基于多重特征匹配的点云配准算法 后印本

Abstract: To solve the problem that iterative closest point (ICP) algorithm has a single feature for searching and low accuracy for registration, this paper proposed a point cloud registration algorithm that based on multiple-feature matching. It chose the improved adaptive octree algorithm to segment the point cloud. Then calculated the multiple features of the points after performed moving least squares (MLS) algorithm to fit the leaf nodes. Next, this algorithm introduced the point pairs similarity that based on multiple features to establish the matching points. Lastly, computed the rotation matrix and translation matrix to achieve registration. Experiments show that this algorithm can effectively improve the accuracy of registration on the basis of keeping the point cloud registration speed high. And with the number of point sets increasing, the trend of accuracy for this method is increasing.