实时激光雷达高斯点云拼接SLAM
Real-Time LiDAR Gaussian Splatting SLAM
AI总结：
提出基于激光雷达的高斯点云拼接SLAM实时框架，将快速G-ICP配准与球形光栅化密集映射紧密结合，利用激光雷达几何信息优化地图，引入几何分数改进效果，在多数据集实验验证其性能。
摘要：
我们提出了一个基于激光雷达的高斯点云拼接SLAM实时框架，该框架将快速G-ICP配准与基于球形光栅化的密集映射紧密结合，用于大规模序列。利用激光雷达几何而非外观，我们重用跟踪估计的局部协方差，用距离感知尺度初始化高斯，并导出表面法线用于几何感知地图优化。我们进一步引入了一个协方差派生的几何分数，用于测量局部复杂性，并驱动平面区域的修剪和结构丰富区域的选择性致密化，同时优化的高斯和激光雷达特定的置信线索被反馈以提高跟踪鲁棒性。在Newer College数据集上，我们的方法使用纯在线轨迹以实时速度（>20 FPS）实现了86.78%的F分数，并且在其他数据集上的额外实验证实了其稳定性和可扩展性。
英文摘要：
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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