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BA-LIOM: tightly coupled laser-inertial odometry and mapping with bundle adjustment

Published online by Cambridge University Press:  04 January 2024

Ruyi Li
Affiliation:
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China
Xuebo Zhang*
Affiliation:
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China
Shiyong Zhang
Affiliation:
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China
Jing Yuan
Affiliation:
Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
Hui Liu
Affiliation:
Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
Songyang Wu
Affiliation:
Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
*
Corresponding author: Xuebo Zhang; Email: zhangxuebo@nankai.edu.cn

Abstract

We design a scheme for laser-inertial odometry and mapping with bundle adjustment (BA-LIOM), which can greatly mitigate the problem of undesired ground warping due to sparsity of laser scans and significantly reduce odometry drift. Specifically, an Inertial measurement unit (IMU)-assisted adaptive voxel map initialization algorithm is proposed and elaborately integrated with the existing framework LIO-SAM, allowing for accurate registration in the beginning of the localization and mapping process. In addition, to accommodate to fast-moving and structure-less scenarios, we design a tightly coupled odometry, which jointly optimizes both the IMU preintegration constraints and scan matching with adaptive voxel maps. The voxels (edge and plane, respectively) are updated with BA optimization. And then the accurate mapping result is obtained by performing local BA. The proposed BA-LIOM is thoroughly assessed using datasets collected from multiple platforms over a variety of environments. Experimental results show the superiority of BA-LIOM over the state-of-the-art methods in robustness and precision, especially for large-scale scenarios. BA-LIOM improves the accuracy of localization by $61\%$ and $73\%$ on the buildings and lawn datasets, respectively, and has a $29\%$ accuracy improvement over LIO-SAM on the KITTI datasets. A supplementary video can be accessed at https://youtu.be/5l4ZFhTc2sw.

Type
Research Article
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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