Real-time Multi-Adaptive-Resolution-Surfel 6D LiDAR Odometry using Continuous-time Trajectory Optimization
This work addresses the problem of high data rates in 3D LiDAR SLAM for autonomous robots, offering a real-time solution that is incremental in its improvements to existing methods.
The paper tackled the challenge of real-time 6D LiDAR odometry for autonomous robots by proposing a method that combines continuous-time trajectory optimization with multi-resolution surfel maps, achieving real-time performance as demonstrated in experiments on multiple datasets and real-robot tests.
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.