# Fact Cards

## Fact #1
- **Statement**: cuVSLAM achieves 116fps on Jetson Orin Nano 8GB at 720p resolution (~8.6ms/frame). 232fps on AGX Orin. 386fps on RTX 4060 Ti.
- **Source**: [Source #3] Intermodalics benchmark
- **Phase**: Assessment
- **Confidence**: ✅ High
- **Related Dimension**: VO speed comparison

## Fact #2
- **Statement**: SuperPoint+LightGlue VO achieves ~10fps on KITTI dataset on desktop GPU (~100ms/frame). With 274 keypoints on RTX 2080Ti, LightGlue matching alone takes 33.9ms.
- **Source**: vo_lightglue, LG issue #36
- **Confidence**: ⚠️ Medium (desktop GPU, not Jetson)
- **Related Dimension**: VO speed comparison

## Fact #3
- **Statement**: SuperPoint feature extraction takes ~40ms on Jetson (TensorRT, 200 keypoints).
- **Source**: SuperPoint-SuperGlue-TensorRT
- **Confidence**: ⚠️ Medium (older Jetson)
- **Related Dimension**: VO speed comparison

## Fact #4
- **Statement**: LightGlue TensorRT with FlashAttention V2 has been deployed on Jetson Orin NX 8GB. No published latency numbers.
- **Source**: qdLMF/LightGlue-with-FlashAttentionV2-TensorRT
- **Confidence**: ⚠️ Medium
- **Related Dimension**: VO speed comparison

## Fact #5
- **Statement**: LiteSAM (opt) inference: 61.98ms on RTX 3090, 497.49ms on Jetson AGX Orin at 1184px input. 6.31M params.
- **Source**: LiteSAM paper, abstract + Section 4.10
- **Confidence**: ✅ High
- **Related Dimension**: Satellite matcher speed

## Fact #6
- **Statement**: Jetson AGX Orin has 275 TOPS INT8, 2048 CUDA cores. Orin Nano Super has 67 TOPS INT8, 1024 CUDA cores. AGX Orin is ~3-4x more powerful.
- **Source**: NVIDIA official specs
- **Confidence**: ✅ High
- **Related Dimension**: Hardware scaling

## Fact #7
- **Statement**: LiteSAM processes at 1/8 scale internally. Coarse matching is O(N²) where N = (H/8 × W/8). For 1184px: ~21,904 tokens. For 1280px: ~25,600. For 480px: ~3,600.
- **Source**: LiteSAM paper, Sections 3.1-3.3
- **Confidence**: ✅ High
- **Related Dimension**: LiteSAM speed vs resolution

## Fact #8
- **Statement**: LiteSAM paper Figure 1 states: "SP+LG achieves the fastest inference speed but at the expense of accuracy" vs LiteSAM on satellite-aerial benchmarks.
- **Source**: LiteSAM paper
- **Confidence**: ✅ High
- **Related Dimension**: SP+LG vs LiteSAM

## Fact #9
- **Statement**: LiteSAM achieves RMSE@30 = 17.86m on UAV-VisLoc. SP+LG is worse on same benchmark.
- **Source**: LiteSAM paper
- **Confidence**: ✅ High
- **Related Dimension**: Satellite matcher accuracy

## Fact #10
- **Statement**: cuVSLAM uses Shi-Tomasi corners ("Good Features to Track") for keypoint detection, divided into NxM grid patches. Uses Lucas-Kanade optical flow for tracking. When tracked keypoints fall below threshold, creates new keyframe.
- **Source**: cuVSLAM paper (arXiv:2506.04359), Section 2.1
- **Confidence**: ✅ High
- **Related Dimension**: cuVSLAM on difficult terrain

## Fact #11
- **Statement**: cuVSLAM automatically switches to IMU when visual tracking fails (dark lighting, long solid surfaces). IMU integrator provides ~1 second of acceptable tracking. After IMU, constant-velocity integrator provides ~0.5 seconds more.
- **Source**: Isaac ROS cuVSLAM docs
- **Confidence**: ✅ High
- **Related Dimension**: cuVSLAM on difficult terrain

