scholarly journals Fast Method of Registration for 3D RGB Point Cloud with Improved Four Initial Point Pairs Algorithm

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 138 ◽  
Author(s):  
Peng Li ◽  
Ruisheng Wang ◽  
Yanxia Wang ◽  
Ge Gao
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Initial Point ◽  
Least Square ◽  
Color Filter ◽  
Fast Method ◽  
3D Point Cloud ◽  
Point Cloud Registration ◽  
Global Registration ◽  
Rgb Values

Three-dimensional (3D) point cloud registration is an important step in three-dimensional (3D) model reconstruction or 3D mapping. Currently, there are many methods for point cloud registration, but these methods are not able to simultaneously solve the problem of both efficiency and precision. We propose a fast method of global registration, which is based on RGB (Red, Green, Blue) value by using the four initial point pairs (FIPP) algorithm. First, the number of different RGB values of points in a dataset are counted and the colors in the target dataset having too few points are discarded by using a color filter. A candidate point set in the source dataset are then generated by comparing the similarity of colors between two datasets with color tolerance, and four point pairs are searched from the two datasets by using an improved FIPP algorithm. Finally, a rigid transformation matrix of global registration is calculated with total least square (TLS) and local registration with the iterative closest point (ICP) algorithm. The proposed method (RGB-FIPP) has been validated with two types of data, and the results show that it can effectively improve the speed of 3D point cloud registration while maintaining high accuracy. The method is suitable for points with RGB values.

3D-point-cloud registration and real-world dynamic modelling-based virtual environment building method for teleoperation

Robotica ◽  
2016 ◽  
Vol 35 (10) ◽  
pp. 1958-1974 ◽  
Author(s):  
Dejing Ni ◽  
Aiguo Song ◽  
Xiaonong Xu ◽  
Huijun Li ◽  
Chengcheng Zhu ◽  
...  
Keyword(s):  
Virtual Environment ◽  
Real World ◽  
Point Cloud ◽  
Effective Means ◽  
Dynamic Modelling ◽  
Least Square ◽  
Human Operator ◽  
3D Point Cloud ◽  
Point Cloud Registration ◽  
Registration Method

SUMMARYIt is a challenging task for a human operator to manipulate a robot from a remote distance, especially in an unknown environment. Excellent teleoperation provides the human operator with a sense of telepresence, mainly including real-world vision, haptic perception, etc. This paper presents a novel virtual environment building method using the red–green–blue (RGB) colour information, the surface normal feature-based 3D-point-cloud registration method and the weighted sliding-average least-square-method-based real-world dynamic modelling for teleoperation. The experiments prove the method to be an accurate and effective means of teleoperation.

Automated Surface Inspection Using 3D Point Cloud Data in Manufacturing: A Case Study

2018 ◽  
Author(s):  
Romina Dastoorian ◽  
Ahmad E. Elhabashy ◽  
Wenmeng Tian ◽  
Lee J. Wells ◽  
Jaime A. Camelio
Keyword(s):  
Point Cloud ◽  
Surface Defect ◽  
Fault Location ◽  
Three Dimensional ◽  
Practical Case ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Defect Inspection ◽  
Cloud Data

With the latest advancements in three-dimensional (3D) measurement technologies, obtaining 3D point cloud data for inspection purposes in manufacturing is becoming more common. While 3D point cloud data allows for better inspection capabilities, their analysis is typically challenging. Especially with unstructured 3D point cloud data, containing coordinates at random locations, the challenges increase with higher levels of noise and larger volumes of data. Hence, the objective of this paper is to extend the previously developed Adaptive Generalized Likelihood Ratio (AGLR) approach to handle unstructured 3D point cloud data used for automated surface defect inspection in manufacturing. More specifically, the AGLR approach was implemented in a practical case study to inspect twenty-seven samples, each with a unique fault. These faults were designed to cover an array of possible faults having three different sizes, three different magnitudes, and located in three different locations. The results show that the AGLR approach can indeed differentiate between non-faulty and a varying range of faulty surfaces while being able to pinpoint the fault location. This work also serves as a validation for the previously developed AGLR approach in a practical scenario.

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
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