Abstract
LiDAR Simultaneous Localization and Mapping (SLAM) plays a crucial role in intelligent robotics, finding extensive applications in autonomous driving and exploration. The traditional feature-based LiDAR SLAM holds a prominent position due to its robustness and accuracy. However, these methods still exhibit limitations in point cloud preprocessing and feature extraction. In this paper, we introduce an effective LiDAR SLAM method to address these issues. Specifically, we propose a novel Concentric Cluster Model (CCM) for clustering point clouds, aiming to preserve stable point clouds and eliminate the unstable ones. Additionally, we propose a Step-by-step Feature Extraction (SFE), which significantly enhances the effect of traditional feature extraction methods. We test the proposed SLAM method on several sequences of the KITTI odometry, M2DGR, and M2DGR-plus datasets. Experimental results show that our method achieves superior accuracy compared to several state-of-the-art LiDAR SLAM methods, while maintaining real-time performance.
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Funding
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62273034, 61973029, and 62076026) and the Scientific and Technological Innovation Foundation of Foshan (BK21BF004).
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All authors contributed to the research. Yang Ren: Contributed to the conception of the study, performed the experiment, performed data analyses and wrote manuscript. Hui Zeng contributed significantly to analysis and manuscript edition. Yiyou Liang helped perform the analysis with constructive discussions.
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Ren, Y., Zeng, H. & Liang, Y. SFE-SLAM: an effective LiDAR SLAM based on step-by-step feature extraction. Appl Intell 55, 87 (2025). https://doi.org/10.1007/s10489-024-05963-4
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DOI: https://doi.org/10.1007/s10489-024-05963-4