Computer Hard Disk Planeness Vision Measurement System Calibration Based on Light Plane Constraint

This paper proposed a calibration method of sheet-of-light vision measurement sensor based on light plane constraint. Through capturing 12 images of different direction from homemade circular calibration target, the center of the circle and the light stripe is extracted based on Halcon platform of Germany. The experimental results obtained the intrinsic parameters, extrinsic parameters and radial distortion coefficient of the nonlinear model. At the same time the light plane constraint equation is got based on PCA plane fitting method. The results show that the calibration method is simple and reliable, and the method does not need any auxiliary adjustment. The work laid the better foundation for hard disk planeness vision measurement.

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Hard-Disk Stereo Vision Measurement System Calibration Using Light Plane Constraint

TELKOMNIKA Indonesian Journal of Electrical Engineering ◽

10.11591/telkomnika.v12i6.5526 ◽

2014 ◽

Vol 12 (6) ◽

Author(s):

Rui-Yin Tang ◽

Peng-Fei Li

Keyword(s):

Stereo Vision ◽

Measurement System ◽

Hard Disk ◽

System Calibration ◽

Vision Measurement ◽

Light Plane ◽

Stereo Vision Measurement

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Field Geometric Calibration Method for Line Structured Light Sensor Using Single Circular Target

Scientific Programming ◽

10.1155/2017/1526706 ◽

2017 ◽

Vol 2017 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Tianfei Chen ◽

Lijun Sun ◽

Qiuwen Zhang ◽

Xiang Wu ◽

Defeng Wu

Keyword(s):

Structured Light ◽

Linear Equations ◽

Calibration Method ◽

Experimental Result ◽

Normal Vector ◽

Geometric Calibration ◽

Light Plane ◽

Light Sensor ◽

Fitting Method ◽

Circular Target

To achieve fast calibration of line structured light sensor, a geometric calibration approach based on single circular calibration target is proposed. The proposed method uses the circular points to establish linear equations, and according to the angle constraint, the camera intrinsic parameters can be calculated through optimization. Then, the light plane calibration is accomplished in two steps. Firstly, when the vanishing lines of target plane at various postures are obtained, the intersections between vanishing lines and laser stripe can be computed, and the normal vector of light plane can be calibrated via line fitting method using intersection points. After that, the distance from the origin of camera coordinate system to the light plane can be derived based on the model of perspective-three-point. The actual experimental result shows that this calibration method has high accuracy, its average measuring accuracy is 0.0451 mm, and relative error is 0.2314%. In addition, the entire calibration process has no complex operations. It is simple, convenient, and suitable for calibration on sites.

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Camera Calibration of Binocular Vision Based on Virtual 1D Target

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.605-607.859 ◽

2012 ◽

Vol 605-607 ◽

pp. 859-863

Author(s):

Yu Bo Guo ◽

Gang Chen ◽

Dong Ye ◽

Feng Yuan

Keyword(s):

Binocular Vision ◽

Constraint Equation ◽

Calibration Method ◽

Epipolar Constraint ◽

Field Calibration ◽

Extrinsic Parameter ◽

Extrinsic Parameters ◽

Virtual Target ◽

Flexible Application ◽

Easy Operation

A field calibration method based on virtual 1D target is proposed for the extrinsic parameters of binocular vision. A target is placed on high precision 1D lifting platform to create virtual 1D target through motions of lifting platform. Two cameras are used to obtain virtual target images of different positions and preliminarily achieve extrinsic parameter calibration of binocular vision based on epipolar constraint equation. Finally, the length of virtual 1-D target is used to optimize the extrinsic parameters. This method is featured with easy operation, flexible application and field calibration. The experimental results verify the feasibility of this calibration method and show it can yield high field calibration precision.

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Calibration Method Using Concentric Circles Planar Template and its Application

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.460-461.111 ◽

2011 ◽

Vol 460-461 ◽

pp. 111-116

Author(s):

Guang Yu Zhu ◽

Jian Qing Zou ◽

Peng Guo ◽

Huai Ceng Zheng

Keyword(s):

High Precision ◽

Calibration Method ◽

Random Errors ◽

Cable Line ◽

System Calibration ◽

Vision Measurement ◽

Dimension Measurement ◽

Industrial Dimension ◽

Concentric Circles ◽

Scene Depth

This article introduces a new calibration method for machine vision measurement system--calibration method using concentric circles planar template. This method not only considering lens distortion and random errors introduced by the process of calibration, but also overcome limitation of strict demands for the standard parts’ position and complexity in stereovision computation, it also capable of conduct calibration to the whole scene depth space, effectively improve the efficiency of calibration with its simple and high precision mean. A relatively high precision can be achieved by applying this new method to diameter measurement of cable line, which is suitable to conduct on-field industrial dimension measurement calibration.

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Extrinsic Parameters Calibration Method of Cameras with Non-Overlapping Fields of View in Airborne Remote Sensing

Remote Sensing ◽

10.3390/rs10081298 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1298 ◽

Cited By ~ 3

Author(s):

Lei Yin ◽

Xiangjun Wang ◽

Yubo Ni ◽

Kai Zhou ◽

Jilong Zhang

Keyword(s):

Remote Sensing ◽

Camera Calibration ◽

Calibration Method ◽

Optimization Method ◽

Airborne Remote Sensing ◽

Camera System ◽

Translation Error ◽

Camera Systems ◽

Calibration Methods ◽

Extrinsic Parameters

Multi-camera systems are widely used in the fields of airborne remote sensing and unmanned aerial vehicle imaging. The measurement precision of these systems depends on the accuracy of the extrinsic parameters. Therefore, it is important to accurately calibrate the extrinsic parameters between the onboard cameras. Unlike conventional multi-camera calibration methods with a common field of view (FOV), multi-camera calibration without overlapping FOVs has certain difficulties. In this paper, we propose a calibration method for a multi-camera system without common FOVs, which is used on aero photogrammetry. First, the extrinsic parameters of any two cameras in a multi-camera system is calibrated, and the extrinsic matrix is optimized by the re-projection error. Then, the extrinsic parameters of each camera are unified to the system reference coordinate system by using the global optimization method. A simulation experiment and a physical verification experiment are designed for the theoretical arithmetic. The experimental results show that this method is operable. The rotation error angle of the camera’s extrinsic parameters is less than 0.001rad and the translation error is less than 0.08 mm.

