# Source Registry

## Source #1
- **Title**: Visual Odometry in GPS-Denied Zones for Fixed-Wing UAV with Reduced Accumulative Error Based on Satellite Imagery
- **Link**: https://www.mdpi.com/2076-3417/14/16/7420
- **Tier**: L1
- **Publication Date**: 2024-08-22
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: Fixed-wing UAV navigation researchers
- **Research Boundary Match**: ✅ Full match (fixed-wing, high altitude, satellite matching)
- **Summary**: VO + satellite image correction achieves 142.88m mean error over 17km at >1000m altitude using ORB + AKAZE. Uses IMU for heading and barometer for altitude. Error rate 0.83% of total distance.
- **Related Sub-question**: 1, 2

## Source #2
- **Title**: Optimized visual odometry and satellite image matching-based localization for UAVs in GPS-denied environments (ITU Thesis)
- **Link**: https://polen.itu.edu.tr/items/1fe1e872-7cea-44d8-a8de-339e4587bee6
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: UAV navigation researchers
- **Research Boundary Match**: ⚠️ Partial overlap (multirotor at 30-100m, but same VO+SIM methodology)
- **Summary**: ORB-SLAM3 + SuperPoint/SuperGlue/GIM achieves GPS-level accuracy. VO module: ±2m local accuracy. SIM module: 93% matching success rate. Demonstrated on DJI Mavic Air 2 at 30-100m.
- **Related Sub-question**: 1, 2, 4

## Source #3
- **Title**: NaviLoc: Trajectory-Level Visual Localization for GNSS-Denied UAV Navigation
- **Link**: https://www.mdpi.com/2504-446X/10/2/97
- **Tier**: L1
- **Publication Date**: 2025-12
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: UAV navigation / VPR researchers
- **Research Boundary Match**: ⚠️ Partial overlap (50-150m altitude, uses VIO not pure VO)
- **Summary**: Achieves 19.5m Mean Localization Error at 50-150m altitude. Runs at 9 FPS on Raspberry Pi 5. 16x improvement over AnyLoc-VLAD, 32x over raw VIO drift. Training-free system.
- **Related Sub-question**: 1, 7

## Source #4
- **Title**: LightGlue: Local Feature Matching at Light Speed (GitHub + ICCV 2023)
- **Link**: https://github.com/cvg/LightGlue
- **Tier**: L1
- **Publication Date**: 2023 (actively maintained through 2025)
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: Computer vision practitioners
- **Research Boundary Match**: ✅ Full match (core component)
- **Summary**: ~20-34ms per image pair on RTX 2080Ti. Adaptive pruning for fast inference. 2-4x speedup with PyTorch compilation.
- **Related Sub-question**: 3, 4

## Source #5
- **Title**: Efficient image matching for UAV visual navigation via DALGlue
- **Link**: https://www.nature.com/articles/s41598-025-21602-5
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: UAV navigation researchers
- **Research Boundary Match**: ✅ Full match
- **Summary**: DALGlue achieves 11.8% improvement over LightGlue on matching accuracy. Uses dual-tree complex wavelet preprocessing + linear attention for real-time performance.
- **Related Sub-question**: 3, 4

## Source #6
- **Title**: Deep-UAV SLAM: SuperPoint and SuperGlue enhanced SLAM
- **Link**: https://isprs-archives.copernicus.org/articles/XLVIII-1-W5-2025/177/2025/
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: UAV SLAM researchers
- **Research Boundary Match**: ✅ Full match
- **Summary**: Replacing ORB-SLAM3's ORB features with SuperPoint+SuperGlue improved robustness and accuracy in aerial RGB scenarios.
- **Related Sub-question**: 4, 5

## Source #7
- **Title**: SCAR: Satellite Imagery-Based Calibration for Aerial Recordings
- **Link**: https://arxiv.org/html/2602.16349v1
- **Tier**: L1
- **Publication Date**: 2026-02
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: Aerial/satellite vision researchers
- **Research Boundary Match**: ✅ Full match
- **Summary**: Long-term auto-calibration refinement by aligning aerial images with 2D-3D correspondences from orthophotos and elevation models.
- **Related Sub-question**: 1, 5

## Source #8
- **Title**: Google Maps satellite imagery coverage and update frequency
- **Link**: https://ongeo-intelligence.com/blog/how-often-does-google-maps-update-satellite-images
- **Tier**: L3
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: GIS practitioners
- **Research Boundary Match**: ✅ Full match
- **Summary**: Conflict zones like eastern Ukraine face 2-5+ year update cycles. Imagery may be intentionally limited or blurred.
- **Related Sub-question**: 7

## Source #9
- **Title**: Satellite Mapping Services comparison 2025
- **Link**: https://ts2.tech/en/exploring-the-world-from-above-top-satellite-mapping-services-for-web-mobile-in-2025/
- **Tier**: L3
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: Developers, GIS practitioners
- **Research Boundary Match**: ✅ Full match
- **Summary**: Google: $200/month free credit, sub-meter resolution. Mapbox: Maxar imagery, generous free tier. Maxar SecureWatch: 30cm resolution, enterprise pricing. Planet: daily 3-4m imagery.
- **Related Sub-question**: 7

## Source #10
- **Title**: Scale Estimation for Monocular Visual Odometry Using Reliable Camera Height
- **Link**: https://ieeexplore.ieee.org/document/9945178/
- **Tier**: L1
- **Publication Date**: 2022
- **Timeliness Status**: ✅ Currently valid (fundamental method)
- **Target Audience**: VO researchers
- **Research Boundary Match**: ✅ Full match
- **Summary**: Known camera height/altitude resolves scale ambiguity in monocular VO. Essential for systems without IMU.
- **Related Sub-question**: 2

## Source #11
- **Title**: Cross-View Geo-Localization benchmarks (SSPT, MA metrics)
- **Link**: https://www.mdpi.com/1424-8220/24/12/3719
- **Tier**: L1
- **Publication Date**: 2024
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: VPR/geo-localization researchers
- **Research Boundary Match**: ⚠️ Partial overlap (general cross-view, not UAV-specific)
- **Summary**: SSPT achieved 84.40% RDS on UL14 dataset. MA improvements: +12% at 3m, +12% at 5m, +10% at 20m thresholds.
- **Related Sub-question**: 1

## Source #12
- **Title**: ORB-SLAM3 GPU Acceleration Performance
- **Link**: https://arxiv.org/html/2509.10757v1
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: SLAM/VO engineers
- **Research Boundary Match**: ✅ Full match
- **Summary**: GPU acceleration achieves 2.8x speedup on desktop systems. 30 FPS achievable on Jetson TX2. Feature extraction up to 3x speedup with CUDA.
- **Related Sub-question**: 3
