Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (1): 1-14.DOI: 10.3778/j.issn.1002-8331.2308-0455

• Research Hotspots and Reviews • Previous Articles     Next Articles

Review of SLAM Based on Lidar

LIU Mingzhe, XU Guanghui, TANG Tang, QIAN Xiaojian, GENG Ming   

  1. College of Communications Engineering, Army Engineering University, Nanjing 210000, China
  • Online:2024-01-01 Published:2024-01-01



  1. 陆军工程大学 通信工程学院,南京 210000

Abstract: Simultaneous localization and mapping (SLAM) is a crucial technology for autonomous mobile robots and autonomous driving systems, with a laser scanner (also known as lidar) playing a vital role as a supporting sensor for SLAM algorithms. This article provides a comprehensive review of lidar-based SLAM algorithms. Firstly, it introduces the overall framework of lidar-based SLAM, providing detailed explanations of the functions of the front-end odometry, back-end optimization, loop closure detection, and map building modules, along with a summary of the algorithms used. Secondly, it presents descriptions and summaries of representative open-source algorithms in a sequential order of 2D to 3D and single-sensor to multi-sensor fusion. Additionally, it discusses commonly used open-source datasets, precision evaluation metrics, and evaluation tools. Lastly, it offers an outlook on the development trends of lidar-based SLAM technology from four dimensions: deep learning, multi-sensor fusion, multi-robot collaboration, and robustness research.

Key words: simultaneous localization and mapping, lidar, inertial, multi-sensor fusion

摘要: 即时定位与地图构建(simultaneous localization and mapping,SLAM)是自主移动机器人和自动驾驶的关键技术之一,而激光雷达则是支撑SLAM算法运行的重要传感器。基于激光雷达的SLAM算法,对激光雷达SLAM总体框架进行介绍,详细阐述前端里程计、后端优化、回环检测、地图构建模块的作用并总结所使用的算法;按由2D到3D,单传感器到多传感器融合的顺序,对经典的具有代表性的开源算法进行描述和梳理归纳;介绍常用的开源数据集,以及精度评价指标和测评工具;从深度学习、多传感器融合、多机协同和鲁棒性研究四个维度对激光雷达SLAM技术的发展趋势进行展望。

关键词: 即时定位与地图构建, 激光雷达, 惯性, 多传感器融合