Accurate semi-direct lidar-inertial odometry based on distance and normal direction

To improve the autonomous navigation capability of unmanned platforms, an accurate semi-direct lidar-inertial odometry algorithm with a new point cloud alignment evaluation metric is proposed. The lidar scan points are corrected through IMU (Inertial Measurement Unit) backward propagation, and the navigation states are predicted through IMU forward propagation. In the update of the navigation states, the point cloud alignment is evaluated based on the distances and the minimum normal direction deviations between the lidar scan points and the local fitted planes of the built map, achieving accurate observations for the navigation states. The proposed algorithm updates navigation states based on the iterative extended Kalman filter. Experiments indicate that the proposed algorithm has higher positioning accuracy than the state-of-the-art method, which gets smaller positioning error (7.868 m) and 52.3% improvement on average. The proposed algorithm predicts and updates navigation states based on the iterative extended Kalman filter (IEKF). The observation equation of the IEKF adopts the normal vector features and the direct alignment for the alignment evaluation. Experiments indicate that the proposed algorithm has more accurate positioning accuracy than the state-of-the-art method. image