# Source Registry

## Source #1
- **Title**: LiteSAM: Lightweight and Robust Feature Matching for Satellite and Aerial Imagery
- **Link**: https://www.mdpi.com/2072-4292/17/19/3349
- **Tier**: L1
- **Publication Date**: 2025-10-01
- **Timeliness Status**: ✅ Currently valid
- **Version Info**: LiteSAM v1.0; benchmarked on Jetson AGX Orin (JetPack 5.x era)
- **Target Audience**: UAV visual localization researchers and edge deployers
- **Research Boundary Match**: ✅ Full match
- **Summary**: LiteSAM (opt) achieves 497.49ms on Jetson AGX Orin at 1184px input. 6.31M params. RMSE@30 = 17.86m on UAV-VisLoc. Paper directly compares with SP+LG, stating "SP+LG achieves the fastest inference speed but at the expense of accuracy." Section 4.9 shows resolution vs speed tradeoff on RTX 3090Ti.
- **Related Sub-question**: Q2 (LiteSAM speed), Q4 (SP+LG for satellite matching)

## Source #2
- **Title**: cuVSLAM: CUDA accelerated visual odometry and mapping
- **Link**: https://arxiv.org/abs/2506.04359
- **Tier**: L1
- **Publication Date**: 2025-06 (paper), v15.0.0 released 2026-03-10
- **Timeliness Status**: ✅ Currently valid
- **Version Info**: cuVSLAM v15.0.0 / PyCuVSLAM v15.0.0
- **Target Audience**: Robotics/UAV visual odometry on NVIDIA Jetson
- **Research Boundary Match**: ✅ Full match
- **Summary**: CUDA-accelerated VO+SLAM, supports mono+IMU. 116fps on Jetson Orin Nano 8GB at 720p. <1% trajectory error on KITTI. <5cm on EuRoC.
- **Related Sub-question**: Q1 (SP+LG vs cuVSLAM)

## Source #3
- **Title**: Intermodalics — NVIDIA Isaac ROS In-Depth: cuVSLAM and the DP3.1 Release
- **Link**: https://www.intermodalics.ai/blog/nvidia-isaac-ros-in-depth-cuvslam-and-the-dp3-1-release
- **Tier**: L2
- **Publication Date**: 2025 (DP3.1 release)
- **Timeliness Status**: ✅ Currently valid
- **Version Info**: cuVSLAM v11 (DP3.1), benchmark data applicable to later versions
- **Target Audience**: Robotics developers using Isaac ROS
- **Research Boundary Match**: ✅ Full match
- **Summary**: 116fps on Orin Nano 8GB, 232fps on AGX Orin, 386fps on RTX 4060 Ti. Outperforms ORB-SLAM2 on KITTI.
- **Related Sub-question**: Q1

## Source #4
- **Title**: Accelerating LightGlue Inference with ONNX Runtime and TensorRT
- **Link**: https://fabio-sim.github.io/blog/accelerating-lightglue-inference-onnx-runtime-tensorrt/
- **Tier**: L2
- **Publication Date**: 2024-07-17
- **Timeliness Status**: ✅ Currently valid
- **Version Info**: torch 2.4.0, TensorRT 10.2.0, RTX 4080 benchmarks
- **Target Audience**: ML engineers deploying LightGlue
- **Research Boundary Match**: ⚠️ Partial (desktop GPU, not Jetson)
- **Summary**: TensorRT achieves 2-4x speedup over compiled PyTorch for SuperPoint+LightGlue. Full pipeline benchmarks on RTX 4080. TensorRT has 3840 keypoint limit. No Jetson-specific benchmarks provided.
- **Related Sub-question**: Q1

