IMU Augment Tightly Coupled Lidar-Visual-Inertial Odometry for Agricultural Environments

This letter presents a new tightly coupled LiDAR-visual-inertial odometry scheme for agricultural autonomous machinery under a structureless environment and the presence of fluctuation uncertainties. By proposing the robust adaptive filter, the effects of unknown disturbances and noises are significantly addressed. In the meantime, the IMU orientation is effectively estimated by the great capability of an error state Kalman filter (ESKF). The IMU attitude estimation is integrated to significantly improve the accuracy of both LiDAR and visual odometry. Hence, the suggested approach obtains the perfect output performance, smooth trajectory, and robustness against uncertainties. Finally, the effectiveness of the proposed LiDAR-visual-odometry is confirmed with the real-time experiment of different scenarios.