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Simultaneous Localization and Mapping (SLAM) is an essential capability forautonomous robots, but due to high data rates of 3D LiDARs real-time SLAM ischallenging. We propose a real-time method for 6D LiDAR odometry. Our approachcombines a continuous-time B-Spline trajectory representation with a GaussianMixture Model (GMM) formulation to jointly align local multi-resolution surfelmaps. Sparse voxel grids and permutohedral lattices ensure fast access to mapsurfels, and an adaptive resolution selection scheme effectively speeds upregistration. A thorough experimental evaluation shows the performance of ourapproach on multiple datasets and during real-robot experiments.

Real-time Multi-Adaptive-Resolution-Surfel 6D LiDAR Odometry using Continuous-time Trajectory Optimization