ICP Algorithm: Theory, Practice And Its SLAM-oriented Taxonomy
This work provides a structured overview for researchers and practitioners in computer vision and robotics, but it is incremental as it synthesizes existing knowledge without new algorithmic contributions.
The paper reviews the ICP algorithm for 3D surface registration, detailing its theory, practice, and traditional taxonomy, and introduces a new SLAM-oriented taxonomy based on task characteristics like online operation and landmark presence.
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.