Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry

Chongjian Yuan1; Wei Xu1; Xiyuan Liu1; Xiaoping Hong2; Fu Zhang1

2022

10.1109/LRA.2022.3187250

IEEE Robotics and Automation Letters   Impact Factor & Quartile

2377-3774

This letter proposes an efficient and probabilistic adaptive voxel mapping method for LiDAR odometry. The map is a collection of voxels; each contains one plane feature that enables the probabilistic representation of the environment and accurate registration of a new LiDAR scan. We further analyze the need for coarse-to-fine voxel mapping and then use a novel voxel map organized by a Hash table and octrees to build and update the map efficiently. We apply the proposed voxel map to an iterated extended Kalman filter and construct a maximum a posteriori probability problem for pose estimation. Experiments on the open KITTI dataset show the high accuracy and efficiency of our method compared to other state-of-the-art methods. Experiments on indoor and unstructured outdoor environments with solid-state LiDAR and non-repetitive scanning LiDAR further verify the adaptability of our mapping method to different environments and LiDAR scanning patterns (see our attached video(1)). Our codes and dataset are open-sourced on Github(2)

Mapping Localization SLAM

SCI ; EI

English

Others

University Grants Committee of Hong Kong General Research Fund[17206421] ; SUSTech startup Fund[Y01966105]

Robotics

Robotics

WOS:000838455200025

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

20222812349124

HTTP ; Indoor positioning systems ; Kalman filters ; Optical radar ; Probability distributions

Surveying:405.3 ; Radar Systems and Equipment:716.2 ; Optical Devices and Systems:741.3 ; Probability Theory:922.1

Web of Science

1.Department of Mechanical Engineering, The University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
2.School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, People's Republic of China

Chongjian Yuan,Wei Xu,Xiyuan Liu,et al. Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry[J]. IEEE Robotics and Automation Letters,2022,PP(99):1-8.

Chongjian Yuan,Wei Xu,Xiyuan Liu,Xiaoping Hong,&Fu Zhang.(2022).Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry.IEEE Robotics and Automation Letters,PP(99),1-8.

Chongjian Yuan,et al."Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry".IEEE Robotics and Automation Letters PP.99(2022):1-8.