# Fact Cards — Solution Assessment (Mode B)

## Fact #1
- **Statement**: LightGlue (with SuperPoint/DISK descriptors) is NOT rotation-invariant. Image pairs with 90° or 180° rotation produce very few or zero matches. Manual image rectification is required before matching.
- **Source**: Source #7 (LightGlue GitHub Issue #64)
- **Phase**: Assessment
- **Target Audience**: UAV systems with non-stabilized cameras
- **Confidence**: ✅ High (confirmed by LightGlue maintainers)
- **Related Dimension**: Cross-view matching robustness, camera rotation handling

## Fact #2
- **Statement**: LightGlue lacks explicit training on unmatchable image pairs. When given non-overlapping views (e.g., after sharp turn), it may return semantically correct but geometrically meaningless matches instead of correctly rejecting the pair.
- **Source**: Source #8 (LightGlue GitHub Issue #13)
- **Phase**: Assessment
- **Target Audience**: Systems requiring segment detection (VO failure detection)
- **Confidence**: ✅ High (confirmed by LightGlue maintainers)
- **Related Dimension**: Segment management, VO failure detection

## Fact #3
- **Statement**: SatLoc-Fusion achieves <15m absolute localization error using a three-layer hierarchical approach: DINOv2 for coarse absolute geo-localization, XFeat for high-frequency VO, optical flow for velocity estimation. Runs real-time on 6 TFLOPS edge hardware.
- **Source**: Source #4 (SatLoc-Fusion, Remote Sensing 2025)
- **Phase**: Assessment
- **Target Audience**: GPS-denied UAV systems
- **Confidence**: ✅ High (peer-reviewed, with dataset)
- **Related Dimension**: Architecture, localization accuracy, hierarchical matching

## Fact #4
- **Statement**: XFeat is 5x faster than SuperPoint with comparable accuracy. Runs real-time on CPU. Supports both sparse and semi-dense matching. 1500+ GitHub stars, actively maintained.
- **Source**: Source #11 (CVPR 2024)
- **Phase**: Assessment
- **Target Audience**: Real-time feature extraction
- **Confidence**: ✅ High (peer-reviewed, CVPR 2024)
- **Related Dimension**: Processing speed, feature extraction

## Fact #5
- **Statement**: SIFT+LightGlue achieves superior spatial consistency and reliability for UAV image mosaicking, including in low-texture and high-rotation conditions. SIFT is rotation-invariant unlike SuperPoint.
- **Source**: Source #2 (ISPRS 2025)
- **Phase**: Assessment
- **Target Audience**: UAV image matching
- **Confidence**: ✅ High (peer-reviewed)
- **Related Dimension**: Feature extraction, rotation handling

## Fact #6
- **Statement**: SuperPoint+LightGlue extraction+matching takes ~0.36±0.06s per image pair on GPU (unspecified GPU model). This is for standard resolution images, not 6000+ pixel width.
- **Source**: Source #18
- **Phase**: Assessment
- **Target Audience**: Performance planning
- **Confidence**: ⚠️ Medium (GPU model not specified, may not be RTX 2060)
- **Related Dimension**: Processing time

## Fact #7
- **Statement**: LightGlue ONNX/TensorRT achieves 2-4x speedup over compiled PyTorch. FP8 quantization adds 6x more but requires Ada Lovelace or newer GPUs. RTX 2060 (Turing) does NOT support FP8 — limited to FP16/INT8 acceleration.
- **Source**: Source #5, #6 (LightGlue-ONNX blog and repo)
- **Phase**: Assessment
- **Target Audience**: RTX 2060 deployment
- **Confidence**: ✅ High (benchmarked by repo maintainer)
- **Related Dimension**: Processing time, hardware constraints

## Fact #8
- **Statement**: YFS90 achieves <7m MAE using terrain-weighted constraint optimization + 2D-3D geo-registration with DEM data. Validated across 20 complex scenarios including plains, hilly terrain, urban/rural. Works with publicly available satellite maps and DEM data. Re-localization capability after failures.
- **Source**: Source #9 (YFS90 GitHub)
- **Phase**: Assessment
- **Target Audience**: GPS-denied UAV navigation
- **Confidence**: ✅ High (peer-reviewed, open source, 69★)
- **Related Dimension**: Optimization approach, DEM integration, accuracy

## Fact #9
- **Statement**: Google Maps $200/month free credit expired February 28, 2025. Current free tier is 100,000 tile requests/month. Rate limits: 6,000 requests/min, 15,000 requests/day for 2D tiles.
- **Source**: Source #13 (Google Maps official docs)
- **Phase**: Assessment
- **Target Audience**: Cost planning
- **Confidence**: ✅ High (official documentation)
- **Related Dimension**: Cost, satellite imagery access

## Fact #10
- **Statement**: Google Maps satellite imagery for eastern Ukraine is likely updated only every 3-5+ years due to: conflict zone (lower priority), geopolitical challenges, limited user demand. This may not meet the AC requirement of "less than 2 years old."
- **Source**: Multiple web sources on Google Maps update frequency
- **Phase**: Assessment
- **Target Audience**: Satellite imagery reliability
- **Confidence**: ⚠️ Medium (general guidelines, not Ukraine-specific confirmation)
- **Related Dimension**: Satellite imagery reliability