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Constrained Multiple Planar Reconstruction for Automatic Camera Calibration of Intelligent Vehicles

Sensors ◽

10.3390/s21144643 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4643

Author(s):

Sang Jun Lee ◽

Jeawoo Lee ◽

Wonju Lee ◽

Cheolhun Jang

Keyword(s):

Camera Calibration ◽

Calibration Method ◽

Image Features ◽

Intelligent Vehicles ◽

Point Problem ◽

Reconstruction Method ◽

Calibration Methods ◽

The World ◽

Extrinsic Parameters ◽

Automatic Camera Calibration

In intelligent vehicles, extrinsic camera calibration is preferable to be conducted on a regular basis to deal with unpredictable mechanical changes or variations on weight load distribution. Specifically, high-precision extrinsic parameters between the camera coordinate and the world coordinate are essential to implement high-level functions in intelligent vehicles such as distance estimation and lane departure warning. However, conventional calibration methods, which solve a Perspective-n-Point problem, require laborious work to measure the positions of 3D points in the world coordinate. To reduce this inconvenience, this paper proposes an automatic camera calibration method based on 3D reconstruction. The main contribution of this paper is a novel reconstruction method to recover 3D points on planes perpendicular to the ground. The proposed method jointly optimizes reprojection errors of image features projected from multiple planar surfaces, and finally, it significantly reduces errors in camera extrinsic parameters. Experiments were conducted in synthetic simulation and real calibration environments to demonstrate the effectiveness of the proposed method.

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Calibration Method for Line-Structured Light Three-Dimensional Measurements Based on a Single Circular Target

Applied Sciences ◽

10.3390/app12020588 ◽

2022 ◽

Vol 12 (2) ◽

pp. 588

Author(s):

Jun Wang ◽

Xuexing Li

Keyword(s):

Structured Light ◽

Three Dimensional ◽

Calibration Method ◽

Optical Center ◽

3D Measurements ◽

Light Plane ◽

Laser Projector ◽

Dimensional Measurements ◽

Intersection Points ◽

Circular Target

Single circular targets are widely used as calibration objects during line-structured light three-dimensional (3D) measurements because they are versatile and easy to manufacture. This paper proposes a new calibration method for line-structured light 3D measurements based on a single circular target. First, the target is placed in several positions and illuminated by a light beam emitted from a laser projector. A camera captures the resulting images and extracts an elliptic fitting profile of the target and the laser stripe. Second, an elliptical cone equation defined by the elliptic fitting profile and optical center of the camera is established based on the projective geometry. By combining the obtained elliptical cone and the known diameter of the circular target, two possible positions and orientations of the circular target are determined and two groups of 3D intersection points between the light plane and the circular target are identified. Finally, the correct group of 3D intersection points is filtered and the light plane is progressively fitted. The accuracy and effectiveness of the proposed method are verified both theoretically and experimentally. The obtained results indicate that a calibration accuracy of 0.05 mm can be achieved for an 80 mm × 80 mm planar target.

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Global Calibration of Multi-Cameras Based on Refractive Projection and Ray Tracing

10.20944/preprints201710.0077.v1 ◽

2017 ◽

Author(s):

Mingchi Feng ◽

Xiang Jia ◽

Jingshu Wang ◽

Song Feng ◽

Taixiong Zheng

Keyword(s):

Ray Tracing ◽

Real Data ◽

Calibration Method ◽

Calibration Error ◽

Multiple Cameras ◽

Projection Model ◽

Vision Measurement ◽

3D Computer Vision ◽

Global Calibration ◽

Calibration Methods

Multi-cameras system is widely applied in 3D computer vision especially when multiple cameras are distributed on both sides of the measured object. The calibration methods of multi-cameras system are critical to the accuracy of vision measurement and the key is to find an appropriate calibration target. In this paper, a high-precision camera calibration method for multi-cameras system based on transparent glass checkerboard and ray tracing is described, which is used to calibrate multiple cameras distributed on both sides of the glass checkerboard. Firstly, the intrinsic parameters of each camera is obtained by Zhang’s calibration method. Then, multiple cameras capture several images from the front and back of the glass checkerboard with different orientations, and all images contain distinct grid corners. As the cameras on one side are not affected by the refraction of glass checkerboard, extrinsic parameters can be directly calculated. However, the cameras on another side are influenced by the refraction of glass checkerboard, and the direct use of projection model will produce calibration error. A multi-cameras calibration method using refractive projection model and ray tracing is developed to eliminate this error. Furthermore, both synthetic and real data are employed to validate the proposed approach. The experimental results of refractive calibration show that the error of the 3D reconstruction is smaller than 0.2 mm, the relative errors of both rotation and translation are less than 0.014%, and the mean and standard deviation of reprojection error of 4-cameras system are 0.00007 and 0.4543 pixel. The proposed method is flexible, high accurate, and simple to carry out.

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