Effective Self-calibration for Camera Parameters and Hand-eye Geometry Based on Two Feature Points Motions

Jia Sun; Peng Wang; Zhengke Qin; Hong Qiao

doi:10.1109/JAS.2017.7510556

Volume 4 Issue 2

Apr. 2017

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(2): 370-380

Jia Sun, Peng Wang, Zhengke Qin and Hong Qiao, "Effective Self-calibration for Camera Parameters and Hand-eye Geometry Based on Two Feature Points Motions," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 370-380, Apr. 2017. doi: 10.1109/JAS.2017.7510556

Citation:

Jia Sun, Peng Wang, Zhengke Qin and Hong Qiao, "Effective Self-calibration for Camera Parameters and Hand-eye Geometry Based on Two Feature Points Motions," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 370-380, Apr. 2017. doi: 10.1109/JAS.2017.7510556

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Effective Self-calibration for Camera Parameters and Hand-eye Geometry Based on Two Feature Points Motions

doi: 10.1109/JAS.2017.7510556

Jia Sun^{1, 2
,},
Peng Wang^{1
,
,},
Zhengke Qin^{1, 2
,},
Hong Qiao^1
,

1.
Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100190, China

Funds:

This work was partly supported by the National Natural Science Foundation of China 61379097

This work was partly supported by the National Natural Science Foundation of China 61401463

This work was partly supported by the National Natural Science Foundation of China 61100098

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Author Bio:
Jia Sun received the B.S. degree from the North University of China, Taiyuan, China, in 2009, and the M.S. degree from the Beijing Institute of Technology, Beijing, China, in 2012. She is currently pursuing the Ph.D. degree with the University of Chinese Academy of Sciences, Beijing. Her current research interests include robot vision, precise assembly, and camera calibration.(e-mail: jia.sun@ia.ac.cn)

Zhengke Qin received the B.S. degree from the Huazhong University of Science and Technology, Wuhan, China, in 2012. He is currently pursuing the Ph.D. degree with the University of Chinese Academy of Sciences, Beijing. His current research interests include computer vision and robotic assembly.(e-mail: qinzhengke2012@ia.ac.cn)

Hong Qiao (SM'06) received the B.Eng. degree in hydraulics and control, the M.Eng. degree in robotics from Xi'an Jiaotong University, Xi'an, China, the M.Phil. degree in robotics control from the Industrial Control Center, University of Strathclyde, Strathclyde, U.K., and the Ph.D. degree in robotics and artificial intelligence from De Montfort University, Leicester, U.K., in 1995. She was a research assistant professor from 1997 to 2000 and an assistant professor from 2000 to 2002 with the Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong. In 2002, she joined as a lecturer with the School of Informatics, University of Manchester, Manchester, U.K. She is currently a professor with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.(e-mail: hong.qiao@ia.ac.cn)
Corresponding author: Peng Wang (M'11) received the B.Eng. degree in electrical engineering and automation from Harbin Engineering University, Harbin, China, in 2004, the M.Eng. degree in automation science and engineering from Harbin Institute of Technology, Harbin, China, in 2007, and the Ph.D. degree from Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2010. He is currently an associate professor with the Institute of Automation, Chinese Academy of Sciences, Beijing, China. His current research interests include intelligent robot, industrial robot, robotic assembly, robotic vision, image processing and visual perception model. (e-mail: peng wang@ia.ac.cn)
Received Date: 2016-07-04
Accepted Date: 2017-01-16

Abstract

Abstract

A novel and effective self-calibration approach for robot vision is presented, which can effectively estimate both the camera intrinsic parameters and the hand-eye transformation at the same time. The proposed calibration procedure is based on two arbitrary feature points of the environment, and three pure translational motions and two rotational motions of robot endeffector are needed. New linear solution equations are deduced, and the calibration parameters are finally solved accurately and effectively. The proposed algorithm has been verified by simulated data with different noise and disturbance. Because of the need of fewer feature points and robot motions, the proposed method greatly improves the efficiency and practicality of the calibration procedure.
- Camera calibration,
- hand-eye calibration,
- robot vision,
- two feature points

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References(28)

References

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Effective Self-calibration for Camera Parameters and Hand-eye Geometry Based on Two Feature Points Motions

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