Most current LiDAR simultaneous localization and mapping (SLAM) systems build maps in point clouds, which are sparse when zoomed in, even though they seem dense to human eyes. Dense maps are essential for robotic applications, such as map-based navigation. Due to the low memory cost, mesh has become an attractive dense model for mapping in recent years. However, existing methods usually produce mesh maps by using an offline post-processing step to generate mesh maps. This two-step pipeline does not allow these methods to use the built mesh maps online and to enable localization and meshing to benefit each other. To solve this problem, we propose the first CPU-only real-time LiDAR SLAM system that can simultaneously build a mesh map and perform localization against the mesh map. A novel and direct meshing strategy with Gaussian process reconstruction realizes the fast building, registration, and updating of mesh maps. We perform experiments on several public datasets. The results show that our SLAM system can run at around $40$Hz. The localization and meshing accuracy also outperforms the state-of-the-art methods, including the TSDF map and Poisson reconstruction. Our code and video demos are available at: https://github.com/lab-sun/SLAMesh.

翻译：当前大多数激光雷达同时定位与地图构建（SLAM）系统以点云形式构建地图，尽管在人眼看来密集，但放大后仍显稀疏。对于基于地图导航等机器人应用而言，稠密地图至关重要。近年来，网格模型凭借其低内存成本已成为一种极具吸引力的稠密建图方案。然而现有方法通常采用离线后处理步骤生成网格地图，这种两步式流程导致无法在线利用已构建的网格地图，也无法使定位与网格建图相互增益。为解决该问题，我们提出首个仅依赖CPU的实时LiDAR SLAM系统，能够同时构建网格地图并基于网格地图执行定位。一种结合高斯过程重建的新型直接网格化策略实现了网格地图的快速构建、配准与更新。我们在多个公开数据集上进行了实验，结果表明本SLAM系统能以约40Hz频率运行，其定位与建图精度均优于包括TSDF地图和泊松重建在内的最先进方法。代码与演示视频已开源至：https://github.com/lab-sun/SLAMesh。