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A Point Cloud Distortion Removing and Mapping Algorithm based on Lidar and IMU UKF Fusion

When the lidar motion is rapid, point cloud distortion is an important factor affecting the accuracy of lidar odometry. In this paper, an effective lidar and IMU fusion based point cloud distortion removing and mapping algorithm is proposed. UKF is used to fuse the angular velocity, linear acceleration of the IMU and the pose of the lidar. It can get the angular velocity bias, linear acceleration bias of the IMU and the linear velocity of the lidar. Then the pose of the lidar at each time in a scanning period is obtained, therefore the distortion of the point cloud can be removed. UKF can also estimate the pose of the lidar, which can be used as the initial value of the point cloud registration algorithm in lidar odometry, therefore the computation time can be reduced. The rapid rotation and rapid translation motion experiments show that the proposed method can deal with the rapid motion of lidar, and achieve high efficiency and precision mapping.