基于EKF的多传感器融合定位算法研究
Research on multi-sensor fusion localization algorithm based on EKF
颜俊杰 1蔡芸 2蒋林 2王振宇 1廖雅曼1
作者信息
- 1. 武汉科技大学 冶金装备及其控制教育部重点实验室,湖北 武汉 430081
- 2. 武汉科技大学 冶金装备及其控制教育部重点实验室,湖北 武汉 430081;武汉科技大学 机器人与智能系统研究院,湖北 武汉 430081
- 折叠
摘要
针对室外移动机器人定位系统精度依靠传感器融合存在累计误差的问题,提出一种基于扩展卡尔曼滤波(EKF)的多传感器融合的室外移动机器人定位方法.通过实时差分定位(Real-Time Kinematic,RTK)和里程计信息、IMU信息对室外移动机器人进行扩展卡尔曼滤波融合定位,在真实室外环境中进行实验.实验结果表明:该算法能消除累计误差,提高机器人的定位精度,动态定位精度可达 2.5 cm以内,相较于里程计-IMU融合定位,误差减少了 92.4%左右,相较于传统的RTK算法,定位精度提高了 55.4%.多次实验表明,该算法具有较好的鲁棒性.
Abstract
Aiming at the problem that the accuracy of outdoor mobile robot positioning system depends on sensor fusion and has accumulated errors,a multi-sensor fusion method based on extended Kalman filter(EKF)was proposed for outdoor mobile robot positioning.The extended Kalman filter fusion localization of outdoor mobile robot was carried out through Real-Time Kinematic(RTK),odometer information and IMU information.Experiments were carried out in a real outdoor environment.The experimental results showed that the algorithm could eliminate the cumulative error,and the robot's positioning accuracy was significantly improved.The dynamic positioning accuracy could reach within 2.5 cm,compared with odometer IMU fusion localization,the error was reduced by about 92.4%;compared to the traditional RTK algorithm,the positioning accuracy was improved by 55.4%.Multiple experiments show that the algorithm has good robustness.
关键词
RTK定位/扩展的卡尔曼滤波/传感器融合/室外移动机器人Key words
RTK positioning/extended Kalman filter/sensor fusion/outdoor mobile robot引用本文复制引用
基金项目
国家重点研发计划项目(2019YFB1310000)
国家自然科学基金(51874217)
出版年
2024
国家科技期刊平台
NSTL
万方数据