| With the development of science and technology,the application of mobile robot has become very extensive,which is of great significance to the development of the national economy and the people's livelihood.How to ensure that mobile robots can successfully complete tasks in complex environments is particularly important.Simultaneous Localization and Mapping(SLAM)technology is the key factor to improve the autonomous navigation ability of mobile robot,which has become a hot spot in the field of robot.In this paper,the Lidar is considered as the observation sensor,the SLAM technology of robot positioning and navigation is deeply studied,The extended Kalman filter(EKF)algorithm is utilized to realize the autonomous positioning of mobile robot.In order to improve the accuracy of pose and position for the mobile robot,the EKF filter algorith is modified.Aiming at the deviation of mobile robot dynamic model,an adaptive extended Kalman filter based simultaneous localization and mapping(AEKF-SLAM)method is proposed.The adaptive factor is introduced in the prediction stage of EKF filter algorithm to reduce the truncation error caused by linearization,The prediction process of mobile robot is simulated and analyzed by setting environmental road markings and determining trajectory.Through simulation analysis,the AEKF-SLAM algorithm can improve the accuracy of estimation for the environmental landmark in the prediction stage,and has better positioning accuracy than EKF-SLAM.Aiming at the problem of angular deviation in lidar,the reason of angular deviation is obtained through the calculation and analysis of the double axis structure of precision turntable in lidar.In this paper,the pitch angle error and yaw angle error forming angular deviation are analyzed theoretically,and an augmented state extended Kalman filter simultaneous localization and mapping(ASEKF-SLAM)method is proposed to reduce the impact of observation model deviation on navigation and mapping accuracy.Compared with EKF-SLAM method,this method can improve the accuracy of mobile robot's estimation of road signs to a certain extent,and has important reference value for the development of mobile robot slam technology.In order to realize the autonomous navigation function of mobile robot,aiming at the two defects local minimum reduction and target unreachability which exists in the conventional artificial potential field based method,the calculation of repulsion and gravity in the traditional algorithm is improved to effectively reduce the impact of the above two defects on autonomous navigation.The experimental simulation results show that the improved artificial potential field path planning method can effectively reduce the impact of defects and achieve the purpose of optimal path planning. |
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