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add clarification to research methodology by including a step for solution comparison and user consultation
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Oleksandr Bezdieniezhnykh
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# Fact Cards
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## Fact #1
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- **Statement**: Camera tilt of 18° produces >5% GSD error. During turns (10-30° tilt), GSD error is 1.5-15.5%. In straight flight (1-5°), error is negligible (0.015-0.38%).
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- **Source**: Source #1 (geometric derivation: error = 1/cos(θ) - 1)
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- **Phase**: Assessment
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- **Target Audience**: UAV VO systems with non-stabilized cameras
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- **Confidence**: ✅ High (mathematical derivation)
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- **Related Dimension**: VO accuracy
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## Fact #2
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- **Statement**: Homography decomposition (already in pipeline) extracts rotation matrix R, from which camera tilt (pitch/roll) can be derived. GSD correction formula: GSD_corrected = GSD_nadir / cos(θ).
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- **Source**: Source #1
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- **Phase**: Assessment
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- **Target Audience**: UAV VO systems
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- **Confidence**: ✅ High
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- **Related Dimension**: VO accuracy
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## Fact #3
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- **Statement**: SALAD aggregation improves DINOv2 retrieval by +12.4pp R@1 on MSLS Challenge over GeM pooling (75.0% vs 62.6%). NordLand: +40.6pp (76.0% vs 35.4%). Overhead: <3ms per image.
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- **Source**: Source #2 (SALAD paper, CVPR 2024)
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- **Phase**: Assessment
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- **Target Audience**: Visual place recognition systems
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- **Confidence**: ✅ High (peer-reviewed CVPR paper)
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- **Related Dimension**: Satellite coarse retrieval quality
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## Fact #4
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- **Statement**: SALAD is backbone-agnostic and can work with ViT-S/14 (384-dim), though the paper only reports ViT-B results. Expected ~2-3pp lower recall with ViT-S.
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- **Source**: Source #2
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- **Phase**: Assessment
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- **Target Audience**: DINOv2 ViT-S users
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- **Confidence**: ⚠️ Medium (extrapolated from paper)
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- **Related Dimension**: Satellite coarse retrieval quality
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## Fact #5
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- **Statement**: GeM pooling provides a simpler improvement over average pooling: 62.6% R@1 on MSLS Challenge vs ~42% for VLAD-style (AnyLoc). It's a one-line change.
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- **Source**: Source #2
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- **Phase**: Assessment
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- **Target Audience**: VPR systems
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- **Confidence**: ✅ High
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- **Related Dimension**: Satellite coarse retrieval quality
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## Fact #6
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- **Statement**: Compute-bound GPU models (DNN inference like SuperPoint, LightGlue, DINOv2, LiteSAM) CANNOT run truly concurrently on a single GPU via CUDA streams. Models saturate the GPU; streams execute sequentially.
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- **Source**: Source #3 (PyTorch docs, CUDA documentation)
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- **Phase**: Assessment
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- **Target Audience**: GPU pipeline developers
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- **Confidence**: ✅ High (official documentation)
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- **Related Dimension**: Pipeline concurrency model
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## Fact #7
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- **Statement**: Recommended single-GPU pattern: run VO sequentially first (latency-critical), then satellite matching. Use async Python for logical overlap — satellite results for frame N arrive while VO processes frame N+2 or N+3. pin_memory() + non_blocking=True for data transfer overlap.
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- **Source**: Source #3
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- **Phase**: Assessment
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- **Target Audience**: GPU pipeline developers
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- **Confidence**: ✅ High
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- **Related Dimension**: Pipeline concurrency model
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## Fact #8
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- **Statement**: python-jose is unmaintained for ~2 years. Multiple CVEs including DER confusion and timing side-channels. Community and Okta recommend migrating to PyJWT.
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- **Source**: Source #4
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- **Phase**: Assessment
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- **Target Audience**: Python JWT library users
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- **Confidence**: ✅ High
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- **Related Dimension**: Security
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## Fact #9
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- **Statement**: Pillow CVE-2026-25990 (PSD out-of-bounds write) affects versions 10.3.0 to <12.1.1. Draft05 pins ≥11.3.0 which is vulnerable. Must upgrade to ≥12.1.1.
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- **Source**: Source #5
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- **Phase**: Assessment
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- **Target Audience**: Python image processing users
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- **Confidence**: ✅ High (NVD)
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- **Related Dimension**: Security
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## Fact #10
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- **Statement**: aiohttp has 7 CVEs (zip bomb DoS, large payload DoS, request smuggling). All fixed in ≥3.13.3.
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- **Source**: Source #6
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- **Phase**: Assessment
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- **Target Audience**: Python async HTTP users
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- **Confidence**: ✅ High (NVD)
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- **Related Dimension**: Security
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## Fact #11
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- **Statement**: h11 CVE-2025-43859 (CVSS 9.1) — HTTP request smuggling affecting uvicorn. Fixed in h11 ≥0.16.0.
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- **Source**: Source #7
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- **Phase**: Assessment
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- **Target Audience**: Python web server users
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- **Confidence**: ✅ High (NVD)
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- **Related Dimension**: Security
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## Fact #12
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- **Statement**: ONNX Runtime path traversal vulnerability (AIKIDO-2026-10185) in external data loading. Fixed in ≥1.24.1.
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- **Source**: Source #8
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- **Phase**: Assessment
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- **Target Audience**: ONNX Runtime users
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- **Confidence**: ✅ High (NVD)
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- **Related Dimension**: Security
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## Fact #13
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- **Statement**: Lens distortion correction is crucial for UAV photogrammetry with non-metric cameras. Distortion at image edges can be 5-20px for wide-angle lenses. Camera parameters (K matrix + distortion coefficients) are known in this system.
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- **Source**: Source #9
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- **Phase**: Assessment
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- **Target Audience**: UAV photogrammetry systems
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- **Confidence**: ✅ High (peer-reviewed)
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- **Related Dimension**: VO accuracy / satellite matching accuracy
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## Fact #14
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- **Statement**: ENU flat-Earth approximation is suitable for <4km extents. Beyond 4km, Earth curvature introduces significant errors. At 10km, error is ~0.5m; at 50km, ~12.5m.
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- **Source**: Source #10
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- **Phase**: Assessment
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- **Target Audience**: Navigation system developers
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- **Confidence**: ✅ High (ESA Navipedia)
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- **Related Dimension**: Coordinate system accuracy
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## Fact #15
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- **Statement**: Visual SLAM memory management: keep only recent features in active memory (rolling window); archive/discard older features. Selective memory storage can reduce database by up to 92.86%.
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- **Source**: Source #11
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- **Phase**: Assessment
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- **Target Audience**: Visual SLAM systems
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- **Confidence**: ✅ High (peer-reviewed)
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- **Related Dimension**: Memory management
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## Fact #16
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- **Statement**: safetensors metadata RCE report is under review (Feb 2026). Polyglot and header-bomb attacks are known vectors. Currently no confirmed fix.
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- **Source**: Source #12
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- **Phase**: Assessment
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- **Target Audience**: ML model deployment teams
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- **Confidence**: ⚠️ Medium (under review)
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- **Related Dimension**: Security
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