三维不变矩特征估计的点云拼接  

Point Clouds Registration of 3D Moment Invariant Feature Estimation

作　　者：秦绪佳[1,2] 王建奇[1] 郑红波[1,2] 梁震华 

机构地区：[1]浙江工业大学计算机科学与技术学院,杭州310032 [2]浙江工业大学浙江省可视媒体智能处理技术研究重点实验室,杭州310032 [3]浙江华震三维数字化工程有限公司,嘉兴314100

出　　处：《机械工程学报》2013年第1期129-134,共6页Journal of Mechanical Engineering

基　　金：国家自然科学基金(61075118;60673063);浙江省自然科学基金(Y1100880)资助项目

摘　　要：点云拼接是点云模型获取和重建的关键问题。提出一种新的三维不变矩特征估计的点云拼接方法,该方法将二维不变矩扩展到三维,用于描述点云的局部特征,设计实现了用该特征查找对应点的ICP算法。该算法先计算点云的特征描述子,由其中一个点云的点查找在另一片点云的最近邻域且特征描述子最相似的点作为这点的对应点,并建立对应点点集,其次将点集变换到以质心为原点的坐标下,然后根据对应点对集合建立协方差矩阵并对它奇异值分解,得到旋转矩阵和平移矩阵,最后迭代上述步骤直到收敛。通过人脸数据拼接的结果表明,该方法是可行有效的。Registration of point clouds is a key problem in model acquisition and reconstruction. A novel method is presented to register a pair of clouds by estimating the 3D moment invariant feature. In this method, the 2D moment invariant is expended to 3 dimensions, and it is used to describe the local surface characteristic feature of point clouds. With this feature searching for corresponding points, the method implements the iterative closest point algorithm （ICP）. The algorithm calculates the characteristic description of point clouds, and every point in one cloud finds another point which has similar description in close field of other cloud as a corresponding point, then corresponding points set can be built by corresponding points. The set should be transformed to centroid coordinates for origin, and then covariance matrix can be established based on the corresponding points set and applied by singular value decomposition （SVD）, the rotation matrix and translation matrix would be obtained from the result of SVD. Do the above steps until the iteration convergenee. The registration result of two face clouds shows this method is feasible and effective.

关 键 词：点云 三维不变矩 迭代最近点拼 接对 应点 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]