【摘要】 针对无任何预知信息下的扫描点云数据配准问题,通过引入新的匹配点对度量准则和改进最近点迭代算法,提出一种扫描点云数据的自动配准方法.该方法分为初始配准和精细配准2个阶段.初始配准阶段中,在考虑孤立点的曲率相似度的基础上,通过引入一种新的点的邻域曲率相似度度量准则,构造出一个有效的一一对应的初始匹配点对数组;然后根据刚体变换的特点和不变量引入距离约束和超线段约束,对初始匹配点对进行过滤;最后利用最终得到的匹配点对的几何信息计算初始配准参数.在精细配准阶段,构造了参与最近点迭代算法的有效初始点集,并改进了最近点的计算过程.数值实例结果表明,文中方法初始配准效果良好,二次配准效果更加准确,达到了不同视角扫描点云数据配准的要求.更多还原

【Abstract】 To register scanned point clouds without any additional information,an automatic registration method is developed by introducing new metrics for matching corresponding points and modifying the iterative closest point algorithm.The whole registration consists of two steps: initial registration and fine registration.In the first step,based on the curvature similarity between two points,a new metric for the curvature similarity between the neighborhoods of them is developed to construct an initial array of corresponding points.Then two restrictions based on invariant features of the rigid transformation are introduced to pick out some corresponding points of high precision from the initial array.Initial registration parameters are computed directly according to the geometric information of these corresponding points.In the second step,the computing of the closest point is modified.Numerical experiments demonstrate the good results of the initial registration and the better results of the fine registration,which have met the requirement of registering point clouds from different views.更多还原