scholarly journals Indoor Scene Point Cloud Registration Algorithm Based on RGB-D Camera Calibration

Sensors ◽  
2017 ◽  
Vol 17 (8) ◽  
pp. 1874 ◽  
Author(s):  
◽  
Keyword(s):  
Camera Calibration ◽  
Point Cloud ◽  
Point Cloud Registration ◽  
Registration Algorithm ◽  
Indoor Scene

Point Cloud Registration Algorithm Combined Gaussian Mixture Model and Point-to-Plane Metric

2018 ◽  
Vol 30 (4) ◽  
pp. 642
Author(s):  
Guichao Lin ◽  
Yunchao Tang ◽  
Xiangjun Zou ◽  
Qing Zhang ◽  
Xiaojie Shi ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Point Cloud ◽  
Gaussian Mixture ◽  
Point Cloud Registration ◽  
Registration Algorithm

Point Cloud Registration Algorithm Based on Extended Point Feature Histogram Feature

2019 ◽  
Vol 56 (24) ◽  
pp. 241503
Author(s):  
汤慧 Tang Hui ◽  
周明全 Zhou Mingquan ◽  
耿国华 Geng Guohua
Keyword(s):  
Point Cloud ◽  
Point Cloud Registration ◽  
Registration Algorithm ◽  
Histogram Feature ◽  
Extended Point ◽  
Point Feature

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.

Point Cloud Registrational Computing Based on Hadoop

2014 ◽  
Vol 644-650 ◽  
pp. 4624-4629
Author(s):  
Song Liu ◽  
Xiao Yao Xie
Keyword(s):  
Parallel Computing ◽  
Point Cloud ◽  
High Accuracy ◽  
Point Cloud Data ◽  
Point Cloud Registration ◽  
Computing Resource ◽  
Cloud Data ◽  
Registration Algorithm ◽  
Hadoop Cluster

For the problem of huge computation and requiring high computing resource in point cloud registration, according to the theory of parallel computing, the algorithm of point cloud registration base on MapReduce is designed. Through building a Hadoop cluster consisted by average PCs, four examples have been tested. The experiment results show that point cloud registration algorithm based on MapReduce can register point cloud data with high accuracy.

scholarly journals Point Cloud Registration Algorithm Based on Laplace Mixture Model

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Qin Shu ◽  
Yu Fan ◽  
Chang Wang ◽  
Xiuli He ◽  
Chunxiao Yu
Keyword(s):  
Mixture Model ◽  
Point Cloud ◽  
Point Cloud Registration ◽  
Registration Algorithm
Sign in / Sign up

Export Citation Format

Share Document