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.

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.

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.

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 Evaluating Thermal Attribute Mapping Strategies for Oblique Airborne Photogrammetric System AOS-Tx8

2019 ◽  
Vol 12 (1) ◽  
pp. 112 ◽  
Author(s):  
Dong Lin ◽  
Lutz Bannehr ◽  
Christoph Ulrich ◽  
Hans-Gerd Maas
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Texture Mapping ◽  
Point Clouds ◽  
Registration Accuracy ◽  
Point Cloud Registration ◽  
Thermal Imagery ◽  
Processing Scheme ◽  
Data Registration ◽  
Thermal Cameras

Thermal imagery is widely used in various fields of remote sensing. In this study, a novel processing scheme is developed to process the data acquired by the oblique airborne photogrammetric system AOS-Tx8 consisting of four thermal cameras and four RGB cameras with the goal of large-scale area thermal attribute mapping. In order to merge 3D RGB data and 3D thermal data, registration is conducted in four steps: First, thermal and RGB point clouds are generated independently by applying structure from motion (SfM) photogrammetry to both the thermal and RGB imagery. Next, a coarse point cloud registration is performed by the support of georeferencing data (global positioning system, GPS). Subsequently, a fine point cloud registration is conducted by octree-based iterative closest point (ICP). Finally, three different texture mapping strategies are compared. Experimental results showed that the global image pose refinement outperforms the other two strategies at registration accuracy between thermal imagery and RGB point cloud. Potential building thermal leakages in large areas can be fast detected in the generated texture mapping results. Furthermore, a combination of the proposed workflow and the oblique airborne system allows for a detailed thermal analysis of building roofs and facades.

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

A Novel Surface Registration for Image-Guided Neurosurgery: Effects of Intervals of Points in Patient Space on Registration Accuracy

2020 ◽  
Vol 10 (6) ◽  
pp. 1466-1472
Author(s):  
Hakje Yoo ◽  
Ahnryul Choi ◽  
Hyunggun Kim ◽  
Joung Hwan Mun
Keyword(s):  
Success Rate ◽  
Point Cloud ◽  
Surgical Navigation ◽  
Spline Interpolation ◽  
Surface Registration ◽  
Registration Accuracy ◽  
Cubic Spline Interpolation ◽  
Registration Error ◽  
Target Registration Error ◽  
Image Guided

Surface registration is an important factor in surgical navigation in determining the success rate and stability of surgery by providing the operator with the exact location of a lesion. The problem of surface registration is that point cloud in the patient space is acquired at irregular intervals due to the operator’s tracking speed and method. The purpose of this study is to analyze the effect of irregular intervals of point cloud caused by tracking speed and method on the registration accuracy. For this study, we created the head phantom to obtain a point cloud in the patient space with a similar object to that of a patient and acquired a point cloud in a total of ten times. In order to analyze the accuracy of registration according to the interval, cubic spline interpolation was applied to the existing point cloud. Additionally, irregular intervals of the point cloud were regenerated into regular intervals. As a result of applying the regenerated point cloud to the surface registration, the surface registration error was not statistically different from the existing point cloud. However, the target registration error significantly lower (p < 0.01). These results indicate that the intervals of point cloud affect the accuracy of registration, and that point cloud with regular intervals can improve the surface registration accuracy.

scholarly journals APPLICATION OF THE STEEP SLOPE RISK ASSESSMENT USING THREE DIMENSIONAL INFORMATION DATA

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 381-386
Author(s):  
D. Y. Shin ◽  
J. S. Sim ◽  
K. S. Lee
Keyword(s):  
Risk Assessment ◽  
Inclination Angle ◽  
Point Cloud ◽  
Spatial Information ◽  
Three Dimensional ◽  
Steep Slope ◽  
Point Cloud Data ◽  
Registration Accuracy ◽  
Terrestrial Lidar ◽  
Cloud Data

<p><strong>Abstract.</strong> A collapse of slope is one of the natural disasters that often occur during the early spring and the rainy season. In order to prevent this kind of disaster, safety monitoring is carried out through risk assessment. This assessment consists of various parameters such as inclination angle and height of the slope, and inspectors evaluate the score using the compass, the laser range finder, and so on. This approach is, however, consumed a lot of the manpower and the time. This study, therefore, aims to evaluate the rapid and accurate steep slope risk by using a terrestrial LiDAR which takes 3 dimensional spatial information data. 3D spatial information data was acquired using the terrestrial LiDAR for steep slopes classified as very unstable slopes. Noise and vegetation of the acquired scan data were removed to generate point cloud data with a rock or mountain model without vegetation. The RMSE of the registration accuracy was 0.0156 m. From the point cloud data, the inclination angle, height, shape, valley, collapse and loss were evaluated. As a result, various risk assessment parameters can be checked at once. In addition, it is expected to be used as basic data for constructing steep slope DB, providing visualization data, and time series analysis in the future.</p>

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