AN Extension of the ICP Algorithm Considering Scale Factor
AN Extension of the ICP Algorithm Considering Scale Factor
复制标题
DOI：
10.1109/icip.2007.4379798
复制
发表时间：
2007-11
期刊：
影响因子：
--
通讯作者：
S. Du;Nanning Zheng;Shihui Ying;Qubo You;Yang Wu
中科院分区：
文献类型：
--
作者：
S. Du;Nanning Zheng;Shihui Ying;Qubo You;Yang Wu
ICP算法对于同一尺度下的两点集的配准具有较高的精度和速度，但对于不同尺度下的配准则不适用。本文介绍了一种新的方法，称为缩放迭代最近点（SICP）算法，它集成了一个规模矩阵的边界到原来的ICP算法缩放配准。该方法采用简单的迭代算法，结合奇异值分解（SVD）算法和抛物线的性质，在每一步迭代中计算平移、旋转和尺度变换，迭代次数少，收敛速度快。SICP算法不依赖于形状表示和特征提取，因此对尺度配准具有通用性。实验结果表明，与标准ICP算法相比，该算法具有较好的鲁棒性和快速性.
The ICP algorithm is accurate and fast for registration between two point sets in a same scale, but it doesn't handle the case with different scales. This paper instead introduces a novel approach named the scaling iterative closest point (SICP) algorithm which integrates a scale matrix with boundaries into the original ICP algorithm for scaling registration. This method uses a simple iterative algorithm with the SVD algorithm and the properties of parabola incorporated to compute the translation, rotation and scale transformations at each iterative step, and its convergence is rapid with only a few iterations. The SICP algorithm is independent of shape representation and feature extraction; thereby it is general for scaling registration. Experimental results demonstrate its robustness and fast speed compared with the standard ICP algorithm.