Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-25T20:57:22.276Z Has data issue: false hasContentIssue false
  • English
  • Français

A flexible method combining camera calibration and hand–eye calibration

Published online by Cambridge University Press:  01 February 2013

Zijian Zhao  and
Ying Weng
Zijian Zhao*
Affiliation:
School of Control Science and Engineering, Shandong University, Jinan 250061, China
Ying Weng
Affiliation:
School of Computer Science, Bangor University, Bangor LL57 1UT, UK
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

We consider the conventional techniques of vision robot system calibration where camera parameters and robot hand–eye parameters are computed separately, i.e., first performing camera calibration and then carrying out hand–eye calibration based on the calibrated parameters of cameras. In this paper we propose a joint algorithm that combines the camera calibration and the hand–eye calibration together. The proposed algorithm gives the solutions of the cameras' parameters and the hand–eye parameters simultaneously by using nonlinear optimization. Both simulations and real experiments show the superiority of our algorithm. We also apply our algorithm in the real application of the robot-assisted surgical system, and very good results have been obtained.

Keywords

Camera calibrationHand–eye calibrationRobot systemHomographyFactorization
Type
Articles
Information
Robotica , Volume 31 , Issue 5 , August 2013 , pp. 747 - 756
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Tsai, R. Y., “A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the shelf TV cameras and lens,” IEEE J. Robot. Autom. 3 (4), 322–344 (Aug. 1987).CrossRefGoogle Scholar
2.Faugeras, O., Three-Dimension Computer Vision: A Geometric Viewpoint (MIT Press, Cambridge, MA, 1993).Google Scholar
3.Gennery, D., “Stereo-camera calibration,” In: Proceedings of the 10th Image Understanding Workshop, Los Angeles, USA (1979) pp. 101–108.Google Scholar
4.Ganapathy, S., “Decomposition of transformation matrices for robot vision,” Pattern Recognit. Lett. 2, 401–412 (Dec. 1984).CrossRefGoogle Scholar
5.Lenz, R. K. and Tsai, R. Y., “Techniques for calibration of the scale factor and image center for high accuracy 3D machine vision metrology,” IEEE Trans. Pattern Anal. Mach. Intell. 10 (5), 713–720 (1988).CrossRefGoogle Scholar
6.Wei, G. Q. and Ma, S. D., “A Complete Two-Plane Camera Calibration Method and Experimental Comparisons,” In: Proceedings of the 4th International Conference on Computer Vision, Berlin, Germany (1993) pp. 439–446.Google Scholar
7.Zhang, Z., “A flexible new technique for camera calibration,” IEEE Trans. Pattern Anal. Mach. Intell. 22 (11), 1330–1334 (2000).CrossRefGoogle Scholar
8.Zhang, Z., “Camera calibration with one-dimensional objects,” IEEE Trans. Pattern Anal. Mach. Intell. 26 (7), 892–899 (2004).CrossRefGoogle ScholarPubMed
9.Sturm, P. and Maybank, S., “On Plane-Based Camera Calibration: A General Algorithm, Singularities, Applications,” In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Ft. Collins, USA (Jun. 1999) pp. 432–437.Google Scholar
10.Wu, F. C., Hu, Z. Y. and Zhu, H. J., “Camera calibration with moving one-dimensional objects,” Pattern Recognit. 38 (5), 355–365 (2005).CrossRefGoogle Scholar
11.Maybank, S. J. and Faugeras, O. D., “A theory of self-calibration of a moving camera,” Int. J. Comput. Vis. 8 (2), 123–152 (Aug. 1992).CrossRefGoogle Scholar
12.Hartley, R. I., “An Algorithm for Self-Calibration from Several Views,” In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Champaign, USA (Jun. 1992) pp. 908–912.Google Scholar
