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Outdoor LiDAR-inertial SLAM using ground constraints

Published online by Cambridge University Press:  26 February 2024

Yating Hu ,
Qigao Zhou ,
Zhejun Miao ,
Hang Yuan  and
Shuang Liu Open the ORCID record for Shuang Liu [Opens in a new window]
Yating Hu
Affiliation:
School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China
Qigao Zhou
Affiliation:
School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China
Zhejun Miao
Affiliation:
School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China
Hang Yuan
Affiliation:
School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China
Shuang Liu*
Affiliation:
School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China
*
Corresponding author: Shuang Liu; Email: shuangliu@ecust.edu.cn
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Abstract

The current LiDAR-inertial odometry is prone to cumulative Z-axis error when it runs for a long time. This error can easily lead to the failure to detect the loop-closing in the correct scenario. In this paper, a ground-constrained LiDAR-inertial SLAM is proposed to solve this problem. Reasonable constraints on the ground motion of the mobile robot are incorporated to limit the Z-axis drift error. At the same time, considering the influence of initial positioning error on navigation, a keyframe selection strategy is designed to effectively improve the flatness and accuracy of positioning and the efficiency of loop detection. If GNSS is available, the GNSS factor is added to eliminate the cumulative error of the trajectory. Finally, a large number of experiments are carried out on the self-developed robot platform to verify the effectiveness of the algorithm. The results show that this method can effectively improve location accuracy in outdoor environments, especially in environments of feature degradation and large scale.

Keywords

outdoor positioningLiDAR-inertial SLAMground constraintskeyframe selection
Type
Research Article
Information
Robotica , Volume 42 , Issue 4 , April 2024 , pp. 1246 - 1261
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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