SA-LIVO：激光雷达和相机「按方向选择性融合」，无GPU稳跑10Hz，12.3ms/帧且内存暴降6.3倍！
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Merged summary
TL;DR - SA-LIVO is a tightly-coupled LiDAR-inertial-visual odometry system that gates LiDAR and camera information per eigen-direction of the joint information matrix instead of per-sensor, keeping accuracy competitive while running ~12.3 ms/frame at 10 Hz on CPU-only hardware with ~6.3× lower peak memory.
- Subspace-Aware Information Fusion (SAIF): eigendecomposes the joint LiDAR-visual information matrix and applies a single-threshold linear-clamp soft gate per eigen-direction, attenuating degenerate directions while preserving observable ones; replaces four hand-tuned per-sensor parameters with one and yields a provably PSD fusion matrix that degrades continuously to single-sensor updates without mode switching.
- Unified single-loop joint InEKF update: LiDAR and photometric residuals are linearized at a shared point in one hybrid right-invariant InEKF iteration loop (rotation error right-invariant, position/velocity Euclidean so SAIF operates in one linear error space), removing the linearization-point mismatch of sequential per-sensor updates (e.g. FAST-LIVO2).
- Efficiency follows from the fusion strategy: since vision only contributes where LiDAR is under-constrained, photometric Jacobians are assembled once before the InEKF loop and reused across iterations (accumulated over a sliding window with per-observation decorrelation to avoid inflating visual information).
- Scale-invariant voxel mapping: distance-adaptive voxel size, per-voxel sufficient statistics for O(1) multi-scale PCA, and a dimensionless planarity ratio (eigenvalue ratio) replacing absolute eigenvalue thresholds; voxels freeze once planar, with saturation-priority to resist drift contamination. Evaluated on 29 sequences across HILTI'22, New College, and Oxford Spires, staying bounded where R3LIVE and SR-LIVO diverge; code and dataset promised public.
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SA-LIVO：激光雷达和相机「按方向选择性融合」，无GPU稳跑10Hz，12.3ms/帧且内存暴降6.3倍！
Public signals
N/A
TL;DR - SA-LIVO is a tightly-coupled LiDAR-inertial-visual odometry system that gates LiDAR and camera information per eigen-direction of the joint information matrix instead of per-sensor, keeping accuracy competitive while running ~12.3 ms/frame at 10 Hz on CPU-only hardware with ~6.3× lower peak memory.
- Subspace-Aware Information Fusion (SAIF): eigendecomposes the joint LiDAR-visual information matrix and applies a single-threshold linear-clamp soft gate per eigen-direction, attenuating degenerate directions while preserving observable ones; replaces four hand-tuned per-sensor parameters with one and yields a provably PSD fusion matrix that degrades continuously to single-sensor updates without mode switching.
- Unified single-loop joint InEKF update: LiDAR and photometric residuals are linearized at a shared point in one hybrid right-invariant InEKF iteration loop (rotation error right-invariant, position/velocity Euclidean so SAIF operates in one linear error space), removing the linearization-point mismatch of sequential per-sensor updates (e.g. FAST-LIVO2).
- Efficiency follows from the fusion strategy: since vision only contributes where LiDAR is under-constrained, photometric Jacobians are assembled once before the InEKF loop and reused across iterations (accumulated over a sliding window with per-observation decorrelation to avoid inflating visual information).
- Scale-invariant voxel mapping: distance-adaptive voxel size, per-voxel sufficient statistics for O(1) multi-scale PCA, and a dimensionless planarity ratio (eigenvalue ratio) replacing absolute eigenvalue thresholds; voxels freeze once planar, with saturation-priority to resist drift contamination. Evaluated on 29 sequences across HILTI'22, New College, and Oxford Spires, staying bounded where R3LIVE and SR-LIVO diverge; code and dataset promised public.