Skip to main content

Advertisement

SpringerLink
Log in

A Novel Pose Estimation Algorithm Based on Points to Regions Correspondence

  • Published:
Journal of Mathematical Imaging and Vision Aims and scope Submit manuscript

Abstract

A novel pose estimation algorithm is put forward in this paper. Given the points on an object and the convex regions in which the correspondent image points lie, the concrete values of position and orientation (t and R) between the object and the camera are found based on a points to regions correspondence. The unit quaternion representation of rotation matrix and convex Linear Matrix Inequalities (LMI) optimization methods are used to estimate the pose. By loosening the requirement of precise point to point correspondence and using convex LMI formulations, this algorithm provides a more robust and faster pose estimation method. The effect of this method is verified by simulation and laboratory experiment results.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Grimson, W.E.L.: Object Recognition by Computer. MIT Press, Cambridge (1990)

    Google Scholar 

  2. Ekvall, S., Hoffmann, F.: Object recognition and pose estimation for robotic manipulation using color cooccurrence histograms. In: IROS 2003, Las Vegas, Nevada (2003)

  3. Hutchinson, S., Hager, G.D., Corke, P.I.: A tutorial on visual servo control. Robot. Autom. IEEE Trans. 12(5), 651–670 (1996)

    Article  Google Scholar 

  4. Kelly, R., Carelli, R.: Stable visual servoing of camera-in-hand robotic systems. IEEE/ASME Trans. Mechatron. 5(1), 39–48 (2000)

    Article  Google Scholar 

  5. Dhome, M., Richetin, M.: Determinition of the attitude of 3D objects from a single perspective view. IEEE Trans. Pattern Anal. Mach. Intell. 11(12), 1265–1278 (1989)

    Article  Google Scholar 

  6. Horaud, R., Conio, B., Leboulleux, O., Lacolle, B.: An analytic solution for the perspective 4-point problem. Comput. Vis. Graph. Image Process. 47, 33–44 (1989)

    Article  Google Scholar 

  7. Rosin, P.L.: Robust pose estimation. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 29(2), 297–303 (1999)

    Article  Google Scholar 

  8. Marchand, E., Chaumette, F.: Virtual visual servoing: a framework for real-time augmented reality. In: Eurographics02, Saarebrucken, Germany, September 2002

  9. Tahri, O., Chaumette, F.: Complex objects pose estimation based on image moment invariants. In: IEEE International Conference on Robotics and Automation, pp. 438–443, Barcelona, Spain, April 2005

  10. DeMenthon, D.F., Davis, L.S.: Model-based object pose in 25 lines of code. Int. J. Comput. Vis. 15(1–2), 123–141 (1995)

    Article  Google Scholar 

  11. Fischler, M.A. Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. Assoc. Comput. Mach. 24(6), 381–395 (1981)

    MathSciNet  Google Scholar 

  12. Horaud, R., Dornaika, F., Lamiroy, B., Christy, S.: Object pose: The link between weak perspective, paraperspective, and full perspective. Int. J. Comput. Vis. 22(2), 173–189 (1997)

    Article  Google Scholar 

  13. Lowe, D.: Fitting parameterized three-dimensional models to images. IEEE Trans. Pattern Anal. Mach. Intell. 213(5), 441–450 (1991)

    Article  Google Scholar 

  14. Arun, K.S., Huang, T.S., Blostein, S.D.: Least squares fitting of two 3-D point sets. IEEE Trans. Pattern Anal. Mach. Intell. 9, 698–700 (1987)

    Article  Google Scholar 

  15. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Training models of shape from sets of examples. In: Proc. 2nd Br. Machine Vision Conf., pp. 9–18 (1992)

  16. Mukundan, R.: Estimation of quaternion parameters from two dimensional image moment. Graph. Model Image Process. 54(4), 345–350 (1992)

    Article  Google Scholar 

  17. Ansar, A., Daniilidis, K.: Linear pose estimation from points or lines. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 578–589 (2003)

    Article  Google Scholar 

  18. Dornaika, F., Garcia, C.: Pose estimation using point and line correspondences. Real-Time Imaging 5, 215–230 (1999)

    Article  Google Scholar 

  19. Wu, Y., Hu, Z.: PnP problem revisited. J. Math. Imaging Vis. 24, 131–141 (2006)

    Article  MathSciNet  Google Scholar 

  20. Basri, R., Jacobs, D.W.: Recognition using region correspondences. Int. J. Comput. Vis. 25(2), 145–166 (1997)

    Article  Google Scholar 

  21. Lu, C., Hager, G., Mjolsness, E.: Fast and globally convergent pose estimation from video images. IEEE Trans. Pattern Anal. Mach. Intell. 22(6), 610–622 (2000)

    Article  Google Scholar 

  22. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 20, 91–110 (2003)

    Google Scholar 

  23. Ma, Y., Košecká, J., Sastry, S.: Optimization criteria and geometric algorithms for motion and structure estimation. Int. J. Comput. Vis. 44(3), 219–249 (2001)

    Article  MATH  Google Scholar 

  24. Navab, N., Genc, Y., Appel, M.: Lines in one orthographic and two perspective views. IEEE Trans. Pattern Anal. Mach. Intell. 25(7), 912–917 (2003)

    Article  Google Scholar 

  25. Farid, H., Kosecka, J.: Estimating planar surface orientation using bi-spectral analysis. IEEE Trans. Image Process. 16(8), 2154–2160 (2007)

    Article  MathSciNet  Google Scholar 

  26. Wertz, J.R.: Spacecraft Attitude Determination and Control, 1st edn. Reidel, Dordrecht (1980)

    Google Scholar 

  27. Vidal, R.: Multi-subspace methods for motion segmentation from affine, perspective, and central panoramic cameras. In: IEEE International Conference on Robotics and Automation, pp. 1228–1233, Barcelona, Spain, April 2005

  28. Boyd, S., EI Ghaoul, L., Feron, E.: Linear matrix inequalities in system and control theory. In: Studies in Applied Mathematics (SIAM) (1994)

  29. Boyd, S.: Lieven Vandenberghe. Convex Optimization. Cambridge University Press, Cambridge (2004)

    Google Scholar 

  30. Jacobs, D., Basri, R.: 3-D to 2-D pose determination with regions. Int. J. Comput. Vis. 34(2–3), 123–145 (1999)

    Article  Google Scholar 

  31. Kvasnica, M., Grieder, P., Baotic, M., Christophersen, F.J.: Multi-Parametric Toolbox (MPT). Swiss Federal Institute of Technology, 11 December 2006

  32. Kurzhanskiy, A.A., Varaiya, P.: Ellipsoidal Toolbox, 2006–2007

  33. Mikolajczyk, K., Tuytelaars, T., Schmid, C.: A comparison of affine region detectors. Int. J. Comput. Vis. 65(1/2), 43–72 (2005)

    Article  Google Scholar 

  34. Craig, J.J.: Introduction to Robotics: Mechanics and Control. Addison-Wesley, Reading (1986)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of the Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY, 82070, USA

    John E. McInroy

  2. Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY, 82070, USA

    Zhen Qi

Authors
  1. John E. McInroy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhen Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to John E. McInroy.

Rights and permissions

Reprints and permissions

About this article

Cite this article

McInroy, J.E., Qi, Z. A Novel Pose Estimation Algorithm Based on Points to Regions Correspondence. J Math Imaging Vis 30, 195–207 (2008). https://doi.org/10.1007/s10851-007-0045-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10851-007-0045-2

Keywords

Search

Navigation