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A multi-metric LiDAR Simultaneous Localization and Mapping system based on intensity feature assistance and two-step decoupling estimation

Published online by Cambridge University Press:  22 July 2025

Zhenghui Xu Open the ORCID record for Zhenghui Xu [Opens in a new window] ,
Jian Li ,
Shimin Wei ,
Ling Tang  and
Huanlong Chen
Zhenghui Xu
Affiliation:
School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing, China
Jian Li*
Affiliation:
School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing, China
Shimin Wei
Affiliation:
School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing, China
Ling Tang
Affiliation:
School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing, China
Huanlong Chen
Affiliation:
Shanghai Institute of Aerospace System Engineering, Shanghai, China
*
Corresponding author: Jian Li; Email: jianli_628@126.com
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Abstract

To address the problems of accuracy degradation, localization drift, and even failure of Simultaneous Localization and Mapping (SLAM) algorithms in unstructured environments with sparse geometric features, such as outdoor parks, highways, and urban roads, a multi-metric light detection and ranging (LiDAR) SLAM system based on the fusion of geometric and intensity features is proposed. Firstly, an adaptive method for extracting multiple types of geometric features and salient intensity features is proposed to address the issue of insufficient sparse feature extraction. In addition to extracting traditional edge and planar features, vertex features are also extracted to fully utilize the geometric information, and intensity edge features are extracted in areas with significant intensity changes to increase multi-level perception of the environment. Secondly, in the state estimation, a multi-metric error estimation method based on point-to-point, point-to-line, and point-to-plane is used, and a two-step decoupling strategy is employed to enhance pose estimation accuracy. Finally, qualitative and quantitative experiments on public datasets demonstrate that compared to state-of-the-art pure geometric and intensity-assisted LiDAR SLAM algorithms, our proposed algorithm achieves superior localization accuracy and mapping clarity, with an ATE accuracy improvement of 28.93% and real-time performance of up to 62.9 ms. Additionally, test conducted in real campus environments further validates the effectiveness of our approach in complex, unstructured scenarios.

Keywords

LiDAR Simultaneous Localization and Mappingmulti-metric systemintensity featuretwo-step decoupling

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Type
Research Article
Information
Robotica , First View , pp. 1 - 20
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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