# Fact Cards

## Fact #1
- **Statement**: XFeat achieves up to 5x faster inference than SuperPoint while maintaining comparable accuracy for pose estimation. It runs in real-time on CPU at VGA resolution.
- **Source**: Source #3 (CVPR 2024 paper)
- **Phase**: Phase 2
- **Target Audience**: Edge device deployments
- **Confidence**: ✅ High
- **Related Dimension**: Feature Extraction

## Fact #2
- **Statement**: XFeat TensorRT implementation exists and is tested on Jetson Orin NX 16GB with JetPack 6.0, CUDA 12.2, TensorRT 8.6.
- **Source**: Source #4
- **Phase**: Phase 2
- **Target Audience**: Jetson platform deployment
- **Confidence**: ✅ High
- **Related Dimension**: Feature Extraction, Edge Optimization

## Fact #3
- **Statement**: SatLoc framework achieves <15m absolute localization error with >90% trajectory coverage at 2+ Hz on edge hardware, using DinoV2 for satellite matching, XFeat for VO, and optical flow for velocity.
- **Source**: Source #2
- **Phase**: Phase 2
- **Target Audience**: GNSS-denied UAV localization
- **Confidence**: ⚠️ Medium (paper details not fully accessible)
- **Related Dimension**: Overall Architecture, Accuracy

## Fact #4
- **Statement**: Mateos-Ramirez et al. achieved 142.88m mean error over 17km (0.83% error rate) with VO + satellite correction on a fixed-wing UAV at 1000m+ altitude. Without satellite correction, error accumulated to 850m+ over 17km.
- **Source**: Source #1
- **Phase**: Phase 2
- **Target Audience**: Fixed-wing UAV at high altitude
- **Confidence**: ✅ High
- **Related Dimension**: Accuracy, Architecture

## Fact #5
- **Statement**: VIO systems typically drift 0.8-1% of distance traveled. Between satellite corrections at 1km intervals, expected drift is ~10m.
- **Source**: Multiple sources (arxiv VIO benchmarks)
- **Phase**: Phase 2
- **Target Audience**: Aerial VIO systems
- **Confidence**: ✅ High
- **Related Dimension**: VO Drift

## Fact #6
- **Statement**: Jetson Orin Nano Super: 8GB LPDDR5 shared memory, 1024 CUDA cores, 32 Tensor Cores, 102 GB/s bandwidth, 67 TOPS INT8. JetPack 6.2: CUDA 12.6.10, TensorRT 10.3.0.
- **Source**: Source #7
- **Phase**: Phase 2
- **Target Audience**: Hardware specification
- **Confidence**: ✅ High
- **Related Dimension**: Edge Optimization

## Fact #7
- **Statement**: CUDA-accelerated feature detection at 4K (3840x2160): ~12ms on Jetson Xavier. At 8K: ~27.5ms. Descriptor extraction for 40K keypoints: ~20-25ms on Xavier. Orin Nano Super has comparable or slightly better compute.
- **Source**: Source #8
- **Phase**: Phase 2
- **Target Audience**: Jetson GPU performance
- **Confidence**: ✅ High
- **Related Dimension**: Processing Time

## Fact #8
- **Statement**: Hybrid EKF/UKF achieves 49% better position accuracy than ESKF alone at 48% lower computational cost than full UKF. Includes adaptive sensor confidence scoring based on image entropy and motion blur.
- **Source**: Source #9
- **Phase**: Phase 2
- **Target Audience**: VIO fusion
- **Confidence**: ✅ High
- **Related Dimension**: Sensor Fusion

## Fact #9
- **Statement**: SIFT+LightGlue outperforms SuperPoint+LightGlue for UAV mosaicking across diverse scenarios (low-texture agricultural and high-texture urban).
- **Source**: Source #10
- **Phase**: Phase 2
- **Target Audience**: UAV image matching
- **Confidence**: ✅ High
- **Related Dimension**: Feature Matching

## Fact #10
- **Statement**: GSD for our system at 400m: (23.5mm × 400m) / (25mm × 6252px) = 0.060 m/pixel. Image footprint: 6252 × 0.06 = 375m width, 4168 × 0.06 = 250m height.
- **Source**: Source #16 + camera parameters
- **Phase**: Phase 2
- **Target Audience**: Our specific system
- **Confidence**: ✅ High
- **Related Dimension**: Scale Estimation

## Fact #11
- **Statement**: Google Maps satellite tiles available via Tile API at zoom levels 0-22. Max zoom varies by region. For eastern Ukraine, zoom 18 (~0.6 m/px) is typically available; zoom 19 (~0.3 m/px) may not be.
- **Source**: Source #14
- **Phase**: Phase 2
- **Target Audience**: Satellite imagery
- **Confidence**: ⚠️ Medium (exact zoom availability for eastern Ukraine unverified)
- **Related Dimension**: Satellite Reference

## Fact #12
- **Statement**: FP8 quantization for LightGlue requires Hopper/Ada GPUs. Jetson Orin Nano uses Ampere architecture — limited to FP16 as best TensorRT precision.
- **Source**: Source #6, Source #7
- **Phase**: Phase 2
- **Target Audience**: Jetson optimization
- **Confidence**: ✅ High
- **Related Dimension**: Edge Optimization

## Fact #13
- **Statement**: SuperPoint+LightGlue TensorRT C++ deployment is available and production-tested. ONNX Runtime path achieves 2-4x speedup over compiled PyTorch.
- **Source**: Source #5, Source #6
- **Phase**: Phase 2
- **Target Audience**: Production deployment
- **Confidence**: ✅ High
- **Related Dimension**: Feature Matching, Edge Optimization

## Fact #14
- **Statement**: Cross-view matching (UAV-to-satellite) is fundamentally harder than same-view matching due to extreme viewpoint differences. Deep learning embeddings (DinoV2, CLIP-based) are the state-of-the-art for coarse retrieval. Local features are used for fine alignment.
- **Source**: Multiple (Sources #2, #12, #15)
- **Phase**: Phase 2
- **Target Audience**: Cross-view geo-localization
- **Confidence**: ✅ High
- **Related Dimension**: Satellite Matching

## Fact #15
- **Statement**: Quadtree spatial indexing enables O(log n) nearest-neighbor lookup for satellite keypoints. Combined with GeoHash for fast region encoding, this is the standard approach for tile management.
- **Source**: Sources #1, #14
- **Phase**: Phase 2
- **Target Audience**: Spatial indexing
- **Confidence**: ✅ High
- **Related Dimension**: Satellite Tile Management
