Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental Voxels

• Published in: IEEE robotics and automation letters Vol. 7; no. 2; pp. 4861 - 4868
• Main Authors: Bai, Chunge, Xiao, Tao, Chen, Yajie, Wang, Haoqian, Zhang, Fang, Gao, Xiang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Data structures; Graphics processing units; Hilbert curve; Laser radar; Lidar; Lidar-inertial odometry; nearest neighbor; Point cloud compression; Queries; Simultaneous localization and mapping; SLAM; Solid state; Spatial data; Spatial databases; Spinning; Tracking
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2022.3152830

This letter presents an incremental voxel-based lidar-inertial odometry (LIO) method for fast-tracking spinning and solid-state lidar scans. To achieve the high tracking speed, we neither use complicated tree-based structures to divide the spatial point cloud nor the strict k nearest neighbor (k-NN) queries to compute the point matching. Instead, we use the incremental voxels (iVox) as our point cloud spatial data structure, which is modified from the traditional voxels and supports incremental insertion and parallel approximated k-NN queries. We propose the linear iVox and PHC (Pseudo Hilbert Curve) iVox as two alternative underlying structures in our algorithm. The experiments show that the speed of iVox reaches 1000-2000 Hz per scan in solid-state lidars and over 200 Hz for 32 lines spinning lidars only with a modern CPU while still preserving the same level of accuracy.