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: this https URL.
当前的激光雷达同时定位和绘图(SLAM)系统通常以点云的形式构建地图,尽管点云在放大时看起来较少。Dense地图对于机器人应用,例如基于地图的导航,至关重要。由于内存成本较低,网格已经成为近年来Mapping中一种吸引人的密集模型。然而,现有的方法通常使用 offline 后处理步骤生成网格地图。此两步流程不允许这些方法使用生成的网格地图并实现定位和网格之间的相互帮助。为了解决这一问题,我们提出了第一个仅使用CPU的实时激光雷达 SLAM 系统,可以同时构建网格地图并对抗网格地图进行定位。一种独特的直接网格重构策略实现了快速构建、注册和更新网格地图。我们在多个公共数据集上进行了实验。结果表明,我们的 SLAM 系统可以运行在约 $40$ 赫兹。定位和网格精度也优于最先进的方法,包括TSDF地图和泊松重构。我们的代码和视频演示可用在此 https URL 上。