Planar Segment Based Three-dimensional Point Cloud Registration in Outdoor Environments

2013 ◽  
Vol 30 (4) ◽  
pp. 552-582 ◽  
Author(s):  
Junhao Xiao ◽  
Benjamin Adler ◽  
Jianwei Zhang ◽  
Houxiang Zhang
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Registration ◽  
Outdoor Environments

Three-Dimensional Point Cloud Registration Based on Independent Component Analysis

2019 ◽  
Vol 56 (1) ◽  
pp. 011203
Author(s):  
刘鸣 Liu Ming ◽  
舒勤 Shu Qin ◽  
杨赟秀 Yang Yunxiu ◽  
袁菲 Yuan Fei
Keyword(s):  
Independent Component Analysis ◽  
Point Cloud ◽  
Three Dimensional ◽  
Component Analysis ◽  
Independent Component ◽  
Point Cloud Registration

Three-Dimensional Point Cloud Registration Based on Maximum Sum of Squares of Correlation Coefficients

2019 ◽  
Vol 56 (22) ◽  
pp. 221504
Author(s):  
苗长伟 Miao Changwei ◽  
唐志荣 Tang Zhirong ◽  
唐英杰 Tang Yingjie
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Correlation Coefficients ◽  
Sum Of Squares ◽  
Point Cloud Registration

Improved Random Sampling Consistency Algorithm Employed in Three-Dimensional Point Cloud Registration

2018 ◽  
Vol 55 (10) ◽  
pp. 101104
Author(s):  
刘美菊 Liu Meiju ◽  
王旭东 Wang Xudong ◽  
李凌燕 Li Lingyan ◽  
高恩阳 Gao Enyang
Keyword(s):  
Random Sampling ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Registration

scholarly journals 3-D Point Cloud Registration Algorithm Based on Greedy Projection Triangulation

2018 ◽  
Vol 8 (10) ◽  
pp. 1776 ◽  
Author(s):  
Jian Liu ◽  
Di Bai ◽  
Li Chen
Keyword(s):  
Machine Vision ◽  
Point Cloud ◽  
Three Dimensional ◽  
Target Object ◽  
Initial Alignment ◽  
Registration Accuracy ◽  
Point Cloud Registration ◽  
Registration Algorithm ◽  
Feature Information ◽  
Complex Target

To address the registration problem in current machine vision, a new three-dimensional (3-D) point cloud registration algorithm that combines fast point feature histograms (FPFH) and greedy projection triangulation is proposed. First, the feature information is comprehensively described using FPFH feature description and the local correlation of the feature information is established using greedy projection triangulation. Thereafter, the sample consensus initial alignment method is applied for initial transformation to implement initial registration. By adjusting the initial attitude between the two cloud points, the improved initial registration values can be obtained. Finally, the iterative closest point method is used to obtain a precise conversion relationship; thus, accurate registration is completed. Specific registration experiments on simple target objects and complex target objects have been performed. The registration speed increased by 1.1% and the registration accuracy increased by 27.3% to 50% in the experiment on target object. The experimental results show that the accuracy and speed of registration have been improved and the efficient registration of the target object has successfully been performed using the greedy projection triangulation, which significantly improves the efficiency of matching feature points in machine vision.

Research on Two-Stage Variable Scale Three-Dimensional Point Cloud Registration Algorithm

2020 ◽  
Vol 57 (20) ◽  
pp. 201503
Author(s):  
卢升 Lu Sheng ◽  
韩俊刚 Han Jungang ◽  
王连哲 Wang Lianzhe ◽  
唐海鹏 Tang Haipeng ◽  
齐全 Qi Quan ◽  
...  
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Two Stage ◽  
Point Cloud Registration ◽  
Registration Algorithm

Three-Dimensional Point Cloud Registration Algorithm Based on Factor Analysis

2019 ◽  
Vol 56 (19) ◽  
pp. 191503
Author(s):  
唐志荣 Tang Zhirong ◽  
蒋悦 Jiang Yue ◽  
苗长伟 Miao Changwei ◽  
赵成强 Zhao Chengqiang
Keyword(s):  
Factor Analysis ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Registration ◽  
Registration Algorithm

scholarly journals A Tutorial Review on Point Cloud Registrations: Principle, Classification, Comparison, and Technology Challenges

2021 ◽  
Vol 2021 ◽  
pp. 1-32
Author(s):  
Leihui Li ◽  
Riwei Wang ◽  
Xuping Zhang
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Spatial Transformation ◽  
Point Cloud Registration ◽  
Basic Principles ◽  
Technical Problems ◽  
Survey Paper ◽  
Academic Researchers ◽  
Industrial Inspection

A point cloud as a collection of points is poised to bring about a revolution in acquiring and generating three-dimensional (3D) surface information of an object in 3D reconstruction, industrial inspection, and robotic manipulation. In this revolution, the most challenging but imperative process is point could registration, i.e., obtaining a spatial transformation that aligns and matches two point clouds acquired in two different coordinates. In this survey paper, we present the overview and basic principles, give systematical classification and comparison of various methods, and address existing technical problems in point cloud registration. This review attempts to serve as a tutorial to academic researchers and engineers outside this field and to promote discussion of a unified vision of point cloud registration. The goal is to help readers quickly get into the problems of their interests related to point could registration and to provide them with insights and guidance in finding out appropriate strategies and solutions.

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