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 three-dimensional point cloud registration based on entropy and particle swarm optimization

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881433 ◽  
Author(s):  
Xu Zhan ◽  
Yong Cai ◽  
Ping He
Keyword(s):  
Particle Swarm Optimization ◽  
Point Cloud ◽  
Fitness Function ◽  
Three Dimensional ◽  
Particle Swarm ◽  
Point Clouds ◽  
Particle Swarm Algorithm ◽  
Registration Accuracy ◽  
Point Cloud Registration ◽  
Swarm Optimization

A three-dimensional (3D) point cloud registration based on entropy and particle swarm algorithm (EPSA) is proposed in the paper. The algorithm can effectively suppress noise and improve registration accuracy. Firstly, in order to find the k-nearest neighbor of point, the relationship of points is established by k-d tree. The noise is suppressed by the mean of neighbor points. Secondly, the gravity center of two point clouds is calculated to find the translation matrix T. Thirdly, the rotation matrix R is gotten through particle swarm optimization (PSO). While performing the PSO, the entropy information is selected as the fitness function. Lastly, the experiment results are presented. They demonstrate that the algorithm is valuable and robust. It can effectively improve the accuracy of rigid registration.

scholarly journals Iterative automatic global registration algorithm for multi-view point cloud of underground tunnel space

2021 ◽  
Vol 54 (3-4) ◽  
pp. 385-395
Author(s):  
Ming Guo ◽  
Bingnan Yan ◽  
Guoli Wang ◽  
Pingjun Nie ◽  
Deng Pan ◽  
...  
Keyword(s):  
Point Cloud ◽  
Iteration Process ◽  
Point Cloud Data ◽  
Subway Tunnel ◽  
Registration Accuracy ◽  
Point Cloud Registration ◽  
Underground Tunnel ◽  
Cloud Data ◽  
Registration Algorithm ◽  
Global Registration

Aiming at the narrow and long tunnel structure, few internal features, and a large amount of point cloud data, the existing registration algorithms and commercial software registration results are not ideal, an iterative global registration algorithm is proposed for massive underground tunnel point cloud registration, which is composed of local initial pose acquisition and global adjustment. Firstly, the feature point coordinates in the point cloud are extracted, and then the station-by-station registration is performed according to the Rodrigues matrix. Finally, the registration result is considered as the initial value of the parameter, and the global adjustment of all observations is carried out. The observation values are weighted by the selection weight iteration method and the weights are constantly modified in the iteration process until the threshold conditions are met and the iteration stops. In this paper, the experimental data, made up of 85 stations of point cloud data, are from the Xiamen subway tunnel, which is about 1300 m long. When the accumulated error of station-to-station registration is too large, several stations are regarded as partial wholes, and the optimal registration is achieved through multiple global adjustments, and the registration accuracy is within 5 mm. Experimental results confirm the feasibility and effectiveness of the algorithm, which provides a new method for point cloud registration of underground space tunnel.

scholarly journals A point cloud registration algorithm based on normal vector and particle swarm optimization

2019 ◽  
Vol 53 (3-4) ◽  
pp. 265-275 ◽  
Author(s):  
Xu Zhan ◽  
Yong Cai ◽  
Heng Li ◽  
Yangmin Li ◽  
Ping He
Keyword(s):  
Particle Swarm Optimization ◽  
Point Cloud ◽  
Particle Swarm ◽  
Target Point ◽  
Normal Vector ◽  
Registration Accuracy ◽  
Point Cloud Registration ◽  
Swarm Optimization ◽  
Registration Algorithm ◽  
The Right

Based on normal vector and particle swarm optimization (NVP), a point cloud registration algorithm is proposed by searching the corresponding points. It provides a new method for point cloud registration using feature point registration. First, in order to find the nearest eight neighbor nodes, the k-d tree is employed to build the relationship between points. Then, the normal vector and the distance between the point and the center gravity of eight neighbor points can be calculated. Second, the particle swarm optimization is used to search the corresponding points. There are two conditions to terminate the search in particle swarm optimization: one is that the normal vector of node in the original point cloud is the most similar to that in the target point cloud, and the other is that the distance between the point and the center gravity of eight neighbor points of node is the most similar to that in the target point cloud. Third, after obtaining the corresponding points, they are tested by random sample consensus in order to obtain the right corresponding points. Fourth, the right corresponding points are registered by the quaternion method. The experiments demonstrate that this algorithm is effective. Even in the case of point cloud data lost, it also has high registration accuracy.

Three-Dimensional Point Cloud Registration Algorithm Based on lp Spatial Mechanics Model

2018 ◽  
Vol 38 (10) ◽  
pp. 1010005
Author(s):  
赵敏 Zhao Min ◽  
舒勤 Shu Qin ◽  
陈蔚 Chen Wei ◽  
杨赟秀 Yang Yunxiu
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Registration ◽  
Registration Algorithm ◽  
Mechanics Model

scholarly journals A Depth-Based Weighted Point Cloud Registration for Indoor Scene

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3608 ◽  
Author(s):  
Shuntao Liu ◽  
Dedong Gao ◽  
Peng Wang ◽  
Xifeng Guo ◽  
Jing Xu ◽  
...  
Keyword(s):  
Measurement Error ◽  
Point Cloud ◽  
Three Dimensional ◽  
Convergence Speed ◽  
Registration Accuracy ◽  
Point Cloud Registration ◽  
Depth Measurement ◽  
Registration Error ◽  
Weighted Point ◽  
Indoor Scenes

Point cloud registration plays a key role in three-dimensional scene reconstruction, and determines the effect of reconstruction. The iterative closest point algorithm is widely used for point cloud registration. To improve the accuracy of point cloud registration and the convergence speed of registration error, point pairs with smaller Euclidean distances are used as the points to be registered, and the depth measurement error model and weight function are analyzed. The measurement error is taken into account in the registration process. The experimental results of different indoor scenes demonstrate that the proposed method effectively improves the registration accuracy and the convergence speed of registration error.

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
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