基于曲率的点云数据配准算法  

Curvature-based registration algorithm of point clouds data

作　　者：路银北[1] 张蕾[1] 普杰信[1] 杜鹏[1] 

机构地区：[1]河南科技大学电子信息工程学院,河南洛阳471003

出　　处：《计算机应用》2007年第11期2766-2769,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60475021);河南省杰出青年基金资助项目(0412000400)

摘　　要：为了实现不同视角下测得的数据的多视定位,提出一种点云数据配准算法。该算法针对近邻内的点,采用二次曲面逼近的方法来求得每个点的曲率,并根据曲率的Hausdorff距离来寻找有效点集,建立名义上的对应关系,最后用四元组法来求得坐标变换,把数据统一到一个坐标系下。该算法利用曲率的性质准确判断对应点集,解决了任意多视点云的拼合问题,试验结果验证了其有效性和精度。A 3-D measuring data registration algorithm was proposed in order to locate the different-view-measured cloud data without clear corresponding relation. In the algorithm, a local parabolic surface was fitted to each neighbor point. The curvature of each point was estimated according to the point and its neighbor points, using Hausdorff distance as measurement criterion. Thus a nominal correspondence was created between the control point set selected from the different positions in the overlapped view area. Then the rotation matrix and translation vector were deduced by quaternion method. The corresponding point sets were judged according to the characteristics of curvature, and the problem of combining arbitrary multi-view point clouds was solved. Experimental results show the accurate and robust performance of the proposed algorithm.

关 键 词：点云 曲率 配准 法矢 HAUSDORFF距离 四元组法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]