## Fact #12
- **Statement**: cuVSLAM does NOT guarantee correct pose recovery after losing track. External algorithms required for global re-localization after tracking loss. Cannot fuse GNSS, wheel odometry, or LiDAR.
- **Source**: Intermodalics blog
- **Confidence**: ✅ High
- **Related Dimension**: cuVSLAM on difficult terrain

## Fact #13
- **Statement**: cuVSLAM benchmarked on KITTI (mostly urban/suburban driving) and EuRoC (indoor drone). Neither benchmark includes nadir agricultural terrain, flat fields, or uniform vegetation. No published results for these conditions.
- **Source**: cuVSLAM paper Section 3
- **Confidence**: ✅ High
- **Related Dimension**: cuVSLAM on difficult terrain

## Fact #14
- **Statement**: cuVSLAM multi-stereo mode "significantly improves accuracy and robustness on challenging sequences compared to single stereo cameras", designed for featureless surfaces (narrow corridors, elevators). But our system uses monocular camera only.
- **Source**: cuVSLAM paper Section 2.2.2
- **Confidence**: ✅ High
- **Related Dimension**: cuVSLAM on difficult terrain

## Fact #15
- **Statement**: PFED achieves 97.15% Recall@1 on University-1652 at 251.5 FPS on AGX Orin with only 4.45G FLOPs. But this is image RETRIEVAL (which satellite tile matches), NOT pixel-level correspondence matching.
- **Source**: PFED paper (arXiv:2510.22582)
- **Confidence**: ✅ High
- **Related Dimension**: Satellite matching alternatives

## Fact #16
- **Statement**: EfficientLoFTR is ~2.5x faster than LoFTR with higher accuracy. Semi-dense matcher, 15.05M params. Has TensorRT adaptation (LoFTR_TRT). Performs well on weak-texture areas where traditional methods fail. Designed for aerial imagery.
- **Source**: EfficientLoFTR paper (CVPR 2024), HuggingFace docs
- **Confidence**: ✅ High
- **Related Dimension**: Satellite matching alternatives

## Fact #17
- **Statement**: Hierarchical AVL system (2025) uses two-stage approach: DINOv2 for coarse retrieval + SuperPoint for fine matching. 64.5-95% success rate on real-world drone trajectories. Includes IMU-based prior correction and sliding-window map updates.
- **Source**: MDPI Remote Sensing 2025
- **Confidence**: ✅ High
- **Related Dimension**: Satellite matching alternatives

## Fact #18
- **Statement**: STHN uses deep homography estimation for UAV geo-localization: directly estimates homography transform (no feature detection/matching/RANSAC). Achieves 4.24m MACE at 50m range. Designed for thermal but architecture is modality-agnostic.
- **Source**: STHN paper (IEEE RA-L 2024)
- **Confidence**: ✅ High
- **Related Dimension**: Satellite matching alternatives

## Fact #19
- **Statement**: For our nadir UAV → satellite matching, the cross-view gap is SMALL compared to typical cross-view problems (ground-to-satellite). Both views are approximately top-down. Main challenges: season/lighting, resolution mismatch, temporal changes. This means general-purpose matchers may work better than expected.
- **Source**: Analytical observation
- **Confidence**: ⚠️ Medium
- **Related Dimension**: Satellite matching alternatives

## Fact #20
- **Statement**: LiteSAM paper benchmarked EfficientLoFTR (opt) on satellite-aerial: 19.8% slower than LiteSAM (opt) on AGX Orin but with 2.4x more parameters. EfficientLoFTR achieves competitive accuracy. LiteSAM paper Table 3/4 provides direct comparison.
- **Source**: LiteSAM paper, Section 4.5
- **Confidence**: ✅ High
- **Related Dimension**: EfficientLoFTR vs LiteSAM
