Please use this identifier to cite or link to this item:
|Title:||Comparative study of point cloud registration approaches||Authors:||Xu, Mingxi||Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2023||Publisher:||Nanyang Technological University||Source:||Xu, M. (2023). Comparative study of point cloud registration approaches. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/172055||Abstract:||Point cloud registration is to obtain a rigid transformation between two different point clouds collected by radar sensors or depth cameras. As a fundamental step in many processes such as reconstruction or segmentation tasks, and Simultaneous Localization And Mapping (SLAM). However, due to the point clouds used in different proposed methods are always collected privately, and the algorithms with different mechanisms will not be compared, there are seldom articles comparing the registration methods systematically. The purpose of this paper is to compare point cloud registration methods with different mechanisms which mainly include local registration, global registration, and learning-based registration. These methods will be tested on a well-defined combined point cloud benchmark and two classic public point cloud datasets and results will contain multi-level metrics. In addition, to simulate a more complete actual use case, I also built a virtual SLAM process on gazebo, and obtained point clouds of the scene by A-loam. The virtual environment will contain an indoor scene and an outdoor scene. Different point cloud registration will be used to match two point clouds obtained by two robots in the same scene and the results will be visualized for a more intuitive presentation.||URI:||https://hdl.handle.net/10356/172055||Schools:||School of Electrical and Electronic Engineering||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Theses|
Files in This Item:
|Comparative Study of Point Cloud Registration Approaches.pdf|
|7.01 MB||Adobe PDF||View/Open|
Updated on Dec 6, 2023
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.