compression; PostgreSQL stores spatial/mission metadata around descriptor files.	CPU query latency must be profiled; GPU FAISS is not default on aarch64.
Local matching	DISK/ALIKED + LightGlue	SuperPoint+LightGlue, SIFT/ORB, LoFTR	Exact match outputs; Apache/BSD-friendly path; adaptive speed knobs.	Jetson profiling needed.
Raster cache	COG + PostgreSQL/PostGIS manifest + signed JSON sidecars	PMTiles, MBTiles, loose tile folders	COG fits geospatial raster and write-new-tile workflow; PostGIS supports spatial/freshness queries.	10 GB budget pressure and local DB availability.
MAVLink	MAVSDK telemetry + pymavlink GPS_INPUT	MAVSDK-only, MAVProxy bridge	MAVSDK does telemetry well; GPS_INPUT needs raw field control.	Plane SITL validation.
FDR	PostgreSQL event index + CBOR/binary payload segments with optional Parquet export	Raw images, plain CSV	Queryable event metadata, bounded payload segments, no raw frame retention.	Schema and local DB availability.
Testing	ArduPilot Plane SITL + AerialVL/VPAir + EuRoC + representative flight replay	Public datasets only	No public dataset covers all ACs.	Representative data collection required.

Tech Stack Summary

• Primary implementation: Python for orchestration, test harness, cache tooling, and MAVLink integration; C++/TensorRT for hot-path vision if profiling requires it.
• Vision utilities: OpenCV 4.x.
• Estimator: BASALT VIO plus project-owned safety/anchor wrapper and mode machine.
• Global retrieval: DINOv2-VLAD style descriptors with CPU-first FAISS top-K search.
• Local matching: DISK/ALIKED + LightGlue; SuperPoint only after license review.
• Cache: COG imagery, PostgreSQL/PostGIS manifest metadata, signed JSON sidecars, FAISS index files.
• Autopilot: MAVSDK subscriptions plus pymavlink GPS_INPUT.
• Validation: public datasets for component de-risking, Plane SITL for integration, representative flight/replay data for acceptance.

Risk Assessment

Risk	Impact	Mitigation
VPR/local matching exceeds Jetson latency	AC-4.1 failure	Conditional VPR, top-K caps, downsampled descriptors, CPU FAISS profiling, TensorRT only after embedding-fidelity checks.
Descriptor cache exceeds 10 GB	AC-8.3/storage failure	PQ/compression, multi-scale budget report, split descriptor budget if needed.
GPL library accidentally becomes production dependency	Licensing issue	Keep OpenVINS/ORB-SLAM3 as reference only unless legal approves; BASALT is the production VIO candidate.
BASALT covariance under-reports real error	False-position safety budget failure	Calibrate wrapper covariance against OpenVINS covariance, ground truth replay, and satellite anchor residuals.
Plane failsafe differs from Copter docs	Safety behavior mismatch	Production-parameter ArduPilot Plane SITL gate.
GPS_INPUT velocity ignore flags behave unexpectedly	EKF drift or false velocity fusion	SITL tests for velocity fields, ignore flags, and EK3_SRC1_* source parameters.
Public datasets fail to represent Ukrainian agricultural terrain	False confidence	Require representative synchronized flight/replay data before AC signoff.
Thermal throttling	Latency regression	Hot-soak test and throttle logging per AC-NEW-5.

Learning / Implementation Requirements

• Jetson profiling with CUDA/TensorRT and memory instrumentation.
• ArduPilot Plane SITL, GPS_INPUT, GPS spoof/failsafe parameters.
• Geospatial raster formats: COG, CRS, tile matrices, m/px metadata, manifests.
• BASALT integration, covariance calibration, and Mahalanobis rejection in the safety/anchor wrapper.
• Aerial VPR benchmark methodology and georeference recall.