scholarly journals Three-Dimensional Reconstruction of Welding Pool Surface by Binocular Vision

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zunan Gu ◽  
Ji Chen ◽  
Chuansong Wu
Keyword(s):  
Binocular Vision ◽  
Image Matching ◽  
Vision System ◽  
Gas Metal Arc Welding ◽  
Three Dimensional ◽  
Welding Pool ◽  
Speeded Up Robust Features ◽  
Pool Surface ◽  
Metal Arc Welding ◽  
Binocular Vision System

AbstractCurrent research of binocular vision systems mainly need to resolve the camera’s intrinsic parameters before the reconstruction of three-dimensional (3D) objects. The classical Zhang’ calibration is hardly to calculate all errors caused by perspective distortion and lens distortion. Also, the image-matching algorithm of the binocular vision system still needs to be improved to accelerate the reconstruction speed of welding pool surfaces. In this paper, a preset coordinate system was utilized for camera calibration instead of Zhang’ calibration. The binocular vision system was modified to capture images of welding pool surfaces by suppressing the strong arc interference during gas metal arc welding. Combining and improving the algorithms of speeded up robust features, binary robust invariant scalable keypoints, and KAZE, the feature information of points (i.e., RGB values, pixel coordinates) was extracted as the feature vector of the welding pool surface. Based on the characteristics of the welding images, a mismatch-elimination algorithm was developed to increase the accuracy of image-matching algorithms. The world coordinates of matching feature points were calculated to reconstruct the 3D shape of the welding pool surface. The effectiveness and accuracy of the reconstruction of welding pool surfaces were verified by experimental results. This research proposes the development of binocular vision algorithms that can reconstruct the surface of welding pools accurately to realize intelligent welding control systems in the future.

scholarly journals Three-dimensional reconstruction of welding pool surface by binocular vision

2020 ◽  
Author(s):  
Zunan Gu ◽  
Ji Chen ◽  
Chuansong Wu
Keyword(s):  
Binocular Vision ◽  
Vision System ◽  
Gas Metal Arc Welding ◽  
Three Dimensional ◽  
Welding Process ◽  
Welding Pool ◽  
Speeded Up Robust Features ◽  
Pool Surface ◽  
Metal Arc Welding ◽  
Dimensional Reconstruction

Abstract Welding pool geometry includes plenty of welding quality information. The observation and reconstruction of the welding pool surface is the basis of developing intelligent control system for welding process to substitute skilled welders. The binocular vision system was ameliorated to capture images of welding pool surface by suppressing the strong arc interference during gas metal arc welding (GMAW). Combining and improving the algorithms of speeded up robust features, binary robust invariant scalable keypoints and KAZE, the feature information of points (i.e. RGB value, pixel coordinates and so on) was extracted as the feature vector of the welding pool surface. Based on the characteristic of welding images, the mismatch elimination algorithm was developed to increase the accuracy of image matching algorithms. The world coordinates of matching feature points was calculated to reconstruct the 3D shape of the welding pool surface. The effectiveness and accuracy of reconstruction for welding pool surface were verified by the experimental results.

The On-Line Calibration Research of the Picking Robot Binocular Vision System

2012 ◽  
Vol 522 ◽  
pp. 634-637
Author(s):  
Ke Yin Chen ◽  
Xiang Jun Zou ◽  
Li Juan Chen
Keyword(s):  
Camera Calibration ◽  
Binocular Vision ◽  
Vision System ◽  
Three Dimensional ◽  
Reference Object ◽  
Systems Research ◽  
Accuracy And Precision ◽  
Calibration Algorithm ◽  
On Line ◽  
Binocular Vision System

In the picking robot binocular vision systems research, the camera calibration is often an indispensable step and these basements to locate the target of the object and rebuild the three-dimensional construction based on the robot stereo vision for the follow-up study. So, searching for a high accuracy and simple camera calibration algorithm is of great significance and necessary. However, For most of these camera calibration algorithms, it is necessary to establish a reference object, namely the target, in front of the camera at present, but posing the target is very not convenient or almost impossible in some cases. Therefore, a picking robot online calibration algorithm based on the vision scene was proposed by studying the work environment characteristics of the picking robot binocular vision system and the invariant projective geometry. The experimental results showed that this algorithm’s calibration accuracy and precision good meets to the requirement of the robot binocular vision system camera calibration in the complex environment.

A Binocular Vision System for Underwater Target Detection

2013 ◽  
Vol 347-350 ◽  
pp. 883-890 ◽  
Author(s):  
Jie Shen ◽  
Hong Ye Sun ◽  
Hui Bin Wang ◽  
Zhe Chen ◽  
Yi Wei
Keyword(s):  
Binocular Vision ◽  
Stereo Matching ◽  
Vision System ◽  
Three Dimensional ◽  
Processing Unit ◽  
Dimensional Reconstruction ◽  
Underwater Target ◽  
Three Dimensional Coordinate ◽  
Target Detecting ◽  
Binocular Vision System

For the underwater target detecting task, a binocular vision system specialized to the underwater optical environment is proposed. The hardware platform is comprised of a image acquising unit, a image processing unit and a upper computer. Accordingly, the loaded software system is operated for the camera calibration, image preprocessing, feature point extraction, stereo matching and the three-dimensional restoration. The improved Harris operator is introduced for the three-dimensional reconstruction, considering the high scattering and strong attenuation by the underwater optical environment. The experiment results prove that the improved Harris operator is better adapt to the complex underwater optical environment and the whole system has the ability to obtain the three-dimensional coordinate of the underwater target more efficient and accurate.

