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A 2D-LiDAR-based localization method for indoor mobile robots using correlative scan matching

Published online by Cambridge University Press:  04 December 2024

Song Du Open the ORCID record for Song Du [Opens in a new window] ,
Tao Chen ,
Zhonghui Lou  and
Yijie Wu Open the ORCID record for Yijie Wu [Opens in a new window]
Song Du
Affiliation:
State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou, China
Tao Chen
Affiliation:
State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou, China
Zhonghui Lou
Affiliation:
State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou, China
Yijie Wu*
Affiliation:
State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou, China
*
Corresponding author: Yijie Wu; Email: [email protected]
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Abstract

Precise pose estimation is crucial to various robots. In this paper, we present a localization method using correlative scan matching (CSM) technique for indoor mobile robots equipped with 2D-LiDAR to provide precise and fast pose estimation based on the common occupancy map. A pose tracking module and a global localization module are included in our method. On the one hand, the pose tracking module corrects accumulated odometry errors by CSM in the classical Bayesian filtering framework. A low-pass filter associating the predictive pose from odometer with the corrected pose by CSM is applied to improve precision and smoothness of the pose tracking module. On the other hand, our localization method can autonomously detect localization failures with several designed trigger criteria. Once a localization failure occurs, the global localization module can recover correct robot pose quickly by leveraging branch-and-bound method that can minimize the volume of CSM-evaluated candidates. Our localization method has been validated extensively in simulated, public dataset-based, and real environments. The experimental results reveal that the proposed method achieves high-precision, real-time pose estimation, and quick pose retrieve and outperforms other compared methods.

Keywords

correlative scan matchingpose trackinglow-pass filterbranch and boundglobal localization
Type
Research Article
Information
Robotica , First View , pp. 1 - 28
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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