# Question Decomposition — AC & Restrictions Assessment

## Original Question
How realistic are the acceptance criteria and restrictions for a GPS-denied visual navigation system for fixed-wing UAV imagery?

## Active Mode
Mode A, Phase 1: AC & Restrictions Assessment

## Question Type
Knowledge Organization + Decision Support

## Research Subject Boundary Definition

| Dimension | Boundary |
|-----------|----------|
| **Platform** | Fixed-wing UAV, airplane type, not multirotor |
| **Geography** | Eastern/southern Ukraine, left of Dnipro River (conflict zone, ~48.27°N, 37.38°E based on sample data) |
| **Altitude** | ≤ 1km, sample data at 400m |
| **Sensor** | Monocular RGB camera, 26MP, no IMU, no LiDAR |
| **Processing** | Ground-based desktop/laptop with NVIDIA RTX 2060+ GPU |
| **Time Window** | Current state-of-the-art (2024-2026) |

## Problem Context Summary

The system must determine GPS coordinates of consecutive aerial photo centers using only:
- Known starting GPS coordinates
- Known camera parameters (25mm focal, 23.5mm sensor, 6252×4168 resolution)
- Known flight altitude (≤1km, sample: 400m)
- Consecutive photos taken within ~100m of each other
- Satellite imagery (Google Maps) for ground reference

Key constraints: NO IMU data, camera not auto-stabilized, potentially outdated satellite imagery for conflict zone.

**Ground Sample Distance (GSD) at 400m altitude**:
- GSD = (400 × 23.5) / (25 × 6252) ≈ 0.060 m/pixel (6 cm/pixel)
- Ground footprint: ~376m × 250m per image
- Estimated consecutive overlap: 60-73% (depending on camera orientation relative to flight direction)

## Sub-Questions for AC Assessment

1. What GPS accuracy is achievable with VO + satellite matching at 400m altitude with 26MP camera?
2. How does the absence of IMU affect accuracy and what compensations exist?
3. What processing speed is achievable per image on RTX 2060+ for the required pipeline?
4. What image registration rates are achievable with deep learning matchers?
5. What reprojection errors are typical for modern feature matching?
6. How do sharp turns and route disconnections affect VO systems?
7. What satellite imagery quality is available for the operational area?
8. What domain-specific acceptance criteria might be missing?

## Timeliness Sensitivity Assessment

- **Research Topic**: GPS-denied visual navigation using deep learning feature matching
- **Sensitivity Level**: 🟠 High
- **Rationale**: Deep learning feature matchers (SuperPoint, LightGlue, GIM) are evolving rapidly; new methods appear quarterly. Satellite imagery providers update pricing and coverage frequently.
- **Source Time Window**: 12 months (2024-2026)
- **Priority official sources to consult**:
  1. LightGlue GitHub repository (cvg/LightGlue)
  2. ORB-SLAM3 documentation
  3. Recent MDPI/IEEE papers on GPS-denied UAV navigation
- **Key version information to verify**:
  - LightGlue: Current release and performance benchmarks
  - SuperPoint: Compatibility and inference speed
  - ORB-SLAM3: Monocular mode capabilities
