On-Site Global Calibration of Mobile Vision Measurement System Based on Virtual Omnidirectional Camera Model

Binhu Chai; Zhenzhong Wei

doi:10.3390/rs13101982

On-Site Global Calibration of Mobile Vision Measurement System Based on Virtual Omnidirectional Camera Model

Remote Sensing ◽

10.3390/rs13101982 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1982

Author(s):

Binhu Chai ◽

Zhenzhong Wei

Keyword(s):

Coordinate System ◽

Measurement System ◽

Image Plane ◽

Calibration Data ◽

Single Image ◽

Camera Model ◽

Omnidirectional Camera ◽

Vision Measurement ◽

Global Calibration ◽

Mobile Vision

The mobile vision measurement system (MVMS) is widely used for location and attitude measurement in aircraft takeoff and landing, and its on-site global calibration is crucial to obtaining high-accuracy measurement aimed at obtaining the transformation relationship between the MVMS coordinate system and the local-tangent-plane coordinate system. In this paper, several new ideas are proposed to realize the global calibration of the MVMS effectively. First, the MVMS is regarded as azimuth and pitch measurement equipment with a virtual single image plane at focal length 1. Second, a new virtual omnidirectional camera model constructed by three mutual orthogonal image planes is put forward, which effectively resolves the problem of global calibration error magnification when the angle between the virtual single image plane and view axis of the system becomes small. Meanwhile, an expanded factorial linear method is proposed to solve the global calibration equations, which effectively restrains the influence of calibration data error. Experimental results with synthetic data verify the validity of the proposed method.

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Global Calibration Method for a Multi-Sensor Measurement System Based on Structured Light Stereo Vision

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.542-543.684 ◽

2012 ◽

Vol 542-543 ◽

pp. 684-689

Author(s):

Jun Yi Lin ◽

Kai Yong Jiang ◽

Chang Biao Huang ◽

Bin Liu

Keyword(s):

Coordinate System ◽

Stereo Vision ◽

Measurement System ◽

Structured Light ◽

Local Coordinate System ◽

Calibration Method ◽

Global Coordinate System ◽

Global Calibration ◽

The Common ◽

Sensor Measurement

The global calibration of multi-sensor measurement system is difficult to accomplish, usually, it needs external high-precision measurement equipment. A novel global calibration method based on common planar template is proposed. Stereo vision combined with laser linear-structured light is adopted to constitute a 3D vision sensor system (3DVSS). Stereo calibration of the 3DVSS can be finished by the planar template calibration method. For the global calibration of multiple vision sensors, it only needs a common planar template which is one of the planar templates can be seen synchronously by every camera in 3DVSS. The homography matrix between the coordinate systems of the 3DVSS and the common planer template can be calculated. The global coordinate is established in the common planar template, so the local coordinate system (LCS) of each 3DVSS can be transformed to the global coordinate system (GCS) through the common planar template. Two experiments have been used to test the proposed method, and the experimental results show that the proposed method is flexible and effective.

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Vision Measurement Scheme Using Single Camera Rotation

ISRN Machine Vision ◽

10.1155/2013/874084 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Shidu Dong

Keyword(s):

Coordinate System ◽

Rotation Angle ◽

Rotation Axis ◽

Translation Vector ◽

Single Camera ◽

Calibration Data ◽

Accelerometer Sensor ◽

Vision Measurement ◽

Measurement Scheme ◽

Camera Rotation

We propose vision measurement scheme for estimating the distance or size of the object in static scene, which requires single camera with 3-axis accelerometer sensor rotating around a fixed axis. First, we formulate the rotation matrix and translation vector from one coordinate system of the camera to another in terms of the rotation angle, which can be figured out from the readouts of the sensor. Second, with the camera calibration data and through coordinate system transformation, we propose a method for calculating the orientation and position of the rotation axis relative to camera coordinate system. Finally, given the rotation angle and the images of the object in static scene at two different positions, one before and the other after camera rotation, the 3D coordinate of the point on the object can be determined. Experimental results show the validity of our method.

