Abstract
We propose a novel high-accuracy active hand-eye calibration approach. In our method, the robot movement and camera view selection are both driven by and targeting the improvement of calibration accuracy. During the calibration process, the data acquisition is guided by an online estimated discrete viewing quality field (DVQF), representing the calibration quality of different views in various 3D locations. The view quality is measured by how much it reduces the uncertainty of calibration results and increases the diversity of robot poses, contributing to the calibration precision. Based on DVQF, we select the next-best-view as the target moving pose for each time step. A fully automatic system is presented to perform the overall hand-eye calibration process without any human intervention. Numerous experiments are conducted both in real-world and simulated scenarios. The proposed algorithm outperforms other approaches and shows much superiority in accuracy and robustness.
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Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., Quillen, D.: Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. Int. J. Robot. Res. 37(4–5), 421 (2018)
Pachtrachai, K., Allan, M., Pawar, V., Hailes, S., Stoyanov, D.: In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2016), pp. 2485–2491
Chai, X., Wen, F., Cao, X., Yuan, K.: In: 2013 IEEE International Conference on Mechatronics and Automation (IEEE, 2013), pp. 57–62
Tsai, R.Y., Lenz, R.K., et al.: A new technique for fully autonomous and efficient 3 D robotics hand/eye calibration. IEEE Trans. Robot. Autom. 5(3), 345 (1989)
Flandin, G., Chaumette, F., Marchand, E.: In: Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), vol. 3 (IEEE, 2000), vol. 3, pp. 2741–2746
Dekel, A., Harenstam-Nielsen, L., Caccamo, S.: Optimal least-squares solution to the hand-eye calibration problem. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020), pp. 13598–13606
Shah, M.: Solving the robot-world/hand-eye calibration problem using the Kronecker product. J. Mech. Robot. 5(3) (2013)
Shiu, Y.C., Ahmad, S.: Calibration of wrist-mounted robotic sensors by solving homogeneous transform equations of the form AX= XB. IEEE Trans. Robot. Autom. 5(1), 16 (1989)
Antonello, M., Gobbi, A., Michieletto, S., Ghidoni, S., Menegatti, E.: In: 2017 European Conference on Mobile Robots (ECMR) (IEEE, 2017), pp. 1–6
Freese, M.: V-rep. https://www.coppeliarobotics.com/ (2019)
Horaud, R., Dornaika, F.: Hand-eye calibration. Int. J. Robot. Res. 14(3), 195 (1995)
Zhuang, H., Roth, Z.S., Sudhakar, R.: Simultaneous robot/world and tool/flange calibration by solving homogeneous transformation equations of the form AX= YB. IEEE Trans. Robot. Autom. 10(4), 549 (1994)
Dornaika, F., Horaud, R.: Simultaneous robot-world and hand-eye calibration. IEEE Trans. Robot. Autom. 14(4), 617 (1998)
Li, A., Wang, L., Wu, D.: Simultaneous robot-world and hand-eye calibration using dual-quaternions and Kronecker product. Int. J. Phys. Sci. 5(10), 1530 (2010)
Ali, I., Suominen, O., Gotchev, A., Morales, E.R.: Methods for simultaneous robot-world-hand-eye calibration: a comparative study. Sensors 19(12), 2837 (2019)
Tabb, A., Yousef, K.M.A.: Solving the robot-world hand-eye (s) calibration problem with iterative methods. Mach. Vis. Appl. 28(5), 569 (2017)
Schmidt, J., Vogt, F., Niemann, H.: In: Joint Pattern Recognition Symposium (2005)
Pachtrachai, K., Allan, M., Pawar, V., Hailes, S., Stoyanov, D.: In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2016)
Wang, J., Olson, E.: In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2016), pp. 4193–4198
Lepetit, V., Moreno-Noguer, F., Fua, P.: Epnp: An accurate o (n) solution to the PNP problem. Int. J. Comput. Vis. 81(2), 155 (2009)
Virtanen, P., Gommers, R., Oliphant, T.E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S.J., Brett, M., Wilson, J., Millman, K.J., Mayorov, N., Nelson, A.R.J., Jones, E., Kern, R., Larson, E., Carey, C.J., Polat, İ, Feng, Y., Moore, E.W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E.A., Harris, C.R., Archibald, A.M., Ribeiro, A.H., Pedregosa, F., van Mulbregt, P.: SciPy 1.0 contributors fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261 (2020). https://doi.org/10.1038/s41592-019-0686-2
Park, F.C., Martin, B.J.: Robot sensor calibration: solving AX=XB on the Euclidean group. IEEE Trans. Robot. Autom. 10(5), 717 (2002)
Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: In: ICRA workshop on open source software, vol. 3 (Kobe, Japan, 2009), vol. 3, p. 5
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This work was supported by the National Key Research and Development Program of China (No. 2018AAA0102200).
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Xinxin Zhang declares that he has no conflict of interest. Zhentao Huang declares that he has no conflict of interest. Lintao Zheng declares that he has no conflict of interest. Kai Xu declares that he has no conflict of interest.
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Xinxin Zhang and Yuefeng Xi are joint first authors.
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Zhang, X., Xi, Y., Huang, Z. et al. Active hand-eye calibration via online accuracy-driven next-best-view selection. Vis Comput 39, 381–391 (2023). https://doi.org/10.1007/s00371-021-02336-7
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DOI: https://doi.org/10.1007/s00371-021-02336-7