{"ID":5937611,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-08T05:40:52.147495227Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.04127","arxiv_id":"2607.04127","title":"Real-Time LiDAR Gaussian Splatting SLAM","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 ($\u003e$20 FPS), and additional experiments on other datasets confirm its stability and scalability.","short_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 r...","url_abs":"https://arxiv.org/abs/2607.04127","url_pdf":"https://arxiv.org/pdf/2607.04127v1","authors":"[\"Seungjun Tak\",\"Yewon Jeon\",\"Jaeik Hwang\",\"SukMin Hwang\",\"Seongbo Ha\",\"Hyeonwoo Yu\"]","published":"2026-07-05T05:55:32Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.RO\"]","methods":"[]","has_code":false}