3D 激光雷达 SLAM 算法综述

无人平台在大范围环境中实现自主定位与导航的能力需求日益严苛,其中基于激光雷达的同步定位和绘图技术(SLAM)是主流的研究方案。在这项工作中,本文系统概述了3D激光雷达SLAM算法框架和关键模块,分析阐述了近年来的研究热点问题和未来发展趋势,梳理了3D激光雷达SLAM算法性能的评估标准,并据此选取目前较为成熟的具有代表性的6种开源3D激光雷达SLAM算法在机器人操作系统(ROS)中进行了测试评估,基于KITTI基准数据集,从KITTI官方精度标准、SLAM算法精度指标、算法耗时和处理帧率3方面进行了横向比较,结果表明,所选6种算法中LIO-SAM算法性能综合表现突出,其在00序列数据集的测试中,绝对轨迹误差(ATE)和相对位姿误差(RPE)的RMSE数据分别为1.303和0.028,算法处理的帧率(fps)为28.6,最后依据CiteSpace分析讨论了3D激光雷达SLAM技术的应用趋势。

Overview of 3D Lidar SLAM algorithms

The ability of unmanned platforms to achieve autonomous positioning and navigation in a wide range of environments is increasingly demanding, in which Lidar-based Simultaneous Localization and Mapping (SLAM) is the mainstream of research schemes. In this work, this paper systematically outlines the framework and key modules of 3D Lidar SLAM algorithm, analyses and describes recent research hotspot problems and future development trends, collates the evaluation criteria for the performance of 3D Lidar SLAM algorithm, based on these, selects six representative mature open source 3D Lidar SLAM algorithms, which are then tested and evaluated on the robot operating system (ROS), based on the KITTI benchmark data set, the parallel comparison is carried out from three aspects: KITTI official precision standard, SLAM algorithm precision index, algorithm time consuming and processing frame rate. The results show that the LIO-SAM algorithm has the best performance among the six algorithms. The RMSE data of ATE and RPE in the 00 sequence data set test are 1. 303 and 0. 028, respectively, and the processing frame rate of the algorithm is 28. 6. Finally, the application trend of 3D laser SLAM technology is discussed based on CiteSpace analysis.