Real-Time LiDAR Gaussian Splatting SLAM

• URL: http://arxiv.org/abs/2607.04127v1
• Date: Sun, 05 Jul 2026 05:55:32 GMT
• Title: Real-Time LiDAR Gaussian Splatting SLAM
• Abstract summary: We present a real-time LiDAR-based framework for Splatting SLAM that tightly couples fast G-ICP registration with sphericalization-based dense mapping for large-scale sequences.<n>We reuse tracking-estimated local covariances to initialize Gaussians with range-aware scales and to derive surface normals for map optimization.<n>On the Newer College dataset, our method achieves an F-score of 86.78% using purely online trajectories at real-time speed.
• Score: 2.0524609401792397
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a real-time LiDAR-based framework for Gaussian Splatting SLAM that tightly couples fast G-ICP registration with spherical rasterization-based dense mapping for large-scale sequences. Leveraging LiDAR geometry rather than appearance, we reuse tracking-estimated local covariances to initialize Gaussians with range-aware scales and to derive surface normals for geometry-aware map optimization. We further introduce a covariance-derived geometry score that measures local complexity and drives pruning in planar regions and selective densification in structurally rich areas, while optimized Gaussians and LiDAR-specific confidence cues are fed back to improve tracking robustness. On the Newer College dataset, our method achieves an F-score of 86.78\% using purely online trajectories at real-time speed ($>$20 FPS), and additional experiments on other datasets confirm its stability and scalability.

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