基于UKF的ArUco码和轮式编码器融合定位算法  

Research on Fusion Localization Algorithm of ArUco Code and Wheel Encoder Based on UKF

作　　者：许佳斌 吴永明[1] 梁济民 王卫军 韩彰秀 李根 XU Jia-bin;WU Yong-ming;LIANG Ji-min;WANG Wei-jun;HAN Zhang-xiu;LI Gen(School of Electromechanical Engineering,Guangdong University of Technology,Guangzhou 510006,China;Guangzhou Institute of Advanced Technology,Guangzhou 511458,China)

机构地区：[1]广东工业大学机电工程学院,广州510006 [2]广州先进技术研究所,广州511458

出　　处：《组合机床与自动化加工技术》2022年第10期1-4,共4页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：国家重点研发计划子课题(2018YFA0902901);国家自然科学基金联合基金(U1813222)。

摘　　要：为提高机器人的定位精度和鲁棒性,避免使用激光雷达和高精度惯性测量单元(inertial measurement unit,IMU)等高成本传感器,采用一种ArUco码作为人工标识,相机作为主要的传感器定位方法。进一步,提出基于无迹卡尔曼滤波(unscented kalman filter,UKF)的融合定位算法,其融合了视觉信息和轮式编码器信息,有效的降低了光照变化、运动模糊、路面不平对定位精度的影响。通过搭建基于ROS(机器人操作系统)的实验平台,对比了三种定位方法,验证了UKF融合定位算法可以有效提高定位精度和鲁棒性。The complexity of industrial environment poses many difficulties to autonomous mobile robot localization,to improve the localization accuracy and robustness in such environment,and avoid the use of high-cost sensors such as Lidar and high-precision inertial measurement unit(IMU),we adopt ArUco code as the artificial landmarks and camera as the core localization sensor.Further,a localization algorithm based on unscented kalman filter(UKF)is proposed,which integrates visual information and wheel encoder information.As a results,it can effectively reduce the influence of illumination changes,motion blur,and road unevenness on localization accuracy.By building an experimental robot based on ROS(robot operating system),our method is compared with two other methods,it is verified that the algorithm can effectively improve localization accuracy and robustness.

关 键 词：视觉定位 多传感器融合 无迹卡尔曼滤波 ArUco码 

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]