  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 multiple datasets and during real-robot experiments.
