SFE-SLAM: an effective LiDAR SLAM based on step-by-step feature extraction

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.