UC Riverside

This dissertation presents a novel motion distortion correction method for point clouds acquired from LiDAR sensors.The proposed method estimates a motion profile from multiple point cloud segments by fitting a second-order polynomial motion model using axis-angle representation and Swing-Twist Decomposition. The proposed algorithm can be integrated into a LiDAR odometry and mapping system, and can enhance the overall performance in the absence or failure of external inertial measurement units. Experimental results demonstrated the effectiveness of the proposed method on a mobile robot operating in structured and unstructured environments.