An improved scan matching algorithm in SLAM

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI:10.1109/ICSAI48974.2019.9010259

Hongkai Zhang, Niansheng Chen, Guangyu Fan, Dingyu Yang

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引用次数: 2

Abstract

Simultaneous localization and mapping (SLAM) technology has always been the research focus of robot navigation in unknown environment. Aiming at the problem of cumulative errors of robot pose in the localization process of SLAM algorithm based on particle filter, a loop detection algorithm based on graph-SLAM was proposed. The algorithm uses constraints to adjust the robot attitude at different moments. In this paper, the constraint refers to the scanning matching of lidar. In the process of drawing, when the robot returns to the known area, if the current laser scanning is successfully matched with the previous laser scanning, the robot's posture can be adjusted to eliminate the accumulated errors caused by the odometer. In the process of laser scanning matching, the method of grouping step threshold value judgment is proposed to match the laser point cloud, which can effectively reduce the computation. Experimental results show that the proposed algorithm can effectively eliminate the cumulative errors of positioning and achieve a better mapping effect.

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一种改进的SLAM扫描匹配算法

同时定位与制图技术一直是未知环境下机器人导航的研究热点。针对基于粒子滤波的SLAM算法在定位过程中存在的机器人位姿累积误差问题，提出了一种基于图SLAM的环检测算法。该算法利用约束来调整机器人在不同时刻的姿态。本文的约束是指激光雷达的扫描匹配。在绘制过程中，当机器人返回到已知区域时，如果当前激光扫描与之前的激光扫描匹配成功，则可以调整机器人的姿态，以消除里程表造成的累积误差。在激光扫描匹配过程中，提出了分组步长阈值判断的方法对激光点云进行匹配，有效地减少了计算量。实验结果表明，该算法能有效消除定位累积误差，获得较好的映射效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2019 6th International Conference on Systems and Informatics (ICSAI)

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