实时激光雷达高斯点云建图，精度高且稳定。
Real-Time LiDAR Gaussian Splatting SLAM
- 用激光几何信息初始化高斯点，结合局部协方差优化
- 实测达86.78%的F-score，支持20+ FPS实时运行
- 自适应删减平面区、增强结构丰富区，适合大场景
我们提出一种基于激光雷达的实时高斯点云同步定位与建图框架，通过快速G-ICP配准与球面光栅化密集建图紧密耦合，实现大规模序列处理。利用激光几何而非外观特征，复用追踪估计的局部协方差来初始化具有距离感知尺度的高斯点，并推导表面法线以支持几何感知地图优化。进一步引入基于协方差的几何评分，衡量局部复杂度，驱动平面区域的删减与结构丰富区域的选择性加密，同时将优化后的高斯点与激光专属置信度反馈至追踪环节，提升鲁棒性。在Newer College数据集上，仅使用在线轨迹即实现86.78%的F-score，且保持实时速度（>20 FPS）；其他数据集上的实验也验证了其稳定性与可扩展性。
原文摘要 · Abstract (English)
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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