Simultaneous Localization and Mapping (SLAM) is an essential capability for autonomous robots, but due to high data rates of 3D LiDARs real-time SLAM is challenging. We propose a real-time method for 6D LiDAR odometry. Our approach combines a continuous-time B-Spline trajectory representation with a Gaussian Mixture Model (GMM) formulation to jointly align local multi-resolution surfel maps. Sparse voxel grids and permutohedral lattices ensure fast access to map surfels, and an adaptive resolution selection scheme effectively speeds up registration. A thorough experimental evaluation shows the performance of our approach on two datasets and during real-robot experiments.

翻译：同时定位和绘图(SLAM)是自主机器人的基本能力,但由于 3D LiDARs 实时 SLAM 数据率高,我们提出了6D LiDAR odo测量的实时方法。我们的方法将连续的 B- Spline 轨迹表示法与高山混合模型(GMMM) 的配方结合起来,以联合对齐本地多分辨率冲浪地图。 Sparse voxel 电网和超声波拉托克确保快速访问地图冲浪器,而适应性分辨率选择方案有效地加快了登记速度。一个彻底的实验性评估展示了我们在两个数据集和真实机器人实验中的做法的绩效。