Research on autonomous navigation algorithm of agricultural machinery agent based on EKF

In order to obtain more accurate, comprehensive and real-time farmland obstacle information and improve the autonomous navigation and positioning accuracy of agricultural machinery agents, a combined positioning method based on Beidou system and visual navigation was proposed. In this paper, a data fusion algorithm based on extended Kalman filter（EKF） is designed, which selects BDS and visual CCD as external sensors. The algorithm integrates BDS and visual sensor data to locate the real-time location of the agent. The system completes absolute positioning by tracking navigation angle and driving progress. Through machine vision image processing, navigation reference and target information are obtained to complete relative positioning. The effectiveness of the algorithm is verified by experiments, and the results of Kalman filter（KF） are compared and analyzed. The results show that the filtered path is smoother, the jitter deviation is reduced, and the coordinate data is more stable and smoother than the KF filtering result. In addition, the mean distance error can be reduced from 0.119 5 m before filtering to 0.070 m after filtering, effectively reducing the process noise. And the position deviation is within ±0.1 m, the accuracy is higher, which can improve the positioning accuracy of intelligent agricultural machinery autonomous navigation.