scholarly journals Pairwise Registration Algorithm for Large-Scale Planar Point Cloud Used in Flatness Measurement

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4860
Author(s):  
Zichao Shu ◽  
Songxiao Cao ◽  
Qing Jiang ◽  
Zhipeng Xu ◽  
Jianbin Tang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Three Dimensional ◽  
Planar Point ◽  
Registration Algorithm ◽  
Geometric Consistency ◽  
Coarse Registration ◽  
Pairwise Registration ◽  
Flatness Measurement

In this paper, an optimized three-dimensional (3D) pairwise point cloud registration algorithm is proposed, which is used for flatness measurement based on a laser profilometer. The objective is to achieve a fast and accurate six-degrees-of-freedom (6-DoF) pose estimation of a large-scale planar point cloud to ensure that the flatness measurement is precise. To that end, the proposed algorithm extracts the boundary of the point cloud to obtain more effective feature descriptors of the keypoints. Then, it eliminates the invalid keypoints by neighborhood evaluation to obtain the initial matching point pairs. Thereafter, clustering combined with the geometric consistency constraints of correspondences is conducted to realize coarse registration. Finally, the iterative closest point (ICP) algorithm is used to complete fine registration based on the boundary point cloud. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of boundary extraction and registration performance.

scholarly journals GEOREFERENCING AND REPROJECTION ERROR INVESTIGATION ON IMAGE BASED 3D DIGITIZATION AND MAPPING OF HISTORICAL BUILDINGS

2019 ◽  
Vol XLII-2/W11 ◽  
pp. 71-75
Author(s):  
C. Altuntas
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Cloud Model ◽  
Low Cost ◽  
Three Dimensional ◽  
Control Points ◽  
Historical Buildings ◽  
Reprojection Error ◽  
Dense Point ◽  
Point Cloud Model

<p><strong>Abstract.</strong> Image based dense point cloud creation is easy and low-cost application for three dimensional digitization of small and large scale objects and surfaces. It is especially attractive method for cultural heritage documentation. Reprojection error on conjugate keypoints indicates accuracy of the model and keypoint localisation in this method. In addition, sequential registration of the images from large scale historical buildings creates big cumulative registration error. Thus, accuracy of the model should be increased with the control points or loop close imaging. The registration of point point cloud model into the georeference system is performed using control points. In this study historical Sultan Selim Mosque that was built in sixteen century by Great Architect Sinan was modelled via photogrammetric dense point cloud. The reprojection error and number of keypoints were evaluated for different base/length ratio. In addition, georeferencing accuracy was evaluated with many configuration of control points with loop and without loop closure imaging.</p>

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 Vision-Based Three-Dimensional Reconstruction and Monitoring of Large-Scale Steel Tubular Structures

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yunchao Tang ◽  
Mingyou Chen ◽  
Yunfan Lin ◽  
Xueyu Huang ◽  
Kuangyu Huang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Learning Algorithm ◽  
Vision System ◽  
Three Dimensional ◽  
Steel Tube ◽  
Dynamic Monitoring ◽  
Complex Environments ◽  
Cross Sectional ◽  
Vision Measurement

A four-ocular vision system is proposed for the three-dimensional (3D) reconstruction of large-scale concrete-filled steel tube (CFST) under complex testing conditions. These measurements are vitally important for evaluating the seismic performance and 3D deformation of large-scale specimens. A four-ocular vision system is constructed to sample the large-scale CFST; then point cloud acquisition, point cloud filtering, and point cloud stitching algorithms are applied to obtain a 3D point cloud of the specimen surface. A point cloud correction algorithm based on geometric features and a deep learning algorithm are utilized, respectively, to correct the coordinates of the stitched point cloud. This enhances the vision measurement accuracy in complex environments and therefore yields a higher-accuracy 3D model for the purposes of real-time complex surface monitoring. The performance indicators of the two algorithms are evaluated on actual tasks. The cross-sectional diameters at specific heights in the reconstructed models are calculated and compared against laser rangefinder data to test the performance of the proposed algorithms. A visual tracking test on a CFST under cyclic loading shows that the reconstructed output well reflects the complex 3D surface after correction and meets the requirements for dynamic monitoring. The proposed methodology is applicable to complex environments featuring dynamic movement, mechanical vibration, and continuously changing features.

scholarly journals Fast and coarse registration of point cloud in ICP three-dimensional space

2020 ◽  
Vol 1453 ◽  
pp. 012023 ◽  
Author(s):  
Xiaokang Ren ◽  
Mai Zhang ◽  
Wenqiao Wang ◽  
Xuetao Mao ◽  
Jie Ren
Keyword(s):  
Point Cloud ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Coarse Registration ◽  
Three Dimensional Space

Registration Algorithm for Three-Dimensional Point Cloud Data

2015 ◽  
Vol 741 ◽  
pp. 237-240
Author(s):  
Li Lun Huang ◽  
Wen Guo Li ◽  
Qi Le Yang ◽  
Ying Chun Chen
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Singular Value ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Icp Algorithm ◽  
Cloud Data ◽  
Registration Algorithm ◽  
Mathematical Algorithms ◽  
Value Decomposition

The principle of registration of the 3D point cloud data and the current algorithms are compared, and ICP algorithm is chosen since its fast convergence speed, high precision, and simple objective function. On the basis of ICP algorithm, singular value decomposition and four-array method are analysed by programming program, and all the mathematical algorithms is transformed into programming language by Matlab software.

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