Abstract: In the optical non-contact measurement process, the reconstruction of complex object depends on the registering of many point clouds. Iterative Closest Point（ICP） algorithm is a mathematical method with high level in processing data of 3D laser scanning about registration. For the sake of obtaining better registering result, a algorithm including initial registration and precise registration is proposed. It combines algorithm based on feature points and ICP to register point cloud data automatically. Experimental results are presented, and show the accurate and robust performance of proposed algorithm.

Key words: point cloud, registration, dental cast, feature points, Iterative Closest Point（ICP）

摘要： 在牙齿三维矫正中需要对牙齿进行排列，常用方法是通过人机交互完成，效率不高。提出了一种基于粒子群的自动化排牙方法，将每颗牙齿上的特征点到标准牙弓曲线的距离和作为目标函数，利用粒子群算法对解空间进行搜索，在搜索过程中加入约束条件，得到牙齿移动的最终位置。利用算法对牙齿进行排列，可以省去人机交互中的平移等操作。实验结果表明：该算法能够有效地用于牙齿三维矫正中，提高了排牙效率。

关键词: 点云, 配准, 牙模型, 特征点, ICP算法