Skip to main content
SpringerLink
Log in

Multi-stage Objective Function Optimized Hand-Eye Self-calibration of Robot in Autonomous Environment

  • Conference paper
  • First Online:
Bio-Inspired Computing: Theories and Applications (BIC-TA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1801))

  • 682 Accesses

Abstract

In the application scenario of robot autonomous tasks, the robot needs to be able to complete calibration online and automatically to achieve self-maintenance, which differs from traditional robot hand-eye calibration in that the traditional robot hand-eye calibration requires a dedicated calibration board to assist offline completion. Aiming at the problem that the existing self-calibration methods cannot be optimized as a whole, which leads to low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed, which describes the solution of hand-eye self-calibration as a minimization objective function problem involving multiple stages. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Two different types of experiments were designed to validate our method. One is a single camera dataset experiment, which shows that our method is more accurate and robust than the existing self-calibration method; the other is an application platform experiment, which verifies the feasibility and availability of our method.

This work is supported by National Natural Science Foundation of China (Grant No.: 62073249).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Jiang, J., Luo, X., Luo, Q., Qiao, L., Li, M.: An overview of hand-eye calibration. Int. J. Adv. Manuf. Technol. 1–21 (2021). https://doi.org/10.1007/s00170-021-08233-6

  2. 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), 16–29 (1989). https://doi.org/10.1109/70.88014

    Article  Google Scholar 

  3. Tsai, R.Y., Lenz, R.K.: A new technique for fully autonomous and efficient 3D robotics hand/eye calibration. IEEE Trans. Robot. Autom. 5(3), 345–358 (1989). https://doi.org/10.1109/70.34770

    Article  Google Scholar 

  4. Zhang, Y., Qiu, Z., Zhang, X.: A simultaneous optimization method of calibration and measurement for a typical hand-eye positioning system. IEEE Trans. Instrum. Meas. 70, 1–11 (2021). https://doi.org/10.1109/tim.2020.3013308

    Article  Google Scholar 

  5. Daniilidis, K.: Hand-eye calibration using dual quaternions. Int. J. Robot. Res. 18(3), 286–298 (1999). https://doi.org/10.1177/02783649922066213

    Article  Google Scholar 

  6. Liu, Z., Liu, X., Duan, G., Tan, J.: Precise hand-eye calibration method based on spatial distance and epipolar constraints. Robot. Auton. Syst. 145, 103868 (2021). https://doi.org/10.1016/j.robot.2021.103868

    Article  Google Scholar 

  7. Andreff, N., Horaud, R., Espiau, B.: On-line hand-eye calibration. In: Second IEEE International Conference on 3-D Digital Imaging and Modeling, pp. 430–436. IEEE, New York (1999). https://doi.org/10.1109/im.1999.805374

  8. Heller, J., Havlena, M., Pajdla, T.: A branch-and-bound algorithm for globally optimal hand-eye calibration. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1608–1615. IEEE, New York (2012). https://doi.org/10.1109/cvpr.2012.6247853

  9. Xie, X., Peng, Z.: A hand-eye calibration method based on robot with stationary viewpoint. China Measur. Test (2018)

    Google Scholar 

  10. Zhi, X., Schwertfeger, S.: Simultaneous hand-eye calibration and reconstruction. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1470–1477. IEEE, New York (2017). https://doi.org/10.1109/iros.2017.8205949

  11. Ng, P.C., Henikoff, S.: SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31(13), 3812–3814 (2003). https://doi.org/10.1093/nar/gkg509

    Article  Google Scholar 

  12. Xu, G.S., Yan, Y.H.: A scene feature-based auto hand-eye calibration method for industrial robot. Mach. Des. Res. 4, 179–191 (2021). https://doi.org/10.1007/978-3-030-43703-9_15

    Article  Google Scholar 

  13. Xu, J., Hoo, J.L., Dritsas, S.: Hand-eye calibration for 2D laser profile scanners using straight edges of common objects. Robot. Comput.-Integr. Manuf. 73, 102221 (2022). https://doi.org/10.1016/j.rcim.2021.102221

    Article  Google Scholar 

  14. Rublee, E., Rabaud, V., Konolige, K., et al.: ORB: an efficient alternative to SIFT or SURF. In: 2011 International Conference on Computer Vision, pp. 2564–2571. IEEE, New York (2011). https://doi.org/10.1109/iccv.2011.6126544

  15. Mair, E., Hager, G.D., Burschka, D., Suppa, M., Hirzinger, G.: Adaptive and generic corner detection based on the accelerated segment test. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 183–196. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15552-9_14

    Chapter  Google Scholar 

  16. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_56

    Chapter  Google Scholar 

  17. Campos, C., Elvira, R., Rodríguez, J.J.G., et al.: ORB-SLAM3: an accurate open-source library for visual, visual-inertial, and multimap SLAM. IEEE Trans. Rob. 37(6), 1874–1890 (2021). https://doi.org/10.1109/tro.2021.3075644

    Article  Google Scholar 

  18. Bian, J.-W., et al.: GMS: grid-based motion statistics for fast, ultra-robust feature correspondence. Int. J. Comput. Vis. 128(6), 1580–1593 (2019). https://doi.org/10.1007/s11263-019-01280-3

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China

    Kaibo Liu & Yunhan Lin

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China

    Kaibo Liu & Yunhan Lin

  3. State Grid Electric Power Research Institute, Nanjing, 211106, Jiangsu, China

    Wangli Zheng

  4. Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China

    Huasong Min & Yunhan Lin

Authors
  1. Kaibo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wangli Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huasong Min
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yunhan Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yunhan Lin .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Wuhan University of Technology, Wuhan, China

    Dongming Zhao

  3. Huazhong University of Science and Technology, Wuhan, China

    Lianghao Li

  4. Huazhong University of Science and Technology, Wuhan, China

    Jianqing Lin

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, K., Zheng, W., Min, H., Lin, Y. (2023). Multi-stage Objective Function Optimized Hand-Eye Self-calibration of Robot in Autonomous Environment. In: Pan, L., Zhao, D., Li, L., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2022. Communications in Computer and Information Science, vol 1801. Springer, Singapore. https://doi.org/10.1007/978-981-99-1549-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-1549-1_12

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-1548-4

  • Online ISBN: 978-981-99-1549-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation