一种基于IEKF的轮式移动机器人自定位算法  

A self-localization algorithm for wheeled mobile robot based on IEKF

作　　者：许垚 孙勇智[1] 马连伟 XU Yao;SUN Yongzhi;MA Lianwei(School of Automation and Electrical Engineering,Zhejiang University of Science and Technology,Hangzhou 310023,Zhejiang,China;School of Information Science and Engineering,NingboTech University,Ningbo 315100,Zhejiang,China)

机构地区：[1]浙江科技学院自动化与电气工程学院,杭州310023 [2]浙大宁波理工学院信息科学与工程学院,浙江宁波315100

出　　处：《浙江科技学院学报》2021年第1期39-45,共7页Journal of Zhejiang University of Science and Technology

基　　金：教育部产学合作协同育人项目(201902321004)。

摘　　要：为使移动机器人在导航时能满足定位要求,提高其定位精度,提出一种将里程计、相机传感器和激光雷达信息进行融合的自定位算法。根据机器人的机械结构和运动方式对其建立运动学模型,由里程计推算出机器人在不同时刻的位置估计;利用相机传感器对环境中的路标特征进行识别并计算出两者之间的距离和夹角;将激光雷达所获得的机器人相对路标特征的距离和角度与相机传感器的信息进行匹配,并利用迭代扩展卡尔曼滤波(iterated extended Kalman filter,IEKF)算法融合里程计信息,最终得到较为精确的机器人的位置估计。仿真试验结果表明,该多传感器融合算法相对传统的定位方法具有更高的定位精度。In order to tailor the mobile robot to meet the localization requirements and improve its localization accuracy when navigating,a self-localization algorithm was proposed on the basis of fusing odometer,vision sensor and laser radar information.According to mechanical structure and motion mode of the robot,its kinematics model was established,calculating the localization estimation of the robot at different time with the odometer;then the vision sensor identified landmark features in the environment and calculated the distance and angle between the robot and the landmark.Moreover,the laser radar acquired the same information between them.After matching the two sensors data,the iterated extended Kalman filter(IEKF)algorithm was used to fuse the matched data and the odometer information,ultimately obtaining a more accurate localization estimation of the robot.The experimental results show that the multi-sensor fusion algorithm has higher localization accuracy than the traditional method.

关 键 词：移动机器人 定位 卡尔曼滤波 多传感器融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]