基于扩展高斯球的点云数据与CAD模型配准

REGISTRATION OF POINTCLOUD AND CAD MODEL BASED ON EXTENDED GAUSSIAN SPHERE

针对点云数据和三维CAD模型坐标配准过程中初始对应位置无法定量确定及最临近点计算效率问题,提出基于扩展高斯球的快速模板匹配算法,通过点云数据扩展高斯球空间与笛卡尔空间的相互转换,使外特征配准算法中测量点云与三维CAD模型初始位置的确定得到定量解决。为提高配准算法中点云数据与三维CAD模型最临近点的计算效率,采用三维CAD模型引导点云对测量数据与三维CAD模型进行桥接,从而将测量点与自由曲面的数值迭代计算问题转化为三维空间中最临近点的空间搜索,使算法的效率得到提升,实例验证了算法的有效性。

There are some problems in the registration of point cloud and CAD model, such as definition initial corresponding position of point cloud and CAD model, registration method＇s efficiency. In order to solve them, a new fast template matching method based on extend Gaussian sphere is proposed. Transforming the extended Gaussian space and Cartesian space solves the initial corresponding position. In order to promote the efficiency of registration, the CAD direct object point cloud is used in the registration of point cloud and CAD model. When two point clouds had been matched, the point cloud and CAD model had also been in the same coordinate system. Because using the searching method to get the closest point in point cloud replaces the optimization method between the point cloud and NURBS, the efficiency of the method has promoted. Some cases are done to prove the method＇s correction.