A Brief Review on Loop Closure Detection with 3D Point Cloud

Abstract

A robust loop closure detection (LCD) in simultaneous localization and mapping (SLAM) can lead to more consistent and accurate map, which is a key part of SLAM. Currently, there are two main kind of data sources used for loop closure detection, the 2D image or 3D points. The 2D image-based LCD method has been extensively studied and many literatures have been published. However, this kind of method is sensitive to the illumination, the change of weather conditions and season et al. With the rapid development of self-driving technique, LiDAR-based SLAM has attracted many researchers because of its advantages. In this paper, we make a summarization on the recent developments of LCD method with 3D data source, especially with 3D point cloud input, and classify these methods into three groups, point feature based, segmentation/object based, and learning-based methods. For each group, the typical references are introduced. For the feature based method, four kind of features for LCD are introduced. Based on these introduction, the readers can get an overview on these LCD methods and even their working principles. Some discussions and the future trend on LCD with 3D point are also presented.