三维点云关键点配准与识别过程中存在寻找匹配对不理想、大量误匹配对及配准与识别准确率下降等问题,提出了一种新颖的关键点误匹配剔除方法。在关键点检测阶段,基于边缘点及其邻域点大多分布在同侧的特性,提出了一种边缘点检测算法,剔除处于边缘的关键点,以提高关键点的可重复性和可匹配性,并降低关键点特征匹配的误匹配率。在关键点特征匹配阶段,对经由最近邻算法得到的初始关键点匹配对,通过Kmeans算法和分裂法,剔除掉大量错误的关键点匹配对,从而提高三维点云之间关键点的匹配率。实验结果表明,该方法能够剔除完整三维点云匹配完整三维点云、完整三维点云匹配杂乱且有遮挡的三维点云、部分点云匹配部分点云所产生的大量关键点误匹配对,提升了关键点匹配效果;同时在时间上,本文算法较随机取样一致性算法更有效率,是最邻近算法的有益补充。

There are many problems exist in the process of registration and recognition of keypoints in 3D point cloud, such as finding matching mismatch, large number of mismatched pairs, and the decreasing of registration and recognition precision. A novel removal method of mismatching keypoints is proposed. In the stage of keypoint detection, an edge point detection algorithm is put forward based on the feature that the edge points and their neighbor points are mostly distributed on the same side. The proposed method can remove the keypoints existing in the edge to improve the repeatability and match ability, and reduce the mismatching rate in the feature matching of keypoint. In the stage of feature matching of keypoints, the initial keypoint matching pairs obtained by nearest-neighbor algorithm are matched, a lot of mismatching keypoint pairs can be removed according to the methods of Kmeans and splitting, and the matching rate of keypoints between 3D point clouds can be improved. Experimental results show that a large number of the mismatching keypoint pairs can be removed,which generated by a complete point cloud matching a complete point cloud, a complete point cloud matching a point cloud with clutter and occlusion, and a partial point cloud matching a partial point cloud, lots of mismatching keypoint pairs, and the matching effect of keypoints can be significantly improved. At the same time, the proposed algorithm is more efficient than random sample consensus algorithm in the time consumption, which is a good supplement to the nearest-neighbor algorithm.