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。
Simultaneous localization and mapping(SLAM)is a crucial technology for autonomous mobile robots and au-tonomous 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.
作者
刘铭哲
徐光辉
唐堂
钱晓健
耿明
LIU Mingzhe;XU Guanghui;TANG Tang;QIAN Xiaojian;GENG Ming(College of Communications Engineering,Army Engineering University,Nanjing 210000,China)
出处
《计算机工程与应用》
CSCD
北大核心
2024年第1期1-14,共14页
Computer Engineering and Applications
基金
国家自然科学基金(62071486)。
