Robust Registration of 2D and 3D Point Sets
Robust Registration of 2D and 3D Point Sets
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DOI：
10.5244/c.15.43
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发表时间：
2003-12
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通讯作者：
A. Fitzgibbon
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文献类型：
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作者：
A. Fitzgibbon
本文介绍了一种新的点集配准方法。使用通用非线性优化（Levenberg-Marquardt算法）直接最小化配准误差。该论文令人惊讶的结论是，这种技术在速度上与专用的迭代最近点算法相当，后者最常用于此任务。由于该例程直接最小化能量函数，因此很容易将其扩展为通过Huber内核进行鲁棒估计，从而产生比现有技术宽许多倍的收敛盆地。最后，我们引入了一个数据结构的最小化的基础上的倒角距离变换，这产生了一个算法，这是更快，更强大的比以前描述的方法。
Abstract This paper introduces a new method of registering point sets. The registration error is directly minimized using general-purpose non-linear optimization (the Levenberg–Marquardt algorithm). The surprising conclusion of the paper is that this technique is comparable in speed to the special-purpose Iterated Closest Point algorithm, which is most commonly used for this task. Because the routine directly minimizes an energy function, it is easy to extend it to incorporate robust estimation via a Huber kernel, yielding a basin of convergence that is many times wider than existing techniques. Finally, we introduce a data structure for the minimization based on the chamfer distance transform, which yields an algorithm that is both faster and more robust than previously described methods.