## Source #5
- **Title**: LightGlue-with-FlashAttentionV2-TensorRT (Jetson Orin NX 8GB)
- **Link**: https://github.com/qdLMF/LightGlue-with-FlashAttentionV2-TensorRT
- **Tier**: L4
- **Publication Date**: 2025-02
- **Timeliness Status**: ✅ Currently valid
- **Version Info**: TensorRT 8.5.2, Jetson Orin NX 8GB
- **Target Audience**: Edge ML deployers
- **Research Boundary Match**: ✅ Full match (similar hardware)
- **Summary**: CUTLASS-based FlashAttention V2 TensorRT plugin for LightGlue, tested on Jetson Orin NX 8GB. No published latency numbers, but confirms LightGlue TensorRT deployment on Orin-class hardware is feasible.
- **Related Sub-question**: Q1

## Source #6
- **Title**: vo_lightglue — Visual Odometry with LightGlue
- **Link**: https://github.com/himadrir/vo_lightglue
- **Tier**: L4
- **Publication Date**: 2024
- **Timeliness Status**: ✅ Currently valid
- **Version Info**: N/A
- **Target Audience**: VO researchers
- **Research Boundary Match**: ⚠️ Partial (desktop, KITTI dataset)
- **Summary**: SP+LG achieves 10fps on KITTI dataset (desktop GPU). Odometric error ~1% vs 3.5-4.1% for FLANN-based matching. Much slower than cuVSLAM.
- **Related Sub-question**: Q1

## Source #7
- **Title**: ForestVO: Enhancing Visual Odometry in Forest Environments through ForestGlue
- **Link**: https://arxiv.org/html/2504.01261v1
- **Tier**: L1
- **Publication Date**: 2025-04
- **Timeliness Status**: ✅ Currently valid
- **Version Info**: N/A
- **Target Audience**: VO researchers
- **Research Boundary Match**: ⚠️ Partial (forest environment, not nadir UAV)
- **Summary**: SP+LG VO pipeline achieves 1.09m avg relative pose error, KITTI score 2.33%. Uses 512 keypoints (reduced from 2048) to cut compute. Outperforms DSO by 40%.
- **Related Sub-question**: Q1

## Source #8
- **Title**: SuperPoint-SuperGlue-TensorRT (C++ deployment)
- **Link**: https://github.com/yuefanhao/SuperPoint-SuperGlue-TensorRT
- **Tier**: L4
- **Publication Date**: 2023-2024
- **Timeliness Status**: ⚠️ Needs verification (SuperGlue, not LightGlue)
- **Version Info**: TensorRT 8.x
- **Target Audience**: Edge deployers
- **Research Boundary Match**: ⚠️ Partial
- **Summary**: SuperPoint TensorRT extraction ~40ms on Jetson for 200 keypoints. C++ implementation.
- **Related Sub-question**: Q1

## Source #9
- **Title**: Comparative Analysis of Advanced Feature Matching Algorithms in HSR Satellite Stereo
- **Link**: https://arxiv.org/abs/2405.06246
- **Tier**: L1
- **Publication Date**: 2024-05
- **Timeliness Status**: ✅ Currently valid
- **Version Info**: N/A
- **Target Audience**: Remote sensing researchers
- **Research Boundary Match**: ⚠️ Partial (satellite stereo, not UAV-satellite cross-view)
- **Summary**: SP+LG shows "overall superior performance in balancing robustness, accuracy, distribution, and efficiency" for satellite stereo matching. But this is same-view satellite-satellite, not cross-view UAV-satellite.
- **Related Sub-question**: Q4

## Source #10
- **Title**: PyCuVSLAM with reComputer (Seeed Studio)
- **Link**: https://wiki.seeedstudio.com/pycuvslam_recomputer_robotics/
- **Tier**: L3
- **Publication Date**: 2026
- **Timeliness Status**: ✅ Currently valid
- **Version Info**: PyCuVSLAM v15.0.0, JetPack 6.2
- **Target Audience**: Robotics developers
- **Research Boundary Match**: ✅ Full match
- **Summary**: Tutorial for deploying PyCuVSLAM on Jetson Orin NX. Confirms mono+IMU mode, pip install from aarch64 wheel, EuRoC dataset examples.
- **Related Sub-question**: Q1
