Advertisement

Bounded Recursive Optimization Approach for Pose Estimation in Robotic Visual Servoing

  • Yuchen Zhang
  • Bo ChenEmail author
  • Li Yu
  • Haiyu Song
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11740)

Abstract

Pose estimation problem is concerned with determining position and orientation of an object in real time using the image information, and has found applications in many fields such as object recognition and robotic visual servoing. Most of vision-based pose estimation schemes are derived from extended Kalman filter, which requires that the noises obey the Gaussian distribution under known covariance. However, the statistical information in robot control may not be accurately obtained or satisfied. In this paper, a novel bounded recursive optimization approach is proposed to solve the pose estimation problem in visual serving, where the addressed noises do not provide any statistical information, and the bounds of noises are also unknown. Finally, the pose estimation simulation is conducted to show the advantages and effectiveness of the proposed approach.

Keywords

Pose estimation Visual servoing Bounded recursive optimization approach 

References

  1. 1.
    Janabi-Sharifi, F., Wilson, W.J.: Automatic selection of image features for visual servoing. IEEE Transact. Robot. Autom. 13(6), 890–903 (1997)CrossRefGoogle Scholar
  2. 2.
    Haralick, B.M., Lee, C.N., Ottenberg, K., Nölle, M.: Review and analysis of solutions of the three point perspective pose estimation problem. Int. J. Comput. Vis. 13(3), 331–356 (1994)CrossRefGoogle Scholar
  3. 3.
    Faugeras, O., Faugeras, O.A.: Three-dimensional Computer Vision: A Geometric Viewpoint. MIT press, Cambridge (1993)zbMATHGoogle Scholar
  4. 4.
    Lowe, D.G.: Three-dimensional object recognition from single two-dimensional images. Artif. Intell. 31(3), 355–395 (1987)CrossRefGoogle Scholar
  5. 5.
    Lu, C.P., Hager, G.D., Mjolsness, E.: Fast and globally convergent pose estimation from video images. IEEE Transact. Pattern Anal. Mach. Intell. 22(6), 610–622 (2000)CrossRefGoogle Scholar
  6. 6.
    Wilson, W.J., Hulls, C.W., Janabi-Sharifi, F.: Robust Image Processing and Position-Based Visual Servoing, pp. 163–201. IEEE Press, New York (2000)Google Scholar
  7. 7.
    Ficocelli, M., Janabi-Sharifi, F.: Adaptive filtering for pose estimation in visual servoing. In: Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No. 01CH37180), vol. 1, pp. 19–24. IEEE (2001) Google Scholar
  8. 8.
    Janabi-Sharifi, F., Marey, M.: A kalman-filter-based method for pose estimation in visual servoing. IEEE Transact. Rob. 26(5), 939–947 (2010)CrossRefGoogle Scholar
  9. 9.
    Chen, B., Hu, G., Ho, D.W., Zhang, W.A., Yu, L.: Distributed robust fusion estimation with application to state monitoring systems. IEEE Transact. Syst. Man Cybern. Syst. 47(11), 2994–3005 (2017)CrossRefGoogle Scholar
  10. 10.
    Chen, B., Hu, G., Ho, D.W., Yu, L.: A new approach to linear/nonlinear distributed fusion estimation problem. IEEE Transact. Autom. Control 64(3), 1301–1308 (2019)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Chen, B., Hu, G.: Nonlinear state estimation under bounded noises. Automatica 98, 159–168 (2018)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Wilson, W.J., Hulls, C.W., Bell, G.S.: Relative end-effector control using cartesian position based visual servoing. IEEE Transact. Robot. Autom. 12(5), 684–696 (1996)CrossRefGoogle Scholar
  13. 13.
    Boyd, S., El Ghaoui, L., Feron, E., Balakrishnan, V.: Studies in applied mathematics: Vol. 15. Linear matrix inequalities in system and control theory. Philadelphia, PA: SIAM (1994)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of AutomationZhejiang University of TechnologyHangzhouPeople’s Republic of China
  2. 2.Institute of Cyberspace SecurityZhejiang University of TechnologyHangzhouPeople’s Republic of China
  3. 3.College of InformationZhejiang University of Finance and EconomicsHangzhouPeople’s Republic of China

Personalised recommendations