A Loosely Coupled Extended Kalman Filter Algorithm for Agricultural Scene-Based Multi-Sensor Fusion - Lv, Meibo; Wei, Hairui; Fu, Xinyu; Wang, Wuwei; Zhou, Daming (Frontiers in Plant Science, 2022)
With the problem of the aging population and the development of modern agriculture, the use of agricultural robots for large-scale agricultural production activities will become a major trend in the future. In particular, autonomous navigation technology is crucial for robots to operate properly. However, there is still a problem of external noise and other factors causing the failure of the navigation system. To solve this problem, we propose an agricultural scene-based multi-sensor fusion method via a loosely coupled extended Kalman filter algorithm to reduce interference from external environment. Specifically, the proposed method fuses inertial measurement unit (IMU), robot Odometer (ODOM), global positioning navigation system (GPS), and visual inertial Odometer (VIO), and uses visualization tools to simulate and analyze the robot trajectory and error. In experiments, we verify the accuracy of the proposed algorithm for sensors failure, and analyze the robustness of the fusion algorithm. The experimental results show that the proposed algorithm has better accuracy and robustness on the agricultural dataset than other algorithms.