# Question Decomposition

## Original Question
Assess solution_draft04.md for weak points, security vulnerabilities, and performance bottlenecks. Produce an improved solution_draft05.md.

## Active Mode
Mode B: Solution Assessment. Draft04 is the 4th iteration. Previous iterations addressed GTSAM factor types, VRAM budget, rotation handling, homography disambiguation, DINOv2 coarse retrieval, concurrency model, session tokens, SSE stability, and satellite matching. Draft04 introduced LiteSAM for satellite fine matching.

## Summary of Problem Context
GPS-denied UAV visual navigation system. Determine GPS coordinates of consecutive aerial photos using visual odometry + satellite geo-referencing + factor graph optimization. Eastern Ukraine region, airplane-type UAVs, camera pointing down, no IMU, up to 3000 photos per flight, RTX 2060 GPU constraint.

## Question Type Classification
- **Primary**: Problem Diagnosis (identify weak points in existing solution)
- **Secondary**: Decision Support (evaluate alternatives for each weak point)

## Research Subject Boundary Definition
- **Population**: GPS-denied UAV navigation systems for fixed-wing aircraft
- **Geography**: Eastern/Southern Ukraine (left of Dnipro River)
- **Timeframe**: Current state-of-the-art (2024-2026)
- **Level**: Production-ready desktop system with RTX 2060 GPU

## Decomposed Sub-Questions

### SQ-1: VO Matcher Regression
Draft04 uses SuperPoint+LightGlue for VO (150-200ms/frame) while draft03 used XFeat (15ms/frame). Was this regression intentional? Should XFeat be restored for VO?

### SQ-2: LiteSAM Maturity & Production Readiness
Is LiteSAM (Oct 2025) mature enough for production? Are pretrained weights reliably available? Has anyone reproduced the claimed results? What is the actual performance on RTX 2060?

### SQ-3: LiteSAM vs Alternatives for Satellite Fine Matching
How does LiteSAM compare to EfficientLoFTR, ASpanFormer, and other semi-dense matchers on satellite-aerial cross-view tasks? Is the claimed 77.3% Hard hit rate reproducible?

### SQ-4: ONNX Optimization Path for LiteSAM
LiteSAM has no ONNX export. What is the performance impact of pure PyTorch vs ONNX on RTX 2060? Can LiteSAM be exported to ONNX/TensorRT?

### SQ-5: VRAM Budget Accuracy
With SuperPoint+LightGlue for VO + DINOv2 + LiteSAM for satellite, what is the true peak VRAM? Does it stay under 6GB on RTX 2060?

### SQ-6: Rotation Invariance Gap
LiteSAM is not rotation-invariant. The 4-rotation retry strategy adds 4x matching time at segment starts. Are there better approaches?

### SQ-7: DINOv2 ViT-S/14 Adequacy
Is ViT-S/14 sufficient for coarse retrieval, or would ViT-B/14 significantly improve recall at the cost of VRAM?

### SQ-8: Security Weak Points
Model weights from Google Drive (supply chain risk). Any new CVEs in dependencies? PyTorch model loading security.

### SQ-9: Segment Reconnection Robustness
How robust is the segment reconnection strategy when multiple disconnected segments exist? Edge cases with >2 segments?

### SQ-10: Satellite Imagery Freshness for Eastern Ukraine
Google Maps imagery for eastern Ukraine conflict zones — how outdated is it? Impact on matching quality?

## Timeliness Sensitivity Assessment

- **Research Topic**: GPS-denied UAV visual navigation with learned feature matchers
- **Sensitivity Level**: 🟠 High
- **Rationale**: LiteSAM published Oct 2025, DINOv2 evolving, LightGlue actively updated, new matchers appearing frequently. Core algorithms (homography, GTSAM, SIFT) are 🟢 Low sensitivity but the learned matcher ecosystem is rapidly evolving.
- **Source Time Window**: 12 months (prioritize 2025-2026 sources)
- **Priority official sources to consult**:
  1. LiteSAM GitHub repo and paper
  2. EfficientLoFTR GitHub
  3. DINOv2 official docs
  4. GTSAM docs
  5. XFeat GitHub
- **Key version information to verify**:
  - LiteSAM: current version, weight availability
  - EfficientLoFTR: latest version
  - DINOv2: model variants
  - GTSAM: v4.2 stability
  - LightGlue-ONNX: latest version