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Volume 24
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10.3390/s24237546
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

LIO-SAM++: A Lidar-Inertial Semantic SLAM with Association Optimization and Keyframe Selection

by
Bingke Shen
1,2,
Wenming Xie
1,2,*,
Xiaodong Peng
1,2,3,
Xiaoning Qiao
1,2 and
Zhiyuan Guo
1,2
1
National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Hangzhou Insititute for Adcanced Study, Chinese Academy of Sciences, Hangzhou 310024, China
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(23), 7546; https://doi.org/10.3390/s24237546
Submission received: 24 September 2024 / Revised: 8 November 2024 / Accepted: 25 November 2024 / Published: 26 November 2024
(This article belongs to the Special Issue Simultaneous Localization and Mapping (SLAM) and Artificial Intelligence (AI) Based Localization for Positioning Applications and Mobile Robot Navigation—Second Edition)
Versions Notes

Abstract

Current lidar-inertial SLAM algorithms mainly rely on the geometric features of the lidar for point cloud alignment. The issue of incorrect feature association arises because the matching process is susceptible to influences such as dynamic objects, occlusion, and environmental changes. To address this issue, we present a lidar-inertial SLAM system based on the LIO-SAM framework, combining semantic and geometric constraints for association optimization and keyframe selection. Specifically, we mitigate the impact of erroneous matching points on pose estimation by comparing the consistency of normal vectors in the surrounding region. Additionally, we incorporate semantic information to establish semantic constraints, further enhancing matching accuracy. Furthermore, we propose an adaptive selection strategy based on semantic differences between frames to improve the reliability of keyframe generation. Experimental results on the KITTI dataset indicate that, compared to other systems, the accuracy of the pose estimation has significantly improved.
Keywords: lidar-inertial SLAM; semantic information; association optimization; keyframe selection lidar-inertial SLAM; semantic information; association optimization; keyframe selection

Share and Cite

MDPI and ACS Style

Shen, B.; Xie, W.; Peng, X.; Qiao, X.; Guo, Z. LIO-SAM++: A Lidar-Inertial Semantic SLAM with Association Optimization and Keyframe Selection. Sensors 2024, 24, 7546. https://doi.org/10.3390/s24237546

AMA Style

Shen B, Xie W, Peng X, Qiao X, Guo Z. LIO-SAM++: A Lidar-Inertial Semantic SLAM with Association Optimization and Keyframe Selection. Sensors. 2024; 24(23):7546. https://doi.org/10.3390/s24237546

Chicago/Turabian Style

Shen, Bingke, Wenming Xie, Xiaodong Peng, Xiaoning Qiao, and Zhiyuan Guo. 2024. "LIO-SAM++: A Lidar-Inertial Semantic SLAM with Association Optimization and Keyframe Selection" Sensors 24, no. 23: 7546. https://doi.org/10.3390/s24237546

APA Style

Shen, B., Xie, W., Peng, X., Qiao, X., & Guo, Z. (2024). LIO-SAM++: A Lidar-Inertial Semantic SLAM with Association Optimization and Keyframe Selection. Sensors, 24(23), 7546. https://doi.org/10.3390/s24237546

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