无里程计移动机器人多传感器融合定位算法  

Multi-Sensor Fusion Localization Algorithm of Mobile Robot without Odometer

作　　者：张礼睿 孙勇智[1] 刘力铭 李津蓉[1] 许垚 ZHANG Li-rui;SUN Yong-zhi;LIU Li-ming;LI Jin-rong;XU Yao(School of Automation and Electrical Engineering,Zhejiang University of Science and Technology,Hangzhou 310023,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

出　　处：《组合机床与自动化加工技术》2023年第2期109-112,共4页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：教育部产学合作协同育人项目(201901014014)。

摘　　要：为解决无里程计移动机器人室内定位问题,提出一种不依赖里程计数据的多传感器融合定位算法。首先,使用RF2O算法从激光雷达信息中获取移动机器人的运动数据;其次,使用扩展卡尔曼滤波融合IMU数据和RF2O算法计算的数据得到初步的定位数据;最后,使用自适应蒙特卡洛定位算法对定位数据进行修正,解决误差累计问题,得到精确的定位结果。结果表明,本文提出的定位算法可以有效降低测量误差、缩短算法运行时间,具有定位精度高且计算量小的优点,可以满足无里程计的移动机器人的定位需求。In order to solve the indoor localization problem of mobile robot without odometer, a localization algorithm based on multi-sensor fusion that does not rely on odometer data is proposed.RF2O algorithm is used to calculate the motion data of the machine from radar data, and extended Kalman filter is used to fuse the inertial measurement unit data and RF2O algorithm to get the preliminary positioning data.Finally, Adaptive Monte Carlo localization algorithm is used to modify the data in order to avoid the problem of error accumulation and obtain accurate positioning data.The results show that the positioning system proposed in this paper can effectively reduce the measurement error, shorten the running time of the algorithm, and has the advantages of high positioning precision and low computational cost, which makes it suitable for indoor mobile robot without odometer that require localization.

关 键 词：室内定位 多传感器融合 RF2O算法 扩展卡尔曼滤波 自适应蒙特卡洛定位 

分 类 号：TH165[机械工程—机械制造及自动化] TG659[金属学及工艺—金属切削加工及机床]