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

Tech Stack Evaluation

Requirements Analysis

Requirement	Implication
Jetson Orin Nano Super, 8 GB, 25 W	Prefer Python orchestration with C++/TensorRT hot paths; benchmark memory early.
Fixed nadir monocular nav camera	Avoid stereo-only VO dependencies; use custom planar VO/IMU.
ArduPilot v1 GPS substitute	Use pymavlink GPS_INPUT; defer ODOMETRY.
Offline satellite cache	Use local SQLite/MBTiles-like packages with explicit manifests and precomputed descriptors.
OpenAPI docs	Use FastAPI for local control/health/object API.
No raw frame persistence	FDR stores metadata, estimates, MAVLink, IMU, health, generated tiles, and failure thumbnails only.

Technology Evaluation

Language and Runtime

Option	Fitness	Maturity	Security	Team/Cost	Scalability	Score	Decision
Python 3.11+ orchestration	High	High	Medium	High	Medium	4/5	Selected
C++ hot-path modules	High	High	Medium	Medium	High	4/5	Selected for VO/matching kernels as needed
.NET backend	Low	High	High	Medium	High	2/5	Rejected for onboard CV hot path
Rust hot-path modules	Medium	Medium	High	Medium	High	3/5	Deferred unless memory safety becomes critical

Vision and Inference

Option	Fitness	Maturity	Security	Team/Cost	Scalability	Score	Decision
OpenCV	High	High	Medium	High	Medium	5/5	Selected
TensorRT	High	High	Medium	Medium	High	5/5	Selected for deployed models
PyTorch runtime	Medium	High	Medium	High	Medium	3/5	Dev/training only, not hot v1 runtime
cuVSLAM	Low for v1	High	Medium	Medium	High	2/5	Rejected for v1 sensor mismatch
ORB-SLAM3 / VINS-Fusion	Medium	Medium	Low licensing fit	Medium	Medium	2/5	Experimental only

Matching and VPR

Option	Fitness	Maturity	Security	Team/Cost	Scalability	Score	Decision
AnyLoc/DINOv2-VLAD style descriptors	High	Medium	Medium	Medium	High	4/5	Selected with benchmark
FAISS CPU/HNSW-flat	High	High	Medium	High	Medium	4/5	Selected v1 baseline
FAISS GPU/cuVS	Medium	Medium	Medium	Low on Jetson	High	3/5	Optimization only
ALIKED + LightGlue	High	Medium	Medium	Medium	High	4/5	Selected learned-feature candidate
OpenCV SIFT/AKAZE	Medium	High	High	High	Medium	3/5	Selected legal baseline
DeDoDe	Medium	Medium	Medium	Medium	Medium	3/5	Experimental fallback
Official Magic Leap SuperPoint weights	High	Medium	Low for product	Medium	High	1/5	Rejected unless commercial license is obtained

State, API, and Storage

Option	Fitness	Maturity	Security	Team/Cost	Scalability	Score	Decision
ESKF with NumPy/SciPy prototype, C++ Eigen if needed	High	High	Medium	Medium	High	5/5	Selected
FastAPI + Pydantic	High	High	Medium	High	Medium	5/5	Selected
SQLite / MBTiles-like local cache	High	High	Medium	High	Medium	4/5	Selected
COG/GeoTIFF + GDAL/Rasterio exchange	High	High	Medium	Medium	High	4/5	Selected
PostgreSQL/PostGIS onboard	Low	High	Medium	Low	High	2/5	Rejected for embedded v1; too heavy
Parquet/JSONL FDR segments	High	High	Medium	High	Medium	4/5	Selected

MAVLink and Ground Station

Option	Fitness	Maturity	Security	Team/Cost	Scalability	Score	Decision
pymavlink GPS_INPUT	High	High	Medium	High	Medium	5/5	Selected
MAVSDK telemetry plumbing	Medium	High	Medium	High	Medium	4/5	Selected for non-GPS telemetry where useful
Mission Planner support	Low	High	Medium	Medium	Medium	2/5	Out of scope
QGroundControl	High	High	Medium	High	Medium	5/5	Selected

Tech Stack Summary

• Primary language: Python 3.11+ for orchestration, API, data pipelines, tests, and integration.
• Hot path: C++/CUDA/TensorRT modules only where profiling proves Python/OpenCV is insufficient.
• Computer vision: OpenCV, TensorRT, DINOv2/AnyLoc-style descriptors, ALIKED + LightGlue candidate, SIFT/AKAZE legal baseline, DeDoDe experimental fallback.
• State estimation: ESKF in local NED/ENU, prototyped in Python and migrated to C++ Eigen only if latency requires.
• Autopilot: pymavlink GPS_INPUT v1; MAVSDK only for general telemetry if it does not interfere with GPS emission.
• API: FastAPI with generated OpenAPI at /openapi.json, local-first deployment.
• Storage: SQLite/MBTiles-like onboard cache, manifest sidecars, GDAL/Rasterio for COG/GeoTIFF exchange, segmented FDR logs.
• Testing: pytest, SITL ArduPilot, replay harness, Jetson benchmark harness, thermal/memory profiling.

Risk Assessment

Risk	Impact	Mitigation
Noncommercial SuperPoint weights enter product build	High	Reject official Magic Leap weights by default; CI/license scan model artifacts; use ALIKED + LightGlue or SIFT/AKAZE unless a commercial grant exists.
VPR model too slow or memory-heavy	High	Conditional VPR only; benchmark DINOv2-S/tiny variants; CPU FAISS baseline; precompute gallery descriptors.
Cross-view false positives in sparse fields	High	Top-K retrieval, local geometry, freshness gates, ESKF innovation gating, covariance calibration.
ArduPilot EKF source behavior changes by version	High	Pin ArduPilot version and SITL tests; v1 GPS_INPUT only.
Cache format exceeds 10 GB	Medium	Validate provider compression and descriptor index size before implementation freeze.
Python hot path misses 400 ms p95	High	Profile first; move only measured bottlenecks to C++/TensorRT.

Learning / Validation Requirements

Area	Required Proof
Jetson runtime	Benchmark VO, VPR trigger, local matching, ESKF, API, and FDR together under sustained load.
Scheduler latency	Prove bounded latest-frame queue, frame-drop accounting, and timestamp-correct GPS_INPUT under heavy VPR/local-matcher load.
ArduPilot integration	SITL proves GPS_INPUT acceptance, failsafe behavior, and no ODOMETRY emission in v1.
Dataset realism	Add real FC IMU logs or approved SITL IMU generated from the trajectory.
Cache ingestion	Validate COG/GeoTIFF import, local package generation, manifests, and stale-tile rejection.
Safety	Monte Carlo validates false-position and cache-poisoning budgets before production release.