# Question Decomposition

## Original Question
Research the GPS-denied onboard navigation problem for a fixed-wing UAV and find the best solution architecture. The system must determine frame-center GPS coordinates using visual odometry, satellite image matching, and IMU fusion — all running on a Jetson Orin Nano Super (8GB shared memory, 67 TOPS).

## Active Mode
Mode A Phase 2 — Initial Research (Problem & Solution)

## Rationale
No existing solution drafts. Full problem decomposition and solution research needed.

## Problem Context Summary (from INPUT_DIR)
- **Platform**: Fixed-wing UAV, camera pointing down (not stabilized), 400m altitude max 1km
- **Camera**: ADTi Surveyor Lite 26S v2, 26MP (6252x4168), focal length 25mm, sensor width 23.5mm
- **GSD at 400m**: ~6cm/pixel, footprint ~375x250m
- **Frame rate**: 3 fps (interval ~333ms, real-world could be 400-500ms)
- **Photo count**: 500-3000 per flight
- **IMU**: Available at high rate
- **Initial GPS**: Known; GPS may be denied/spoofed during flight
- **Satellite reference**: Pre-uploaded Google Maps tiles
- **Hardware**: Jetson Orin Nano Super, 8GB shared memory, 67 TOPS
- **Region**: Eastern/southern Ukraine (conflict zone)
- **Key challenge**: Reconnecting disconnected route segments after sharp turns

## Question Type Classification
**Decision Support** — we need to evaluate and select the best architectural approach and component technologies for each part of the pipeline.

## Research Subject Boundary Definition

| Dimension | Boundary |
|-----------|----------|
| Population | Fixed-wing UAVs with nadir cameras at 200-1000m altitude |
| Geography | Rural/semi-urban terrain in eastern Ukraine |
| Timeframe | Current state-of-the-art (2023-2026) |
| Level | Edge computing (Jetson-class, 8GB memory), real-time processing |

## Decomposed Sub-Questions

### A. Existing/Competitor Solutions
1. What existing systems solve GPS-denied UAV visual navigation?
2. What open-source implementations exist for VO + satellite matching?
3. What commercial/military solutions address this problem?

### B. Architecture Components
4. What is the optimal pipeline architecture (sequential vs parallel, streaming)?
5. How should VO, satellite matching, and IMU fusion be combined (loosely vs tightly coupled)?
6. How to handle disconnected route segments (the core architectural challenge)?

### C. Visual Odometry Component
7. What VO algorithms work best for aerial nadir imagery on edge hardware?
8. What feature extractors/matchers are optimal for Jetson (SuperPoint, ORB, XFeat)?
9. How to handle scale estimation with known altitude and camera parameters?
10. What is the optimal image downsampling strategy for 26MP on 8GB memory?

### D. Satellite Image Matching Component
11. How to efficiently match UAV frames against pre-loaded satellite tiles?
12. What cross-view matching methods work for aerial-to-satellite registration?
13. How to preprocess and index satellite tiles for fast retrieval?
14. How to handle resolution mismatch (6cm UAV vs 50cm+ satellite)?

### E. IMU Fusion Component
15. How to fuse IMU data with visual estimates (EKF, UKF, factor graph)?
16. How to use IMU for dead-reckoning during feature-less frames?
17. How to use IMU for image orientation compensation (non-stabilized camera)?

### F. Edge Optimization
18. How to fit the full pipeline in 8GB shared memory?
19. What TensorRT optimizations are available for feature extractors?
20. How to achieve <5s per frame on Jetson Orin Nano Super?

### G. API & Streaming
21. What is the best approach for REST API + SSE on Python/Jetson?
22. How to implement progressive result refinement?

## Timeliness Sensitivity Assessment

- **Research Topic**: GPS-denied UAV visual navigation with edge processing
- **Sensitivity Level**: 🟠 High
- **Rationale**: Deep learning feature matchers (SuperPoint, LightGlue, XFeat) and edge inference frameworks (TensorRT) evolve rapidly. Jetson Orin Nano Super is a recent (Dec 2024) product. Cross-view geo-localization is an active research area.
- **Source Time Window**: 12 months (prioritize 2025-2026)
- **Priority official sources to consult**:
  1. NVIDIA Jetson documentation and benchmarks
  2. OpenCV / kornia / hloc official docs
  3. Recent papers on cross-view geo-localization (CVPR, ECCV, ICCV 2024-2025)
- **Key version information to verify**:
  - JetPack SDK: Current version ____
  - SuperPoint/LightGlue: Latest available for TensorRT ____
  - XFeat: Version and Jetson compatibility ____
