三维模型重建中点云ICP拼接算法的改进

提出一种以不同视野下的点云为处理对象,自动进行点云配准的拼接算法.该算法首先利用点云的曲率、法矢量等几何信息,计算出初次拼接变换,并用几何哈希的方法筛选出最优的变换,完成初次拼接.然后改进最近点迭代法(iterative closest point,ICP)中最近点的选取方法:对点云A中的测点,先求出另一点云B中与其最近的3个点,并由这3个点构成一个三角形,把测点到三角形的垂足作为测点的最近点.并用该算法对点云进行再次拼接.最后基于该算法实现了对车头模型的配准.结果表明,该算法具有较高的配准精度.

Improved ICP registration in 3-D model reconstruction

One algorithm to automatically register point cloud data in 3-D model reconstruction was proposed. In the algorithm, the initial registration transformations were firstly calculated according to the curvature and normal vector of the point cloud. Then the best initial registration transformation was selected by geometric hashing method and the initial registration was completed by this transformation. Next,the ICP (iterative closest point) algorithm was improved by redefining the nearest point: For one measured point of A,the nearest three points in B point cloud were firstly found; the triangle was formed by this three points and the foot point of this triangle was taken as nearest point of measured point. Then the improved ICP algorithm was used for accurate secondary registration. Finally,this algorithm was used to register the point data in previous model. And the results show this algorithm has the better performance in registration accuracy.