# 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 GPS-denied navigation
- **Research Boundary Match**: ✅ Full match
- **Summary**: VO + satellite correction pipeline for fixed-wing UAV at 1000m+ altitude. Mean error 142.88m over 17km (0.83%). Uses ORB features, centroid-based displacement, Kalman filter smoothing, quadtree for satellite keypoint indexing.
- **Related Sub-question**: A1, B5, C7, D11

## Source #2
- **Title**: SatLoc: Hierarchical Adaptive Fusion Framework for GNSS-denied UAV Localization
- **Link**: https://www.scilit.com/publications/e5cafaf875a49297a62b298a89d5572f
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: UAV localization in GNSS-denied environments
- **Research Boundary Match**: ✅ Full match
- **Summary**: Three-layer fusion: DinoV2 for satellite geo-localization, XFeat for VO, optical flow for velocity. Adaptive confidence-based weighting. <15m error, >90% coverage, 2+ Hz on edge hardware.
- **Related Sub-question**: B4, B5, C8, D12

## Source #3
- **Title**: XFeat: Accelerated Features for Lightweight Image Matching (CVPR 2024)
- **Link**: https://arxiv.org/abs/2404.19174
- **Tier**: L1
- **Publication Date**: 2024-04
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: Edge device feature matching
- **Research Boundary Match**: ✅ Full match
- **Summary**: 5x faster than SuperPoint, runs on CPU at VGA resolution. Sparse and semi-dense matching. TensorRT deployment available for Jetson. Comparable accuracy to SuperPoint.
- **Related Sub-question**: C8, F18, F20

## Source #4
- **Title**: XFeat TensorRT Implementation
- **Link**: https://github.com/PranavNedunghat/XFeatTensorRT
- **Tier**: L2
- **Publication Date**: 2024
- **Timeliness Status**: ✅ Currently valid
- **Summary**: C++ TensorRT implementation of XFeat, tested on Jetson Orin NX 16GB with JetPack 6.0, CUDA 12.2, TensorRT 8.6.
- **Related Sub-question**: C8, F18, F19

## Source #5
- **Title**: SuperPoint+LightGlue TensorRT Deployment
- **Link**: https://github.com/fettahyildizz/superpoint_lightglue_tensorrt
- **Tier**: L2
- **Publication Date**: 2024
- **Timeliness Status**: ✅ Currently valid
- **Summary**: C++ TensorRT implementation of SuperPoint+LightGlue. Production-ready deployment for Jetson platforms.
- **Related Sub-question**: C8, F19

## Source #6
- **Title**: FP8 Quantized LightGlue in TensorRT
- **Link**: https://fabio-sim.github.io/blog/fp8-quantized-lightglue-tensorrt-nvidia-model-optimizer/
- **Tier**: L2
- **Publication Date**: 2026
- **Timeliness Status**: ✅ Currently valid
- **Summary**: Up to ~6x speedup with FP8 quantization. Requires Hopper/Ada Lovelace GPUs (not available on Jetson Orin Nano Ampere). FP16 is the best available precision for Orin Nano.
- **Related Sub-question**: F19

## Source #7
- **Title**: NVIDIA JetPack 6.2 Release Notes
- **Link**: https://docs.nvidia.com/jetson/archives/jetpack-archived/jetpack-62/release-notes/index.html
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Summary**: CUDA 12.6.10, TensorRT 10.3.0, cuDNN 9.3. Super Mode for Orin Nano: up to 2x inference performance, 50% memory bandwidth boost. Power modes: 15W, 25W, MAXN SUPER.
- **Related Sub-question**: F18, F19, F20

## Source #8
- **Title**: cuda-efficient-features (GPU feature detection benchmarks)
- **Link**: https://github.com/fixstars/cuda-efficient-features
- **Tier**: L2
- **Publication Date**: 2024
- **Timeliness Status**: ✅ Currently valid
- **Summary**: 4K detection: 12ms on Jetson Xavier. 8K: 27.5ms. 40K keypoints extraction: 20-25ms on Xavier. Orin Nano Super should be comparable or better.
- **Related Sub-question**: F20

## Source #9
- **Title**: Adaptive Covariance Hybrid EKF/UKF for Visual-Inertial Odometry
- **Link**: https://arxiv.org/abs/2512.17505
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Summary**: Hybrid EKF/UKF achieves 49% better position accuracy, 57% better rotation accuracy than ESKF alone, at 48% lower computational cost than full UKF. Includes adaptive sensor confidence scoring.
- **Related Sub-question**: E15

## Source #10
- **Title**: SIFT+LightGlue for UAV Image Mosaicking (ISPRS 2025)
- **Link**: https://isprs-archives.copernicus.org/articles/XLVIII-2-W11-2025/169/2025/
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Summary**: SIFT+LightGlue outperforms SuperPoint+LightGlue for UAV mosaicking across diverse scenarios. Superior in both low-texture and high-texture environments.
- **Related Sub-question**: C8, D12

## Source #11
- **Title**: UAVision - GNSS-Denied UAV Visual Localization System
- **Link**: https://github.com/ArboriseRS/UAVision
- **Tier**: L4
- **Publication Date**: 2024-2025
- **Timeliness Status**: ✅ Currently valid
- **Summary**: Open-source system using LightGlue for map matching. Includes image processing modules and visualization.
- **Related Sub-question**: A2

## Source #12
- **Title**: TerboucheHacene/visual_localization
- **Link**: https://github.com/TerboucheHacene/visual_localization
- **Tier**: L4
- **Publication Date**: 2024
- **Timeliness Status**: ✅ Currently valid
- **Summary**: Vision-based GNSS-free localization with SuperPoint/SuperGlue/GIM matching. Optimized VO + satellite image matching hybrid pipeline. Learning-based matchers for natural environments.
- **Related Sub-question**: A2, D12

## Source #13
- **Title**: GNSS-Denied Geolocalization with Terrain Constraints
- **Link**: https://github.com/yfs90/gnss-denied-uav-geolocalization
- **Tier**: L4
- **Publication Date**: 2024
- **Timeliness Status**: ✅ Currently valid
- **Summary**: No altimeters/IMU required, uses image matching + terrain constraints. GPS-comparable accuracy for day/night across varied terrain.
- **Related Sub-question**: A2

## Source #14
- **Title**: Google Maps Tile API Documentation
- **Link**: https://developers.google.com/maps/documentation/tile/satellite
- **Tier**: L1
- **Publication Date**: Current
- **Timeliness Status**: ✅ Currently valid
- **Summary**: Zoom levels 0-22. Satellite tiles via HTTPS. Session tokens required. Bulk download possible but subject to usage policies.
- **Related Sub-question**: D13

## Source #15
- **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
- **Timeliness Status**: ✅ Currently valid
- **Summary**: Trajectory-level optimization rather than per-frame matching. Optimizes entire trajectory against satellite reference for improved accuracy.
- **Related Sub-question**: B4, D11

## Source #16
- **Title**: GSD Estimation for UAV Photogrammetry
- **Link**: https://blog.truegeometry.com/calculators/UAV_photogrammetry_workflows_calculation.html
- **Tier**: L3
- **Publication Date**: Current
- **Timeliness Status**: ✅ Currently valid
- **Summary**: GSD = (sensor_width × altitude) / (focal_length × image_width). For our case: (23.5mm × 400m) / (25mm × 6252) = 0.06 m/pixel.
- **Related Sub-question**: C9
