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A fast and accurate tightly coupled Light Detection and Ranging-inertial odometry based on sparse voxel maps and Gauss-Newton method

Published online by Cambridge University Press:  13 October 2025

Wei Xie ,
Zi Ding Open the ORCID record for Zi Ding [Opens in a new window] ,
Langwen Zhang  and
Pengfei Lyu
Wei Xie
Affiliation:
Department of Automation Science and Engineering, South China University of Technology, Guangzhou, China
Zi Ding
Affiliation:
Department of Automation Science and Engineering, South China University of Technology, Guangzhou, China
Langwen Zhang*
Affiliation:
Department of Automation Science and Engineering, South China University of Technology, Guangzhou, China
Pengfei Lyu
Affiliation:
Department of Civil and Environmental Engineering, Kitami Institute of Technology, Kitami, Japan
*
Corresponding author: Langwen Zhang; E-mail: aulwzhang@scut.edu.cn
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Abstract

This work proposes an optimization approach for the time-consuming parts of Light Detection and Ranging (LiDAR) data processing and IMU-LiDAR data fusion in the LiDAR-inertial odometry (LIO) method. Two key novelties enable faster and more accurate navigation in complex, noisy environments. Firstly, to improve map update and point cloud registration efficiency, we employ a sparse voxel maps with a new update function to construct a local map around the mobile robot and utilize an improved Generalized Iterative Closest Point algorithm based on sparse voxels to achieve LiDAR point clouds association, thereby boosting both map updating and computational speed. Secondly, to enhance real-time accuracy, this paper analyzes the residuals and covariances of both IMU and LiDAR data in a tightly coupled manner, and achieves system state estimation by fusing sensor information through Gauss-Newton method, effectively mitigating localization deviations by appropriately weighting the LiDAR covariances. The performance of our method is evaluated against advanced LIO algorithms using eight open datasets and five self-collected campus datasets. Results show a 24.7–60.1% reduction in average processing time per point cloud frame, along with improved robustness and higher precision motion trajectory estimation in most cluttered and complex indoor and outdoor environments.

Keywords

Light Detection and Ranging-inertial odometrysensor fusionSimultaneous Localization and Mappingsparse voxel mapsGauss-Newton method

Information

Type
Research Article
Information
Robotica , First View , pp. 1 - 18
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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