基于2D激光SLAM的移动机器人系统研究  

Research on Mobile Robot System Based on 2D Laser Slam

作　　者：王庆辉 张建英 WANG Qinghui;ZHANG Jianying(School of Information Engineering,Shenyang University of Chemical Technology,Shenyang 110142)

机构地区：[1]沈阳化工大学信息工程学院,沈阳110142

出　　处：《计算机与数字工程》2025年第1期57-62,共6页Computer & Digital Engineering

摘　　要：在室内移动机器人系统下地图构建对于机器人的精准定位导航具有重要意义,采用基于图优化理论的Cartographer算法,融合激光雷达、里程计和IMU传感器数据完成机器人的建图功能。针对在低成本硬件条件下算法建图效果较差,使用轮式里程计后容易出现地图漂移或者障碍物消失的问题,提出了采用扩展卡尔曼滤波(Extended Kalman Filter,EKF)融合里程计和IMU数据的方法,结合Cartographer算法提供更准确的初始位姿估计,减小前端匹配的累计误差。其次通过参数优化调整方法使算法更好的适配硬件系统。同时在自主搭载的机器人硬件平台上,对比算法优化前后的建图效果。实验结果表明该方案可以有效改进建图的效果。Map building in indoor mobile robot system is of great significance to the robot's precise positioning and navigation.Cartographer algorithm based on graph optimization theory is adopted,and the data of lidar,odometer and IMU sensor are fused to complete the robot's map building function.Aiming at the problem that the algorithm has poor mapping effect under the condition of low-cost hardware,and the map drifts or obstacles disappear easily after using wheeled odometer,a method of fusing odometer and IMU data by using extended Kalman filter(EKF)is proposed,which combines with Cartographer algorithm to provide more accu⁃rate initial pose estimation and reduce the cumulative error of front-end matching.Secondly,the algorithm is better adapted to the hardware system by parameter optimization and adjustment.At the same time,on the robot hardware platform,the mapping effect before and after the algorithm optimization is compared.The experimental results show that this scheme can effectively improve the effect of mapping.

关 键 词：同时定位与建图 Cartographer EKF 图优化 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]