## Fact #11
- **Statement**: Mapbox Satellite offers imagery up to 0.3m resolution at zoom 16+, sourced from Maxar, Landsat, Sentinel. 200,000 free vector tile requests/month. Unlimited offline downloads on pay-as-you-go. Potentially more diverse and recent imagery for Ukraine than Google Maps alone.
- **Source**: Source #20 (Mapbox docs)
- **Phase**: Assessment
- **Target Audience**: Alternative satellite providers
- **Confidence**: ✅ High (official documentation)
- **Related Dimension**: Satellite imagery reliability, cost

## Fact #12
- **Statement**: Copernicus DEM GLO-30 provides free 30m resolution global elevation data including Ukraine. Accessible via Sentinel Hub API. Can be used for terrain-weighted optimization like YFS90.
- **Source**: Source #15 (Copernicus docs)
- **Phase**: Assessment
- **Target Audience**: DEM integration
- **Confidence**: ✅ High (official documentation)
- **Related Dimension**: Position optimizer, terrain constraints

## Fact #13
- **Statement**: GTSAM v4.2 (stable) provides Python bindings with GPSFactor for absolute position constraints and iSAM2 for incremental optimization. Can model VO constraints, satellite anchor constraints, and drift limits in a unified factor graph.
- **Source**: Source #14 (GTSAM docs)
- **Phase**: Assessment
- **Target Audience**: Optimizer design
- **Confidence**: ✅ High (widely used in robotics)
- **Related Dimension**: Position optimizer

## Fact #14
- **Statement**: DALGlue achieves 11.8% MMA improvement over LightGlue on MegaDepth benchmark. Specifically designed for UAV visual navigation with wavelet transform preprocessing for handling dynamic flight blur.
- **Source**: Source #10 (Scientific Reports 2025)
- **Phase**: Assessment
- **Target Audience**: Feature matching selection
- **Confidence**: ✅ High (peer-reviewed)
- **Related Dimension**: Feature matching

## Fact #15
- **Statement**: The oblique-robust AVL method (IEEE TGRS 2024) uses SE(2)-steerable networks for rotation-equivariant features. Handles drastic perspective changes and non-perpendicular camera angles for UAV-to-satellite matching. No retraining needed for new scenes.
- **Source**: Source #12 (IEEE TGRS 2024)
- **Phase**: Assessment
- **Target Audience**: Cross-view matching
- **Confidence**: ✅ High (peer-reviewed, IEEE)
- **Related Dimension**: Cross-view matching, rotation handling

## Fact #16
- **Statement**: Homography decomposition can be unstable in certain configurations (2025 IJCV study). Non-planar objects (buildings, trees) violate planar assumption. For aerial images, dominant ground plane exists but RANSAC inlier ratio drops with non-planar content.
- **Source**: Source #16 (IJCV 2025)
- **Phase**: Assessment
- **Target Audience**: VO design
- **Confidence**: ✅ High (peer-reviewed)
- **Related Dimension**: VO robustness

## Fact #17
- **Statement**: Sliding-window factor graph optimization combines the accuracy of full graph optimization with the efficiency of windowed processing. Superior to either pure filtering or full batch optimization for real-time navigation.
- **Source**: Source #17 (Cambridge 2020)
- **Phase**: Assessment
- **Target Audience**: Optimizer design
- **Confidence**: ✅ High (peer-reviewed)
- **Related Dimension**: Position optimizer

## Fact #18
- **Statement**: SuperPoint is a fully-convolutional model — GPU memory scales linearly with image resolution. 6252×4168 input would require significant VRAM. Standard practice is to downscale to 1024-2048 long edge for feature extraction.
- **Source**: Source #18, SuperPoint docs
- **Phase**: Assessment
- **Target Audience**: Memory management
- **Confidence**: ✅ High (architectural fact)
- **Related Dimension**: Memory management, processing pipeline

## Fact #19
- **Statement**: For GPS-denied UAV localization, hierarchical coarse-to-fine approaches (image retrieval → local feature matching) are state-of-the-art. Direct local feature matching alone fails when the search area is too large or viewpoint difference is too high.
- **Source**: Source #3, #4, #12 (CEUSP, SatLoc, Oblique-robust AVL)
- **Phase**: Assessment
- **Target Audience**: Architecture design
- **Confidence**: ✅ High (consensus across multiple papers)
- **Related Dimension**: Architecture, satellite matching

## Fact #20
- **Statement**: Google Maps Tiles API daily rate limit of 15,000 requests would be hit when processing a 3000-image flight requiring ~2000 satellite tiles plus expansion tiles. Need to either pre-cache or use the per-minute limit (6,000/min) strategically across multiple days.
- **Source**: Source #13 (Google Maps docs)
- **Phase**: Assessment
- **Target Audience**: System design
- **Confidence**: ✅ High (official rate limits)
- **Related Dimension**: Satellite tile management, rate limiting
