# 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.