计算机工程

• 图形图像处理 • 上一篇    下一篇

归一化互相关系数与迭代最近曲面片点云配准方法

张梅,文静华   

  1. (贵州财经大学 信息学院,贵阳 550004)
  • 出版日期:2016-10-15 发布日期:2016-10-12

Point Cloud Registration Method of Normalized Cross-correlation Coefficient and Iterative Closest Surface

ZHANG Mei,WEN Jinghua   

  1. (School of Information,Guizhou Financial University,Guiyang 550004,China)
  • Online:2016-10-15 Published:2016-10-12

摘要: 针对无附加信息的激光点云数据,基于匹配点对衡量准则与迭代最近曲面片(ICS)算法提出一种新的配准方法。引入归一化零均值互相关系数衡量点的邻域曲率相似度,构造一一对应的初始匹配点对有效数组,利用四元素和线性最小二乘法计算初始配准参数。通过局部曲面片代替离散点,建立参与ICS算法的有效点集,并用一次近似距离代替点到对应曲面片的几何距离,建立配准的非线性最小二乘优化模型和求解策略。实例结果表明,与迭代拼接算法相比,该方法具有多视角普适性,且高效精确。

关键词: 激光点云, 配准, 归一化零均值互相关系数, 邻域曲率, 迭代最近曲面片

Abstract: Aiming at the registration of laser point cloud data with no additional information,this paper proposes a new registration method based on the measure criterion for matching point and Iterative Closest Surface(ICS) algorithm.The method introduces a new Normalized Zero-mean Cross-correlation Coefficient(NZCC) to measure curvature similarity of the neighborhood of a point.The effective array of one-to-one initial matching points is built.The initial registration parameters can be computed by using the four elements and the linear least square method.The method uses local surface instead of discrete points,the efficient point sets which involve in ICS are built,and uses one-time similar distance instead of the geometric distance from point to its corresponding surface patches,the nonlinear least square optimization model and solution strategy of registration is established.Numerical example results show that compared with the iterative stitching algorithm,this method is feasible,accurate and efficient.

Key words: laser point cloud, registration, Normalized Zero-mean Cross-correlation Coefficient(NZCC);neighborhood curvature;Iterative Closest Surface(ICS)

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