Advertisement

Pose Estimation from Uncertain Omnidirectional Image Data Using Line-Plane Correspondences

  • Christian Gebken
  • Antti Tolvanen
  • Gerald Sommer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)

Abstract

Omnidirectional vision is highly beneficial for robot navigation. We present a novel perspective pose estimation for omnidirectional vision involving a parabolic central catadioptric sensor using line-plane correspondences. We incorporate an appropriate and approved stochastic method to deal with uncertainties in the data.

Keywords

Image Point Rigid Body Motion Geometric Algebra Parabolic Mirror Good Linear Unbiased Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aliaga, D.G.: Accurate catadioptric calibration for real-time pose estimation of room-size environments. In: International Conference on Computer Vision (ICCV), pp. 127–134 (2001)Google Scholar
  2. 2.
    Angles, P.: Construction de revêtements du groupe conforme d’un espace vectoriel muni d’une ≪métrique≫ de type (p, q). Ann. Inst. Henri Poincaré 33(1), 33–51 (1980)MATHMathSciNetGoogle Scholar
  3. 3.
    Ansar, A., Daniilidis, K.: Linear Pose Estimation from Points or Lines. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 282–296. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Cauchois, C., Brassart, E., Delahoche, L., Drocourt, C.: Spatial localization method with omnidirectional vision. In: 11th IEEE International Conference on Advanced Robotics (ICAR), Coimbra, Portugal, pp. 287–292 (2003)Google Scholar
  5. 5.
    Faugeras, O.: Three-Dimensional Computer Vision. MIT Press, Cambridge (1993)Google Scholar
  6. 6.
    Gaspar, J., Santos-Victor, J.: Vision-based navigation and enviromental representations with an omni-directional camera. IEEE Transactions on Robotics and Automation 16(6), 890–898 (2000)CrossRefGoogle Scholar
  7. 7.
    Geyer, C., Daniilidis, K.: Catadioptric projective geometry. International Journal of Computer Vision 45(3), 223–243 (2001)MATHCrossRefGoogle Scholar
  8. 8.
    Hestenes, D., Sobczyk, G.: Clifford Algebra to Geometric Calculus: A Unified Language for Mathematics and Physics. Reidel, Dordrecht (1984)MATHGoogle Scholar
  9. 9.
    Koch, K.-R.: Parameter Estimation and Hypothesis Testing in Linear Models. Springer, Heidelberg (1997)Google Scholar
  10. 10.
    Nayar, S.K., Peri, V.: Folded catadioptric cameras. In: Conference on Computer Vision and Pattern Recognition (CVPR), Ft. Collins, CO, USA, pp. 217–223 (1999)Google Scholar
  11. 11.
    Perwass, C., Gebken, C., Grest, D.: CLUCalc (2006), http://www.clucalc.info/
  12. 12.
    Perwass, C., Gebken, C., Sommer, G.: Estimation of Geometric Entities and Operators from Uncertain Data. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 459–467. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Rosenhahn, B., Sommer, G.: Pose estimation in conformal geometric algebra, part I: The stratification of mathematical spaces, part II: Real-time pose estimation using extended feature concepts. Journal of Mathematical Imaging and Vision 22, 27–70 (2005)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Tolvanen, A., Perwass, C., Sommer, G.: Projective Model for Central Catadioptric Cameras Using Clifford Algebra. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 192–199. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Christian Gebken
    • 1
  • Antti Tolvanen
    • 1
  • Gerald Sommer
    • 1
  1. 1.Institut für Informatik, CAU KielKielGermany

Personalised recommendations