A METHOD FOR REGISTRATION OF 3-D SHAPES
A METHOD FOR REGISTRATION OF 3-D SHAPES
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DOI：
10.1109/34.121791
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发表时间：
1992-02-01
影响因子：
23.6
通讯作者：
MCKAY, ND
中科院分区：
文献类型：
--
作者：
BESL, PJ;MCKAY, ND
本文描述了一种通用的、与表示形式无关的方法，用于对包括自由曲线和曲面在内的三维形状进行精确且计算高效的配准。该方法处理全部六个自由度，基于迭代最近点（ICP）算法，该算法仅需要一个用于找到几何实体上与给定点最近的点的过程。ICP算法总是单调收敛到均方距离度量的最近局部最小值，并且经验表明在最初的几次迭代中收敛速度很快。因此，对于具有一定“形状复杂度”的特定类别的对象，给定一组适当的初始旋转和平移，可以通过测试每个初始配准在所有六个自由度上全局最小化均方距离度量。例如，对于给定的“模型”形状和代表模型形状主要部分的感知“数据”形状，可以通过测试一个初始平移和一组相对较小的旋转（以适应给定的模型复杂度水平）在几分钟内完成配准。该方法的一个重要应用是在形状检测之前将来自未固定刚性物体的感知数据与理想几何模型进行配准。所描述的方法对于确定诸如不同几何表示的全等（形状等价）等基本问题以及在对应关系未知的情况下估计点集之间的运动也是有用的。实验结果展示了配准算法在点集、曲线和曲面上的能力。
This paper describes a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and experience shows that the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of "shape complexity," one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. For example, a given "model" shape and a sensed "data" shape that represents a major portion of the model shape can be registered in minutes by testing one initial translation and a relatively small set of rotations to allow for the given level of model complexity. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model prior to shape inspection. The described method is also useful for deciding fundamental issues such as the congruence (shape equivalence) of different geometric representations as well as for estimating the motion between point sets where the correspondences are not known. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces.