# Source Registry — Draft02 Assessment

## Source #1
- **Title**: GTSAM GPSFactor Class Reference
- **Link**: https://gtsam.org/doxygen/a04084.html
- **Tier**: L1
- **Publication Date**: 2025 (latest docs)
- **Timeliness Status**: ✅ Currently valid
- **Version Info**: GTSAM 4.2
- **Target Audience**: GTSAM users building factor graphs with GPS constraints
- **Research Boundary Match**: ✅ Full match
- **Summary**: GPSFactor and GPSFactor2 work with Pose3/NavState, NOT Pose2. For 2D position constraints, PriorFactorPoint2 or custom factors are needed.
- **Related Sub-question**: D (GTSAM iSAM2 Factor Graph Design)

## Source #2
- **Title**: GTSAM Pose2 SLAM Example
- **Link**: https://gtbook.github.io/gtsam-examples/Pose2SLAMExample.html
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Version Info**: GTSAM 4.2
- **Target Audience**: GTSAM users
- **Research Boundary Match**: ✅ Full match
- **Summary**: BetweenFactorPose2 provides odometry constraints with noise model Diagonal.Sigmas(Point3(sigma_x, sigma_y, sigma_theta)). PriorFactorPose2 anchors poses.
- **Related Sub-question**: D

## Source #3
- **Title**: GTSAM Python pip install version compatibility (PyPI)
- **Link**: https://pypi.org/project/gtsam/
- **Tier**: L1
- **Publication Date**: 2026-01
- **Timeliness Status**: ✅ Currently valid
- **Version Info**: gtsam 4.2 (stable), gtsam-develop 4.3a1 (alpha)
- **Target Audience**: Python developers
- **Research Boundary Match**: ✅ Full match
- **Summary**: GTSAM 4.2 stable on pip. 4.3 alpha has breaking changes (C++17, Boost removal). Known issues with Eigen 5.0.0, ARM64 builds. Stick with 4.2 for production.
- **Related Sub-question**: D

## Source #4
- **Title**: LightGlue-ONNX repository
- **Link**: https://github.com/fabio-sim/LightGlue-ONNX
- **Tier**: L1
- **Publication Date**: 2026-01 (last updated)
- **Timeliness Status**: ✅ Currently valid
- **Version Info**: LightGlue-ONNX (supports ONNX Runtime + TensorRT)
- **Target Audience**: Computer vision developers
- **Research Boundary Match**: ✅ Full match
- **Summary**: ONNX export with 2-4x speedup over PyTorch. FP16 works on Turing (RTX 2060). FP8 requires Ada Lovelace/Hopper. Mixed precision supported since July 2023.
- **Related Sub-question**: C (LightGlue ONNX on RTX 2060)

## Source #5
- **Title**: LightGlue rotation issue #64
- **Link**: https://github.com/cvg/LightGlue/issues/64
- **Tier**: L4
- **Publication Date**: 2023
- **Timeliness Status**: ✅ Currently valid (issue still open)
- **Target Audience**: LightGlue users
- **Research Boundary Match**: ✅ Full match
- **Summary**: SuperPoint+LightGlue not rotation-invariant. Fails at 90°/180°. Workaround: try rotating images by {0°, 90°, 180°, 270°}. Steerable CNNs proposed but not available.
- **Related Sub-question**: G (Image Rotation Handling)

## Source #6
- **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 hybrid achieves robust matching in low-texture and high-rotation UAV scenarios. Outperforms both pure SIFT and SuperPoint+LightGlue.
- **Related Sub-question**: G

## Source #7
- **Title**: DINOv2-Based UAV Visual Self-Localization
- **Link**: https://ui.adsabs.harvard.edu/abs/2025IRAL...10.2080Y/abstract
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: UAV localization researchers
- **Research Boundary Match**: ✅ Full match
- **Summary**: DINOv2 with adaptive enhancement achieves 86.27 R@1 on DenseUAV benchmark for UAV-to-satellite matching. Proves DINOv2 viable for coarse retrieval.
- **Related Sub-question**: A (DINOv2 Cross-View Retrieval)

## Source #8
- **Title**: SatLoc-Fusion (Remote Sensing 2025)
- **Link**: https://www.mdpi.com/2072-4292/17/17/3048
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: UAV navigation researchers
- **Research Boundary Match**: ✅ Full match
- **Summary**: Hierarchical: DINOv2 absolute + XFeat VO + optical flow. <15m error, >2Hz on 6 TFLOPS edge. Adaptive confidence-based fusion. Validates our approach architecture.
- **Related Sub-question**: A, B

## Source #9
- **Title**: XFeat: Accelerated Features (CVPR 2024)
- **Link**: https://arxiv.org/abs/2404.19174
- **Tier**: L1
- **Publication Date**: 2024
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: CV researchers
- **Research Boundary Match**: ✅ Full match
- **Summary**: 5x faster than SuperPoint. Real-time on CPU. Semi-dense matching. XFeat has built-in matcher for fast VO; also compatible with LightGlue via xfeat-lightglue models.
- **Related Sub-question**: B (XFeat Reliability)

