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基于2D激光雷达的SLAM算法研究综述 被引量:10

Review of SLAM Algorithm Based on 2D Lidar
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摘要 移动机器人导航功能的实现需要同时定位与建图(simultaneous localization and mapping,SLAM)和路径规划这两方面的技术,其中由SLAM技术生成的栅格地图是移动机器人运用路径规划算法的前提。2D激光SLAM由于其建图精度较高、性能稳定且价格便宜,在室内移动机器人中应用十分广泛。2D激光SLAM是指移动机器人在自身所处环境及位置先验信息未知的情况下,以2D激光雷达为主要传感器,感知周围环境信息,从而实现自身位姿的估计和地图的构建。将2D激光SLAM分为两部分,第一部分从激光测距原理入手,对三角法和飞行时间法进行了详细介绍和优缺点比较。第二部分从前端扫描匹配、后端优化、回环检测和地图构建这四个方面分别详细阐述了2D激光SLAM系统框架。同时对主流2D激光SLAM算法进行了深入分析和优缺点比较,并对激光SLAM未来的发展进行了展望。 The realization of mobile robot navigation needs simultaneous localization and mapping(SLAM)and path planning.The grid map generated by SLAM technology is the premise of mobile robot using path planning algorithm.2D laser SLAM is widely used in indoor mobile robots because of its high mapping accuracy,stable performance and low price.2D laser SLAM means that mobile robot uses 2D laser radar as the main sensor to sense the surrounding environment information when its own environment and location prior information are unknown,so as to realize its own pose estimation and map construction.The 2D laser SLAM is divided into two parts.In the first part,starting from the principle of laser ranging,the triangle method and time-of-flight method are introduced in detail and their advantages and disadvantages are compared.In the second part,the framework of 2D laser SLAM system is described in detail from four aspects:front-end scanning matching,back-end optimization,loop detection and map construction.At the same time,the main 2D laser SLAM algorithms are analyzed and compared,and the future development of laser SLAM is prospected.
作者 沈斯杰 田昕 魏国亮 袁千贺 SHEN Si-jie;TIAN Xin;WEI Guo-liang;YUAN Qian-he(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;School of Science,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处 《计算机技术与发展》 2022年第1期13-18,46,共7页 Computer Technology and Development
基金 国家自然科学基金项目(61873169)。
关键词 2D激光SLAM 前端扫描匹配 后端优化 回环检测 地图构建 2D laser SLAM front-end scanning matching back-end optimization loop detection map construction
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