论文标题
ICP算法：理论，实践及其面向巨大的分类学
ICP Algorithm: Theory, Practice And Its SLAM-oriented Taxonomy
论文作者
论文摘要
迭代最接近的点（ICP）算法是三维表面配准的几何比喻对齐的最重要算法之一，该算法经常用于计算机视觉任务，包括同时定位和映射（SLAM）任务。在本文中，我们说明了ICP算法的理论原理，如何在表面注册任务中使用以及ICP算法变体的传统分类法。随着SLAM成为一个流行的话题，我们还基于每种大满贯任务的特征，包括SLAM任务是否在线，以及地标在SLAM任务中是否存在地标。我们通过比较几个最新的研究论文并分析其实施细节来综合每种SLAM任务。
The Iterative Closest Point (ICP) algorithm is one of the most important algorithms for geometric alignment of three-dimensional surface registration, which is frequently used in computer vision tasks, including the Simultaneous Localization And Mapping (SLAM) tasks. In this paper, we illustrate the theoretical principles of the ICP algorithm, how it can be used in surface registration tasks, and the traditional taxonomy of the variants of the ICP algorithm. As SLAM is becoming a popular topic, we also introduce a SLAM-oriented taxonomy of the ICP algorithm, based on the characteristics of each type of SLAM task, including whether the SLAM task is online or not and whether the landmarks are present as features in the SLAM task. We make a synthesis of each type of SLAM task by comparing several up-to-date research papers and analyzing their implementation details.