## Source #10
- **Title**: XFeat + LightGlue compatibility (GitHub issue #128)
- **Link**: https://github.com/cvg/LightGlue/issues/128
- **Tier**: L4
- **Publication Date**: 2024
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: LightGlue/XFeat users
- **Research Boundary Match**: ✅ Full match
- **Summary**: XFeat-LightGlue trained models available on HuggingFace (vismatch/xfeat-lightglue). Also ONNX export available. XFeat's built-in matcher is separate.
- **Related Sub-question**: B

## Source #11
- **Title**: DINOv2 VRAM usage by model variant
- **Link**: https://blog.iamfax.com/tech/image-processing/dinov2/
- **Tier**: L3
- **Publication Date**: 2024
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: Developers deploying DINOv2
- **Research Boundary Match**: ✅ Full match
- **Summary**: ViT-S/14: ~300MB VRAM, 0.05s/img. ViT-B/14: ~600MB, 0.1s/img. ViT-L/14: ~1.5GB, 0.35s/img. ViT-G/14: ~5GB, 2s/img.
- **Related Sub-question**: H (Memory Model)

## Source #12
- **Title**: Copernicus DEM on AWS Open Data
- **Link**: https://registry.opendata.aws/copernicus-dem/
- **Tier**: L1
- **Publication Date**: Ongoing
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: Developers needing DEM data
- **Research Boundary Match**: ✅ Full match
- **Summary**: Free access via S3 without authentication. Cloud Optimized GeoTIFFs, 1x1 degree tiles, 30m resolution. `aws s3 ls --no-sign-required s3://copernicus-dem-30m/`
- **Related Sub-question**: E (Copernicus DEM)

## Source #13
- **Title**: Google Maps Tiles API Usage and Billing
- **Link**: https://developers.google.com/maps/documentation/tile/usage-and-billing
- **Tier**: L1
- **Publication Date**: 2026-02 (updated)
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: Google Maps API consumers
- **Research Boundary Match**: ✅ Full match
- **Summary**: 100K free requests/month. 6,000/min, 15,000/day rate limits. $200 monthly credit expired Feb 2025. Requires session tokens.
- **Related Sub-question**: J

## Source #14
- **Title**: Google Maps vs Mapbox tile schema
- **Link**: https://developers.google.com/maps/documentation/tile/2d-tiles-overview
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Summary**: Both use z/x/y Web Mercator tiles (256px). Compatible coordinate systems. Google requires session tokens; Mapbox requires API tokens. Mapbox global to zoom 16, regional to 21+.
- **Related Sub-question**: J

## Source #15
- **Title**: FastAPI SSE connection cleanup issues (sse-starlette #99)
- **Link**: https://github.com/sysid/sse-starlette/issues/99
- **Tier**: L4
- **Publication Date**: 2024
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: FastAPI SSE developers
- **Research Boundary Match**: ✅ Full match
- **Summary**: Async generators cannot be easily cancelled once awaited. Use EventPublisher pattern with asyncio.Queue for proper cleanup. Prevents shutdown hangs and connection lingering.
- **Related Sub-question**: K (Security/Stability)

## Source #16
- **Title**: OpenCV decomposeHomographyMat issues (#23282)
- **Link**: https://github.com/opencv/opencv/issues/23282
- **Tier**: L4
- **Publication Date**: 2023
- **Timeliness Status**: ✅ Currently valid
- **Summary**: decomposeHomographyMat can return non-orthogonal rotation matrices. Returns 4 solutions. Positive depth constraint needed for disambiguation. Calibration matrix K precision critical.
- **Related Sub-question**: F (Homography Decomposition)

## Source #17
- **Title**: CVE-2025-48379: Pillow Heap Buffer Overflow
- **Link**: https://nvd.nist.gov/vuln/detail/CVE-2025-48379
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Version Info**: Pillow 11.2.0-11.2.1 affected, fixed in 11.3.0
- **Summary**: Heap buffer overflow in Pillow's image encoding. Requires pinning Pillow ≥11.3.0 and validating image formats.
- **Related Sub-question**: K (Security)

## Source #18
- **Title**: SALAD: Optimal Transport Aggregation for Visual Place Recognition
- **Link**: https://arxiv.org/abs/2311.15937
- **Tier**: L1
- **Publication Date**: 2024
- **Timeliness Status**: ✅ Currently valid
- **Summary**: DINOv2+SALAD outperforms NetVLAD. Single-stage retrieval, no re-ranking. 30min training. Optimal transport aggregation better than raw DINOv2 CLS token for retrieval.
- **Related Sub-question**: A

## Source #19
- **Title**: NaviLoc: Trajectory-Level Visual Localization
- **Link**: https://www.mdpi.com/2504-446X/10/2/97
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Summary**: Treats VPR as noisy measurement, uses trajectory-level optimization. 19.5m MLE, 16x improvement over per-frame VPR. Validates trajectory optimization approach.
- **Related Sub-question**: L (Recent Advances)

## Source #20
- **Title**: FAISS GPU memory management
- **Link**: https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Summary**: GPU faiss allocates ~2GB scratch space by default. On 6GB VRAM RTX 2060, CPU-based faiss recommended. Supports CPU-GPU interop.
- **Related Sub-question**: H (Memory)
