多传感器信息融合在移动机器人定位中的应用  

Application of multi-sensor information fusion in localization of mobile robot

作　　者：陈小宁[1] 黄玉清[1] 杨佳[1] 

机构地区：[1]西南科技大学信息工程学院,四川绵阳621010

出　　处：《传感器与微系统》2008年第6期110-113,共4页Transducer and Microsystem Technologies

基　　金：国防基础科研计划资助项目(A3120060264)

摘　　要：机器人自定位是实现自主导航的关键问题之一。为了满足机器人在导航时精确定位的要求,提出一种基于多传感器信息融合的自定位算法。根据对机器人运动机构的分析和运动机构间的刚体约束,建立起机器人的运动学模型;由传感器的工作原理建立里程计和超声波传感器的观测模型;利用扩展卡尔曼滤波(EKF)算法将里程计和超声波传感器采集的数据进行融合;最后,由匹配的环境特征对机器人的位置进行修正,得到精确的位置估计。实验结果表明:该算法明显地消除了里程计的累计误差,有效地提高了定位精度。Robot self-localization is one of the most important issues to tackle in autonomous navigation. In order to meet the requirement of accurate localization, a self-localization algorithm based on multi-sensor information fusion is proposed. The kinematics model is made based on the analyzing kinematics architecture of mobile robot and the rigid restrictions ; the measurement model of odometric and ultrasonic sensors are built based on the theory of sensors; the data provided by odometric and ultrasonic sensors are fused together by means of an extended Kalman filter （EKF） technique. Finally, the position of robot is reset by matched environment feature, and the position estimation of robot is given accurately. Experiment result proves that the proposed algorithm eliminates the cumulative errors of odometry obviously, and improves the localization precision efficiently.

关 键 词：移动机器人 多传感器 信息融合 定位 扩展卡尔曼滤波 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]