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New iterative closest point algorithm for isotropic scaling registration of point sets with noise

机译：带有噪声的点集各向同性缩放配准的新的迭代最近点算法

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This paper proposes a new probability iterative closest point (ICP) approach with bounded scale based on expectation maximization (EM) estimation for isotropic scaling registration of point sets with noise. The bounded-scale ICP algorithm can handle the case with different scales, but it could not effectively yield the alignment of point sets with noise. Aiming at improving registration precision, a Gaussian probability model is integrated into the bounded-scale registration problem, which is solved by the proposed method. This new method can be solved by the E-step and M-step. In the E-step, the one-to-one correspondence is built up between two point sets. In the M-step, the scale transformation including the rotation matrix, translation vector and scale factor is computed by singular value decomposition (SVD) method and the properties of parabola. Then, the Gaussian model is updated via the distance and variance between transformed point sets. Experimental results demonstrate the proposed method improves the performance significantly with high precision and fast speed. (C) 2016 Elsevier Inc. All rights reserved.
机译：本文提出了一种基于期望最大化（EM）估计的有界标度的概率迭代最近点（ICP）方法，用于带有噪声的点集的各向同性标度配准。有界尺度ICP算法可以处理不同尺度的情况，但不能有效地使点集与噪声对齐。为了提高配准精度，将高斯概率模型集成到有界标配问题中，该方法可以解决该问题。这种新方法可以通过E步和M步解决。在E步骤中，在两个点集之间建立了一对一的对应关系。在M步中，通过奇异值分解（SVD）方法和抛物线的属性来计算包括旋转矩阵，平移矢量和比例因子的比例变换。然后，通过变换的点集之间的距离和方差更新高斯模型。实验结果表明，该方法以较高的精度和速度显着提高了性能。 （C）2016 Elsevier Inc.保留所有权利。

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