Real-time Multi-Adaptive-Resolution-Surfel 6D LiDAR Odometry using Continuous-time Trajectory Optimization
Jan Quenzel, Sven Behnke
Abstract
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.
Topics & Concepts
OdometryComputer scienceSimultaneous localization and mappingArtificial intelligenceTrajectoryComputer visionLidarMixture modelRobotGaussianVisual odometryScheme (mathematics)Data associationMobile robotRemote sensingMathematicsGeographyProbabilistic logicMathematical analysisQuantum mechanicsPhysicsAstronomyRobotics and Sensor-Based LocalizationAdvanced Vision and ImagingImage and Object Detection Techniques