FELC-SLAM: feature extraction and loop closure optimized lidar SLAM system

作者：Ruizhen Gao, Yuang Li, Baihua Li, Guoguang Li · 发表于：Measurement Science and Technology · 年份：2024 · DOI：10.1088/1361-6501/ad6e0c · 被引用次数：7 · 研究领域：Robotics and Sensor-Based Localization、Remote Sensing and LiDAR Applications、3D Surveying and Cultural Heritage

Abstract Simultaneous Localization and Mapping (SLAM) is one of the key technologies in robot navigation and autonomous driving, playing an important role in robot navigation. Due to the sparsity of LiDAR data and the singularity of point cloud features, accuracy loss of LiDAR SLAM can occur during point cloud matching and localization. In response to these issues, this paper proposes a LiDAR Measurement SLAM algorithm that integrates multi type geometric feature extraction and optimized point cloud registration algorithms. This article first adopts advanced ground segmentation methods and feature segmentation strategies, including ground features, edge features, planar features, and spherical features, to improve matching accuracy. In addition, this article improves the previous method for extracting edge and planar features, extracting clearer and more robust line and surface features to address the degradation of geometric features. Finally, by introducing a robust decoupling global registration method for loop closure detection in the backend of the system, the sparsity problem of distant point clouds and the degradation problem caused by the reduction of inner layers in point cloud registration were effectively solved. In the evaluation of the KITTI dataset, our algorithm reduced absolute trajectory error values by 60%, 29%, and 71% compared to LeGO-LOAM in multi loop and feature constrained scenarios (such as sequences 00, 01, and 02), respectively. The evaluation of th...