Robotics and Sensor-Based Localization
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Abstract
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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@article{Tak2026Real,
title = {Real-Time LiDAR Gaussian Splatting SLAM},
author = {Seungjun Tak and Yewon Jeon and 황재익 and SukMin Hwang and Seongbo Ha and Hyeonwoo Yu},
journal = {arXiv (Cornell University)},
year = {2026},
doi = {10.48550/arxiv.2607.04127},
url = {https://doi.org/10.48550/arxiv.2607.04127}
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