Silva Luciano, Bellon Olga R P, Boyer Kim L
Departamento de Informática, Universidade Federal do Paraná, Caixa Postal 19092, Curitiba, PR, Brazil 81531-980.
IEEE Trans Pattern Anal Mach Intell. 2005 May;27(5):762-76. doi: 10.1109/TPAMI.2005.108.
This paper addresses the range image registration problem for views having low overlap and which may include substantial noise. The current state of the art in range image registration is best represented by the well-known iterative closest point (ICP) algorithm and numerous variations on it. Although this method is effective in many domains, it nevertheless suffers from two key limitations: It requires prealignment of the range surfaces to a reasonable starting point and it is not robust to outliers arising either from noise or low surface overlap. This paper proposes a new approach that avoids these problems. To that end, there are two key, novel contributions in this work: a new, hybrid genetic algorithm (GA) technique, including hillclimbing and parallel-migration, combined with a new, robust evaluation metric based on surface interpenetration. Up to now, interpenetration has been evaluated only qualitatively; we define the first quantitative measure for it. Because they search in a space of transformations, GAs are capable of registering surfaces even when there is low overlap between them and without need for prealignment. The novel GA search algorithm we present offers much faster convergence than prior GA methods, while the new robust evaluation metric ensures more precise alignments, even in the presence of significant noise, than mean squared error or other well-known robust cost functions. The paper presents thorough experimental results to show the improvements realized by these two contributions.
本文探讨了低重叠且可能包含大量噪声的视图的距离图像配准问题。距离图像配准的当前技术水平最好由著名的迭代最近点（ICP）算法及其众多变体来代表。尽管该方法在许多领域都很有效，但它仍然存在两个关键限制：它需要将距离曲面预对齐到一个合理的起始点，并且对由噪声或低曲面重叠引起的异常值不具有鲁棒性。本文提出了一种新方法来避免这些问题。为此，这项工作有两个关键的新颖贡献：一种新的混合遗传算法（GA）技术，包括爬山法和平行迁移法，以及一种基于曲面互穿的新的鲁棒评估指标。到目前为止，互穿仅进行了定性评估；我们定义了第一个对其进行定量测量的方法。由于遗传算法在变换空间中进行搜索，即使曲面之间重叠度很低且无需预对齐，它们也能够对曲面进行配准。我们提出的新颖遗传算法搜索算法比先前的遗传算法方法收敛速度快得多，而新的鲁棒评估指标即使在存在大量噪声的情况下也能确保比对均方误差或其他著名的鲁棒代价函数更精确的对齐。本文给出了全面的实验结果，以展示这两个贡献所实现的改进。
