点云配准是三维建模的关键步骤，而配准速率又是其中的一个瓶颈。实际中点云数据规模大并且对配准速率有一定要求。针对配准点云规模增大导致的配准速率退化以及点云距离过大导致配准失败的情况，结合曲率特征与一致性漂移思想提出一种快速配准点云的方法，首先计算点云曲率特征，然后对比点云间的曲率相似度，提取具有相似结构的特征点作为配准点云。实验表明,该方法不仅将配准的时间消耗缩减1/2左右，并且能够配准距离200个单位坐标差的点云。

Point registration is a critical step of three-dimensional modeling, but the registration rate has been a major bottleneck restricting development of point registration. In the real life, the point registration data are large in scale and have certain requirement of the registration rate. Concerning decrease of the registration rate resulted from a large point registration scale and potential registration failure caused by a too large cloud distance, this paper combines features of curvature and the concept of coherent point drift to propose a quick point registration method. To begin with, the point cloud curvature is calculated. Then, the curvature similarity between point clouds is compared. The registered point clouds with feature points similar in the structure are extracted. This experiment suggests that this method can not only reduce the time consumption of registration by around two folds, but also register point clouds within the distance of 200 units of coordinate difference.