ICP Algorithm: Theory, Practice And Its SLAM-oriented Taxonomy

TL;DR

This paper reviews the ICP algorithm's theoretical foundations, traditional variants, and introduces a SLAM-oriented taxonomy, analyzing its application in 3D surface registration and SLAM tasks.

Contribution

It provides a comprehensive overview of ICP theory, traditional variants, and proposes a new taxonomy tailored for SLAM applications, synthesizing recent research.

Findings

Detailed analysis of ICP variants

Introduction of SLAM-oriented ICP taxonomy

Comparison of recent SLAM research implementations

Abstract

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.