Improved Iterative Closest Point (ICP) Point Cloud Registration Algorithm based on Matching Point Pair Quadratic Filtering

Abstract

Aiming at the problems of long computing time and poor registration accuracy in current point cloud registration, an improved ICP algorithm based on matching point pair secondary filtering was proposed, which combined ground segmentation and point cloud filtering algorithm for pre-processing. Firstly, ground segmentation is performed on the point cloud data obtained by Lidar, and ground points are filtered. Next, Kdtree_ICP is used for point cloud registration, and the abnormal matching point pairs obtained by Kdtree search are filtered during the matching process. Finally, the point cloud data of outdoor ground is used for experimental verification. Experimental results show that the proposed method greatly improves the computational speed and accuracy, and the algorithm is stable and reliable.