Real-Time LiDAR Gaussian Splatting SLAM via Geometry-Aware Covariance Coupling

We present a real-time LiDAR-based framework for Gaus-sian Splatting SLAM that tightly couples fast G-ICP registration withspherical 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 scalesand to derive surface normals for geometry-aware map optimization. Wefurther introduce a covariance-derived geometry score that measures lo-cal complexity and drives pruning in planar regions and selective densifi-cation in structurally rich areas, while optimized Gaussians and LiDAR-specific confidence cues are fed back to improve tracking robustness. Onthe Newer College dataset, our method achieves an F-score of 86.78%using purely online trajectories at real-time speed (>20 FPS), and addi-tional experiments on other datasets confirm its stability and scalability.Code is available at github.com/Lab-of-AI-and-Robotics/LiDAR-GS-SLAM.Project Page: lab-of-ai-and-robotics.github.io/GS-SLAM-Family