Motion Distortion Elimination for LiDAR-Inertial Odometry Under Rapid Motion Conditions

Under rapid motion conditions, such as high-speed and vigorous angular velocity, motion distortion in light detection and ranging (LiDAR) point cloud will be severe and reduce the accuracy of LiDAR-inertial odometry (LIO). To address this issue, a novel algorithm based on multioutput Gaussian process regression (MOGPR) is proposed in this work to eliminate motion distortion. First, the single sample plus previous sample (SSPS) algorithm is used to obtain an accurate vehicle state at the same rate as the raw inertial measurement unit (IMU) data. Next, MOGPR is applied to obtain interpolated states that correspond to the moment of each point in the LiDAR point cloud. The motion distortion can be eliminated by aligning all points in the same LiDAR point cloud to a common reference coordinate frame, using the interpolated states as a guide. Finally, the corrected LiDAR point cloud and IMU data are fused using a factor graph, and the IMU bias is estimated to compensate the raw IMU data, further improving the accuracy of motion distortion elimination. Experiments are conducted on simulation data, the public KITTI dataset, and our platform, demonstrating the effectiveness of the proposed method in motion distortion elimination compared to existing methods. In addition, the localization accuracy of the LIO algorithm integrating the proposed method is improved by approximately 30%.