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Computer vision measurement system for standards calibration in XY plane with sub-micrometer accuracy

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-019-04297-7 ◽

2019 ◽

Vol 105 (1-4) ◽

pp. 1531-1537 ◽

Cited By ~ 1

Author(s):

Pedro Bastos Costa ◽

Fabiana Rodrigues Leta ◽

Felipe de Oliveira Baldner

Keyword(s):

Computer Vision ◽

Measurement System ◽

Vision Measurement

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The Simulation of Vision Measurement System for Cannon Barrel Pose

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.588-589.1337 ◽

2012 ◽

Vol 588-589 ◽

pp. 1337-1340

Author(s):

Y.X. Zhu ◽

X.S. Duan

Keyword(s):

Computer Simulation ◽

Measurement System ◽

Simulation Experiment ◽

New Method ◽

Simulation Platform ◽

Motion Model ◽

Vision Measurement ◽

Imaging Model

For the pose measurement of cannon barrel, a vision method through checked plane had been proposed. To test and improve the precision of this new method without considering the hardware error and some other inextricable objective factors，derive the imaging model of the marker (checked plane) from motion model of cannon barrel and the position relative to it using variable-controlling method. Establish the computer simulation platform of vision measurement system for cannon barrel pose based on C++ Builder. The simulation experiment validate the veracity and dependability of this method.

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Design of Program Framework of Binocular Vision Measurement System Based on DM642

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.220-223.1311 ◽

2012 ◽

Vol 220-223 ◽

pp. 1311-1314

Author(s):

Chen Dong ◽

Shu Guang Dai

Keyword(s):

Signal Processing ◽

Binocular Vision ◽

Measurement System ◽

Video Processing ◽

Measurement Instrument ◽

Rapid Progress ◽

Image Capture ◽

Video Signal Processing ◽

Vision Measurement ◽

Binocular Measurement

As the rapid progress and development of DSP technology, a new way to solve the video signal processing was accepted. At present, binocular measurement instrument are mostly based on image capture card and PC on the market, expensive and bulky. For this problem, binocular vision measurement system based on DSP has been used. The design of elementary program structure about video processing which based on DSP/BIOS was finished and verified for its real-time. The results of the experiment show that the driver can be well used in the system.

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A Self-Calibration Method for Robotic Measurement System

10.1115/imece1999-0753 ◽

1999 ◽

Author(s):

Chunhe Gong ◽

Jingxia Yuan ◽

Jun Ni

Keyword(s):

Coordinate System ◽

Measurement System ◽

Calibration Method ◽

Position Measurement ◽

Robot Calibration ◽

Robot System ◽

Self Calibration ◽

World Coordinate System ◽

Robot Accuracy ◽

Calibration Methods

Abstract Robot calibration plays an increasingly important role in manufacturing. For robot calibration on the manufacturing floor, it is desirable that the calibration technique be easy and convenient to implement. This paper presents a new self-calibration method to calibrate and compensate for robot system kinematic errors. Compared with the traditional calibration methods, this calibration method has several unique features. First, it is not necessary to apply an external measurement system to measure the robot end-effector position for the purpose of kinematic identification since the robot measurement system has a sensor as its integral part. Second, this self-calibration is based on distance measurement rather than absolute position measurement for kinematic identification; therefore the calibration of the transformation from the world coordinate system to the robot base coordinate system, known as base calibration, is not necessary. These features not only greatly facilitate the robot system calibration but also shorten the error propagation chain, therefore, increase the accuracy of parameter estimation. An integrated calibration system is designed to validate the effectiveness of this calibration method. Experimental results show that after calibration there is a significant improvement of robot accuracy over a typical robot workspace.

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IMAGE NETWORK GENERATION OF UNCALIBRATED UAV IMAGES WITH LOW-COST GPS DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b3-31-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 31-37 ◽

Cited By ~ 1

Author(s):

Shan Huang ◽

Zuxun Zhang ◽

Jianan He ◽

Tao Ke

Keyword(s):

Coordinate System ◽

Low Cost ◽

Reference Image ◽

Model Parameters ◽

Camera Model ◽

Orientation Data ◽

Network Generation ◽

Free Network ◽

Uav Images ◽

Image Network

The use of unmanned air vehicle (UAV) images acquired by a non-metric digital camera to establish an image network is difficult in cases without accurate camera model parameters. Although an image network can be generated by continuously calculating camera model parameters during data processing as an incremental structure from motion (SfM) methods, the process is time consuming. In this study, low-cost global position system (GPS) information is employed in image network generation to decrease computational expenses. Each image is considered as reference, and its neighbor images are determined based on GPS coordinates during processing. The reference image and its neighbor images constitute an image group, which is used to generate a free network through image matching and relative orientation. Data are then transformed from the free network coordinate system of each group into the GPS coordinate system by using the GPS coordinates of each image. After the exterior elements of each image are determined in the GPS coordinate system, the initial image network is established. Finally, self-calibration bundle adjustment constrained by GPS coordinates is conducted to refine the image network. The proposed method is validated on three fields. Results confirm that the method can achieve good image network when accurate camera model parameters are unavailable.

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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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