散乱点云数据配准算法  

Registration of Scattered Cloud Data

作　　者：朱延娟[1] 周来水[2] 张丽艳[2] 

机构地区：[1]同济大学航空航天与力学学院,上海200092 [2]南京航空航天大学CAD/CAM工程研究中心,南京210016

出　　处：《计算机辅助设计与图形学学报》2006年第4期475-481,共7页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(60273097);高等学校优秀青年教师教学科研奖励计划;南京航空航天大学创新科研基金(S0272-054);同济大学理科科技发展基金

摘　　要：提出一种以物体表面上不附加任何几何和拓扑信息的散乱点集为处理对象,自动进行点云数据配准的算法·该算法针对待配准的2组点云数据,根据测点及其邻域点估算每个点的曲面法矢,并对法矢方向进行调整,使其指向曲面的同一侧;然后计算各个测点的曲率·根据每个测点的曲率来识别出2组点云数据中可以匹配的点对集合,计算将每一个点对的法矢方向映射为一致的三维空间变换,采用几何哈希方法找出使得最多数量的点对法矢一致的变换,运用该变换将散乱点云作初次配准·以初次配准后的结果作为新的初始位置,将匹配点对集合中的所有点对采用最近点迭代法进行二次配准,从而实现了2组散乱数据的精确配准·应用实例表明,该算法效果良好·An algorithm to automatically register point cloud data from different scans is proposed. The source data may include no additional information no other than coordinates of the measured points. In the algorithm, normal vector and curvature are first calculated according to the point and its neighbor points, and then normal vector of every point is oriented to the outside of the surface. All the pairwise points are located whose curvatures are sufficiently similar and all the rigid transformations that map the first point to the second one are computed, while making the normal vectors coincide. A hash table is constituted from the coordinate transformations in 3D space. The target transformation that makes the most amount normal vectors coincide in this table is employed to register the two point cloud data. Thus, initialized by the former result, the iterative closest point algorithm leads to perfect registration. Experimental results show the accurate and robust performance of the proposed algorithm.

关 键 词：散乱点云数据 曲率 法矢 几何哈希 配准 最小二乘 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]