The pose and motion of mobile robots on uneven terrain have become more complex and diverse,leading to a decrease in the accuracy and efficiency of robot pose estimation.Therefore,an accurate estimation method of mobile robot posture in unflat ter-rain is proposed.The kinematic function is obtained according to the motion characteristics and constraints of the mobile robot,which is input into the Mahony algorithm to get a rough attitude estimate.The pose estimation and odometer observation data are taken as the initial parameters of the EKF algorithm,and the pose estimation is performed by filtering and fusion processing.The PL-ICP point cloud matching method is combined to further update the pose estimation results.The updated pose estimation is used as the prior estimation of EKF algorithm and fused with other sensor data to obtain the preliminary pose estimation results.The pre-liminary pose estimation results are used as the optimization target of fruit fly optimization algorithm to obtain more accurate pose es-timation results of mobile robot.The experimental results show that the proposed method can effectively improve the accuracy and effi-ciency of pose estimation on the basis of ensuring the stable operation of the mobile robot,and obtain a good application effect.
关键词
移动机器人/运动学函数/位姿估计/EKF算法/PL-ICP点云匹配方法/果蝇优化算法
Key words
Mobile Robots/Kinematic Function/Pose Estimation/EKF Algorithm/PL-ICP Point Cloud Matching Method/Fruit Fly Optimization Algorithm
维普
万方数据