基于多传感器融合技术的室内移动机器人定位研究  

Indoor mobile robot localization based on multi-sensor fusion technology

作　　者：张书亮 谭向全[1,3] 吴清文 ZHANG Shuliang;TAN Xiangquan;WU Qingwen(Space Robot Engineering Center,Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Science,Changchun 130033,China;College of Materials Science and Opto-Electronic Technology,University of Chinese Academy of Science,Beijing 100049,China;CAS Key Laboratory of On-orbit Manufacturing and Integration for Space Optics Systems,Changchun 130033,China)

机构地区：[1]中国科学院长春光学精密机械与物理研究所空间机器人工程中心,吉林长春130033 [2]中国科学院大学材料科学与光电技术学院,北京100049 [3]中国科学院空间光学系统在轨制造与集成重点实验室,吉林长春130033

出　　处：《传感器与微系统》2021年第8期53-56,共4页Transducer and Microsystem Technologies

摘　　要：为解决室内环境中移动机器人使用单一传感器定位精度低和稳定性差的问题,提出了一种融合了轮式里程计、惯性测量单元(IMU)、超宽带(UWB)和激光雷达定位数据的多传感器融合定位方法。基于扩展卡尔曼滤波(EKF)算法和自适应蒙特—卡罗定位(AMCL)算法,依次对不同传感器定位数据进行融合。实验结果表明:多传感器融合有助于室内移动机器人获得更精确的定位和较好的定位稳定性。In order to solve the problem of low localization accuracy and poor stability of mobile robots using a single sensor in an indoor environment,a multi-sensor fusion localization method was proposed,which combines the localization data of wheel odometer,inertial measurement unit(IMU),ultra wide band(UWB)and LiDAR.The algorithm is based on the extended Kalman filtering(EKF)algorithm and the adaptive Monte Carlo localization(AMCL)algorithm,which sequentially fuse the localization data of different sensors.Experimental results show that multi-sensor fusion is helpful for indoor mobile robots to obtain more accurate localization and better localization stability.

关 键 词：多传感器融合定位 移动机器人 扩展卡尔曼滤波 自适应蒙特—卡罗定位 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]