# Fact Cards

## Fact #1
- **Statement**: VO + satellite image correction achieves ~142.88m mean error over 17km flight at >1000m altitude using ORB features and AKAZE satellite matching. Error rate: 0.83% of total distance. This system uses IMU for heading and barometer for altitude.
- **Source**: Source #1 — https://www.mdpi.com/2076-3417/14/16/7420
- **Phase**: Phase 1
- **Target Audience**: Fixed-wing UAV at high altitude (>1000m)
- **Confidence**: ✅ High (peer-reviewed, real-world flight data)
- **Related Dimension**: GPS accuracy, drift correction

## Fact #2
- **Statement**: ORB-SLAM3 monocular mode with optimized parameters achieves ±2m local accuracy for visual odometry. Scale ambiguity and drift remain for long flights.
- **Source**: Source #2 — ITU Thesis
- **Phase**: Phase 1
- **Target Audience**: UAV navigation (30-100m altitude, multirotor)
- **Confidence**: ✅ High (thesis with experimental validation)
- **Related Dimension**: VO accuracy, scale ambiguity

## Fact #3
- **Statement**: Combined VO + Satellite Image Matching (SIM) with SuperPoint/SuperGlue/GIM achieves 93% matching success rate and "GPS-level accuracy" at 30-100m altitude.
- **Source**: Source #2 — ITU Thesis
- **Phase**: Phase 1
- **Target Audience**: Low-altitude UAV (30-100m)
- **Confidence**: ✅ High
- **Related Dimension**: Registration rate, satellite matching

## Fact #4
- **Statement**: NaviLoc achieves 19.5m Mean Localization Error at 50-150m altitude, runs at 9 FPS on Raspberry Pi 5. 16x improvement over AnyLoc-VLAD. Training-free system.
- **Source**: Source #3 — NaviLoc paper
- **Phase**: Phase 1
- **Target Audience**: Low-altitude UAV (50-150m) in rural areas
- **Confidence**: ✅ High (peer-reviewed)
- **Related Dimension**: GPS accuracy, processing speed

## Fact #5
- **Statement**: LightGlue inference: ~20-34ms per image pair on RTX 2080Ti for 1024 keypoints. 2-4x speedup possible with PyTorch compilation and TensorRT.
- **Source**: Source #4 — LightGlue GitHub Issues
- **Phase**: Phase 1
- **Target Audience**: All GPU-accelerated vision systems
- **Confidence**: ✅ High (official repository benchmarks)
- **Related Dimension**: Processing speed

## Fact #6
- **Statement**: SuperPoint+SuperGlue replacing ORB features in SLAM improves robustness and accuracy for aerial RGB imagery over classical handcrafted features.
- **Source**: Source #6 — ISPRS 2025
- **Phase**: Phase 1
- **Target Audience**: UAV SLAM researchers
- **Confidence**: ✅ High (peer-reviewed)
- **Related Dimension**: Feature matching quality

## Fact #7
- **Statement**: Eastern Ukraine / conflict zones may have 2-5+ year old satellite imagery on Google Maps. Imagery may be intentionally limited, blurred, or restricted for security reasons.
- **Source**: Source #8
- **Phase**: Phase 1
- **Target Audience**: Ukraine conflict zone operations
- **Confidence**: ⚠️ Medium (general reporting, not Ukraine-specific verification)
- **Related Dimension**: Satellite imagery quality

## Fact #8
- **Statement**: Maxar SecureWatch offers 30cm resolution with ~3M km² new imagery daily. Mapbox uses Maxar's Vivid imagery with sub-meter resolution. Google Maps offers sub-meter detail in urban areas but 1-3m in rural areas.
- **Source**: Source #9
- **Phase**: Phase 1
- **Target Audience**: All satellite imagery users
- **Confidence**: ✅ High
- **Related Dimension**: Satellite providers, cost

## Fact #9
- **Statement**: Known camera height/altitude resolves scale ambiguity in monocular VO. The pixel-to-meter conversion is s = H / f × sensor_pixel_size, enabling metric reconstruction without IMU.
- **Source**: Source #10
- **Phase**: Phase 1
- **Target Audience**: Monocular VO systems
- **Confidence**: ✅ High (fundamental geometric relationship)
- **Related Dimension**: No-IMU compensation

## Fact #10
- **Statement**: Camera heading (yaw) can be estimated from consecutive frame feature matching by decomposing the homography or essential matrix. Pitch/roll can be estimated from horizon detection or vanishing points. Without IMU, these estimates are noisier but functional.
- **Source**: Multiple vision-based heading estimation papers
- **Phase**: Phase 1
- **Target Audience**: Vision-only navigation systems
- **Confidence**: ⚠️ Medium (well-established but accuracy varies)
- **Related Dimension**: No-IMU compensation

## Fact #11
- **Statement**: GSD at 400m with 25mm/23.5mm sensor/6252px = 6.01 cm/pixel. Ground footprint: 376m × 250m. At 100m photo interval, consecutive overlap is 60-73%.
- **Source**: Calculated from problem data using standard GSD formula
- **Phase**: Phase 1
- **Target Audience**: This specific system
- **Confidence**: ✅ High (deterministic calculation)
- **Related Dimension**: Image coverage, overlap

## Fact #12
- **Statement**: GPU-accelerated ORB-SLAM3 achieves 2.8x speedup on desktop systems. 30 FPS possible on Jetson TX2. Feature extraction speedup up to 3x with CUDA-optimized pipelines.
- **Source**: Source #12
- **Phase**: Phase 1
- **Target Audience**: GPU-equipped systems
- **Confidence**: ✅ High
- **Related Dimension**: Processing speed

## Fact #13
- **Statement**: Without IMU, the Mateos-Ramirez paper (Source #1) would lose: (a) yaw angle for rotation compensation, (b) fallback when feature matching fails. Their 142.88m error would likely be significantly higher without IMU heading data.
- **Source**: Inference from Source #1 methodology
- **Phase**: Phase 1
- **Target Audience**: This specific system
- **Confidence**: ⚠️ Medium (reasoned inference)
- **Related Dimension**: No-IMU impact

## Fact #14
- **Statement**: DALGlue achieves 11.8% improvement over LightGlue on matching accuracy while maintaining real-time performance through dual-tree complex wavelet preprocessing and linear attention.
- **Source**: Source #5
- **Phase**: Phase 1
- **Target Audience**: Feature matching systems
- **Confidence**: ✅ High (peer-reviewed, 2025)
- **Related Dimension**: Feature matching quality

## Fact #15
- **Statement**: Cross-view geo-localization benchmarks show MA@20m improving by +10% with latest methods (SSPT). RDS metric at 84.40% indicates reliable spatial positioning.
- **Source**: Source #11
- **Phase**: Phase 1
- **Target Audience**: Cross-view matching researchers
- **Confidence**: ✅ High
- **Related Dimension**: Cross-view matching accuracy
