<abstract xmlns="http://www.w3.org/1999/xhtml">We present a reliable calibration method using the constraint of 2D projective lines and 3D world points to elaborate the accuracy of the camera calibration. Based on the relationship between the 3D points and the projective plane, the constraint equations of the transformation matrix are generated from the 3D points and 2D projective lines. The transformation matrix is solved by the singular value decomposition. The proposed method is compared with the point-based calibration to verify the measurement validity. The mean values of the root-mean-square errors using the proposed method are 7.69×10−4, 6.98×10−4, 2.29×10−4, and 1.09×10−3 while the ones of the original method are 8.10×10−4, 1.29×10−2, 2.58×10−2, and 8.12×10−3. Moreover, the average logarithmic errors of the calibration method are evaluated and compared with the former method in different Gaussian noises and projective lines. The variances of the average errors using the proposed method are 1.70×10−5, 1.39×10−4, 1.13×10−4, and 4.06×10−4, which indicates the stability and accuracy of the method.</abstract>

We present a reliable calibration method using the constraint of 2D projective lines and 3D world points to elaborate the accuracy of the camera calibration. Based on the relationship between the 3D points and the projective plane, the constraint equations of the transformation matrix are generated from the 3D points and 2D projective lines. The transformation matrix is solved by the singular value decomposition. The proposed method is compared with the point-based calibration to verify the measurement validity. The mean values of the root-mean-square errors using the proposed method are 7.69×10−4, 6.98×10−4, 2.29×10−4, and 1.09×10−3 while the ones of the original method are 8.10×10−4, 1.29×10−2, 2.58×10−2, and 8.12×10−3. Moreover, the average logarithmic errors of the calibration method are evaluated and compared with the former method in different Gaussian noises and projective lines. The variances of the average errors using the proposed method are 1.70×10−5, 1.39×10−4, 1.13×10−4, and 4.06×10−4, which indicates the stability and accuracy of the method.

Global Calibration Method of a Camera Using the Constraint of Line Features and 3D World Points

Department of Vehicle Application Engineering, Traffic and Transportation College, Nanling Campus, Jilin University, Renmin Street 5988#, 130025, Changchun, Jilin, P. R. China

Mechanical Science and Engineering College, Nanling Campus, Jilin University, Renmin Str. 5988#, 130025, Changchun, Jilin, P. R. China

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

We present a reliable calibration method using the constraint of 2D projective lines and 3D world points to elaborate the accuracy of the camera calibration....

Global Calibration Method of a Camera Using the Constraint of Line Features and 3D World Points

Published Online: Aug 19, 2016

Page range: 190 - 196

Received: Feb 28, 2016

Accepted: Jul 20, 2016

DOI: https://doi.org/10.1515/msr-2016-0023

Keywords
Calibration method, projective lines, measurement validity, Gaussian noises

© 2016 Guan Xu et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Global Calibration Method of a Camera Using the Constraint of Line Features and 3D World Points

Published Online: Aug 19, 2016

Page range: 190 - 196

Received: Feb 28, 2016

Accepted: Jul 20, 2016

DOI: https://doi.org/10.1515/msr-2016-0023

KeywordsCalibration method, projective lines, measurement validity, Gaussian noises

© 2016 Guan Xu et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Calibration method, projective lines, measurement validity, Gaussian noises