13.Luong, Q. T. and Faugeras, O., “Self-calibration of a moving camera from point correspondences and fundamental matrices,” Int. J. Comput. Vis. 22 (3), 261–289 (1997).CrossRefGoogle Scholar
14.Triggs, B., “Autocalibration from Planar Scenes,” In: Proceedings of the 5th European Conference on Computer Vision, Freiburg, Germany (Jun 1998) pp. 89–105.Google Scholar
15.Ueshiba, T. and Tomita, F., “Plane-Based Calibration Algorithm for Multi-Camera Systems via Factorization of Homography Matrices,” In: Proceedings of the 9th International Conference on Computer Vision, Beijing, China (2003) pp. 966–973.Google Scholar
16.Sturm, P., “Algorithms for Plane-Based Pose Estimation,” In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Dublin, Ireland (2000) pp. 706–711.Google Scholar
17.Hartley, R. and Zisserman, A., Multiple View Geometry in Computer Vision (Cambridge, UK: Cambridge University Press, 2000).Google Scholar
18.Tomasi, C. and Kanade, T., “Shape and motion from image streams under orthography: A factorization method,” Int. J. Comput. Vis. 9 (2), 137–154 (1992).CrossRefGoogle Scholar
19.Zelnik-Manor, L. and Irani, M., “Multiview constraints on homographies,” IEEE Trans. Pattern Anal. Mach. Intell. 24 (2), 214–223 (2002).CrossRefGoogle Scholar
20.Malm, H. and Heyden, A., “Simplified Intrinsic Camera Calibration and Hand-Eye Calibration for Robot Vision,” In: Proceedings of the 2003 IEEE/RSJ International Conference on Intelligence Robots and Systems, Sendai, Japan (2003) pp. 1037–1043.Google Scholar
21.Shiu, Y. C. and Ahmad, S., “Calibration of wrist-mounted robotics sensors by solving homogeneous transform equations of the form AX=XB,” IEEE Trans. Robot. Autom. 5 (1), 16–27 (1989).CrossRefGoogle Scholar
22.Tsai, R. K. and Lenz, R. Y., “A new technique for fully autonomous and efficient 3D robotics hand/eye calibration,” IEEE Trans. Robot. Autom. 5 (3), 345–358 (1989).CrossRefGoogle Scholar
23.Zhuang, H. and Shiu, Y. C., “A noise-tolerant algorithm for robotic hand–eye calibration with or without sensor orientation measurement,” IEEE Trans. Syst. Man Cybern. 23 (4), 1168–1175 (1993).CrossRefGoogle Scholar
24.Horaud, R. and Dornaika, F., “Hand-eye calibration,” Int. J. Robot. Res. 14 (3), 195–210 (1995).CrossRefGoogle Scholar
25.Daniilidis, K., “Hand-eye calibration using dual quaternions,” Int. J. Robot. Res. 18 (3), 286–298 (1999).CrossRefGoogle Scholar
26.Zhao, Z. and Liu, Y., “Hand-Eye Calibration Based on Screw Motions,” In: Proceedings of the 18th International Conference on Pattern Recognition, Hong Kong, China (2006) pp. 1022–1026.Google Scholar
27.Chen, H. H., “A Screw-Motion Approach to Uniqueness Analysis of Head-Eye Geometry,” In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Maui, USA (1991) pp. 145–151.Google Scholar
28.Zhang, H., Roth, Z. and Sudhakar, R., “Simultaneous robot/world and tool/flange calibration by solving homogeneous transformation of the form AX=YB,” IEEE Trans. Robot. Autom. 10 (4), 549–554 (1994).CrossRefGoogle Scholar
29.Dornaika, F. and Horaud, R., “Simultaneous robot-world and hand-eye calibration,” IEEE Trans. Robot. Autom. 14 (4), 617–622 (1998).CrossRefGoogle Scholar
30.Andreff, N., Horaud, R. and Espian, B., “On-Line Hand-Eye Calibration,” In: Proceedings of the 2nd International Conference on 3-D Digital Imaging and Modeling, Ottawa, Canada (1999) pp. 430–436.Google Scholar
31.Horn, B. K. P., Hilden, H. M. and Negahdaripour, S., “Close-form solution of absolute orientation using orthonormal matrices,” J. Opt. Soc. Am. 5 (7), 1127–1135 (1988).CrossRefGoogle Scholar
32.Zhao, Z., “Hand-Eye Calibration Using Convex Optimization,” In: Proceedings of the International Conference on Robotics and Automation, Shanghai, China (2011) pp. 2947–2952.Google Scholar