Machine Recognition of Laser Reflection From Gas Metal Arc Weld Pool Surfaces

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
ZhenZhou Wang ◽  
YuMing Zhang ◽  
XiaoJi Ma
Keyword(s):  
Weld Pool ◽  
Gas Metal Arc Welding ◽  
Three Dimensional ◽  
Quality Measurement ◽  
Radiation Background ◽  
Low Contrast ◽  
Pool Surface ◽  
Metal Arc Welding ◽  
Edge Points ◽  
Weld Pool Surface

The reflection of projected laser lines may be used to determine the three-dimensional geometry of the reflecting weld pool surface. However, for gas metal arc welding (GMAW), the transfer of the droplets into the weld pool makes the weld pool surface highly dynamic and fluctuating. The position and geometry of the local reflecting surface, which intercepts and reflects the projected laser changes rapidly. As a result, the reflection rays change their trajectories rapidly. The contrast of laser reflection with the background is much reduced and methods are needed to extract laser reflection from low contrast images. To this end, an image quality measurement method is proposed based on the number of the edge points to determine if an image may be further processed. The image to be processed is then modeled as a superposition of the laser reflection and arc radiation background. Methods have been proposed to remove the uneven distribution of the arc radiation background from the image, such that a global threshold is possible to segment the laser reflection lines. The set of the laser line points are then clustered to form separate laser lines. These laser lines are then modeled and the parameters in the models are used to validate each modeled line. Processing results verified the effectiveness of the proposed methods/algorithms in providing laser lines from low contrast images that are formed by laser reflection from a high dynamic gas metal arc weld pool surface.

scholarly journals Eye Gaze Based 3D Triangulation for Robotic Bionic Eyes

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5271
Author(s):  
Di Fan ◽  
Yanyang Liu ◽  
Xiaopeng Chen ◽  
Fei Meng ◽  
Xilong Liu ◽  
...  
Keyword(s):  
Binocular Vision ◽  
Visual Servoing ◽  
Vision System ◽  
Eye Gaze ◽  
Three Dimensional ◽  
Optical Center ◽  
Extrinsic Parameters ◽  
Active Binocular Vision ◽  
The Impact ◽  
Binocular Vision System

Three-dimensional (3D) triangulation based on active binocular vision has increasing amounts of applications in computer vision and robotics. An active binocular vision system with non-fixed cameras needs to calibrate the stereo extrinsic parameters online to perform 3D triangulation. However, the accuracy of stereo extrinsic parameters and disparity have a significant impact on 3D triangulation precision. We propose a novel eye gaze based 3D triangulation method that does not use stereo extrinsic parameters directly in order to reduce the impact. Instead, we drive both cameras to gaze at a 3D spatial point P at the optical center through visual servoing. Subsequently, we can obtain the 3D coordinates of P through the intersection of the two optical axes of both cameras. We have performed experiments to compare with previous disparity based work, named the integrated two-pose calibration (ITPC) method, using our robotic bionic eyes. The experiments show that our method achieves comparable results with ITPC.

Reconstruction of Three-Dimensional Gas Metal Arc Weld Pool Surface From Reflected Laser Pattern

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
XiaoJi Ma ◽  
YuMing Zhang
Keyword(s):  
Weld Pool ◽  
High Speed ◽  
Gas Metal Arc Welding ◽  
Three Dimensional ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Surface Geometry ◽  
Pool Surface ◽  
Metal Arc Welding ◽  
Weld Pool Surface

A system has been developed to measure the three-dimensional weld pool surface geometry in the gas metal arc welding (GMAW) process. It utilizes the specular nature of the weld pool surface by projecting a five-line laser pattern onto the surface and imaging its reflection. Specifically, the laser reflection is intercepted by an imaging plane and captured using a high speed camera. The reflected pattern is used to reconstruct the weld pool surface based on the law of reflection. Two reconstruction algorithms, referred to as center-points reconstruction and piece-wise weld pool surface reconstruction algorithm, are applied to sequentially reconstruct the weld pool height and three-dimensional surface geometry. Reconstructions has been conducted using simulated weld pool surface to provide a method to compare the reconstruction result with a known surface and evaluate the reconstruction accuracy. It is found that the proposed method is capable of reconstructing weld pool surface with acceptable accuracy. The height error of reconstructed center-points is less than 0.1 mm and the error of estimated weld pool boundary is less than 10%. Reconstruction results from images captured in welding experiments are also demonstrated.

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