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

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)...

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Vydané v:	IEEE robotics and automation letters Ročník 7; číslo 2; s. 4861 - 4868
Hlavní autori:	Bai, Chunge, Xiao, Tao, Chen, Yajie, Wang, Haoqian, Zhang, Fang, Gao, Xiang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.04.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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Abstract	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.
AbstractList	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.
Author	Bai, Chunge Zhang, Fang Xiao, Tao Wang, Haoqian Gao, Xiang Chen, Yajie
Author_xml	– sequence: 1 givenname: Chunge orcidid: 0000-0002-2114-3073 surname: Bai fullname: Bai, Chunge email: bcg971004@gmail.com organization: Department of Electronic Information and Engineering, Tsinghua University, Beijing, China – sequence: 2 givenname: Tao surname: Xiao fullname: Xiao, Tao email: xiaotao@idriverplus.com organization: Idriver+ Technologies Company Ltd., Beijing, China – sequence: 3 givenname: Yajie surname: Chen fullname: Chen, Yajie email: yajie.cw@outlook.com organization: Idriver+ Technologies Company Ltd., Beijing, China – sequence: 4 givenname: Haoqian orcidid: 0000-0003-2792-8469 surname: Wang fullname: Wang, Haoqian email: wanghaoqian@tsinghua.edu.cn organization: Department of Electronic Information and Engineering, Tsinghua University, Beijing, China – sequence: 5 givenname: Fang surname: Zhang fullname: Zhang, Fang email: zhangfang@idriverplus.com organization: Idriver+ Technologies Company Ltd., Beijing, China – sequence: 6 givenname: Xiang orcidid: 0000-0002-3070-9980 surname: Gao fullname: Gao, Xiang email: gao.xiang.thu@gmail.com organization: Idriver+ Technologies Company Ltd., Beijing, China
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Snippet	This letter presents an incremental voxel-based lidar-inertial odometry (LIO) method for fast-tracking spinning and solid-state lidar scans. To achieve the...
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SubjectTerms	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
Title	Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental Voxels
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