Robust Real-time LiDAR-inertial Initialization

Fangcheng Zhu, Yunfan Ren, Fu Zhang

Research output: Chapter in Book/Conference Proceeding/ReportConference Paper published in a bookpeer-review

73 Citations (Scopus)

Abstract

For most LiDAR-inertial odometry, accurate initial states, including temporal offset and extrinsic transfor-mation between LiDAR and 6-axis IMUs, play a significant role and are often considered as prerequisites. However, such information may not be always available in customized LiDAR-inertial systems. In this paper, we propose LI-Init: a full and real-time LiDAR-inertial system initialization process that calibrates the temporal offset and extrinsic parameter between LiDARs and IMUs, and also the gravity vector and IMU bias by aligning the state estimated from LiDAR measurements with that measured by IMU. We implement the proposed method as an initialization module, which can automatically detects the degree of excitation of the collected data and calibrate, on-the-fly, the temporal offset, extrinsic, gravity vector, and IMU bias, which are then used as high-quality initial state values for real-time LiDAR-inertial odometry systems. Experiments conducted with different types of LiDARs and LiDAR-inertial combinations show the robustness, adaptability and efficiency of our initialization method. The implementation of our LiDAR-inertial initialization procedure LI-Init and test data are open-sourced on Github11https://www.github.com/hku-mars/LiDAR IMU Init and also integrated into a state-of-the-art LiDAR-inertial odometry system FAST-LIO2.

Original languageEnglish
Title of host publication2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3948-3955
Number of pages8
ISBN (Electronic)9781665479271
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022 - Kyoto, Japan
Duration: 23 Oct 202227 Oct 2022

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
Volume2022-October
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
Country/TerritoryJapan
CityKyoto
Period23/10/2227/10/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

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