An Efficient Linear Method for the Estimation of Ego-Motion from Optical Flow

  • Florian Raudies
  • Heiko Neumann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5748)

Abstract

Approaches to visual navigation, e.g. used in robotics, require computationally efficient, numerically stable, and robust methods for the estimation of ego-motion. One of the main problems for ego-motion estimation is the segregation of the translational and rotational component of ego-motion in order to utilize the translation component, e.g. for computing spatial navigation direction. Most of the existing methods solve this segregation task by means of formulating a nonlinear optimization problem. One exception is the subspace method, a well-known linear method, which applies a computationally high-cost singular value decomposition (SVD). In order to be computationally efficient a novel linear method for the segregation of translation and rotation is introduced. For robust estimation of ego-motion the new method is integrated into the Random Sample Consensus (RANSAC) algorithm. Different scenarios show perspectives of the new method compared to existing approaches.

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References

  1. 1.
    Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. Int. J. of Comp. Vis. 12(1), 43–77 (1994)CrossRefGoogle Scholar
  2. 2.
    Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Bruss, A.R., Horn, B.K.P.: Passive navigation. Comp. Vis., Graph., and Im. Proc. 21, 3–20 (1983)CrossRefGoogle Scholar
  4. 4.
    Chiuso, A., Brockett, R., Soatto, S.: Optimal structure from motion: Local ambiguities and global estimates. Int. J. of Comp. Vis. 39(3), 195–228 (2000)CrossRefMATHGoogle Scholar
  5. 5.
    Clauss, M., Bayerl, P., Neumann, H.: Segmentation of independently moving objects using a maximum-likelihood principle. In: Lafrenz, R., Avrutin, V., Levi, P., Schanz, M. (eds.) Autonome Mobile Systeme 2005, Informatik Aktuell, pp. 81–87. Springer, Berlin (2005)Google Scholar
  6. 6.
    Farnebaeck, G.: Polynomial expansion for orientation and motion estimation. PhD thesis, Dept. of Electrical Engineering, Linkoepings universitet (2002)Google Scholar
  7. 7.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. of the ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Gibson, J.J.: The Perception of the Visual World. Houghton Mifflin, Boston (1950)Google Scholar
  9. 9.
    Heeger, D.J., Jepson, A.D.: Subspace methods for recovering rigid motion i: Algorithm and implementation. Int. J. of Comp. Vis. 7(2), 95–117 (1992)CrossRefGoogle Scholar
  10. 10.
    Helmholtz, H.: Treatise on physiological optics. In: Southhall, J.P, (ed.) (1925)Google Scholar
  11. 11.
    Kanatani, K.: 3-d interpretation of optical-flow by renormalization. Int. J. of Comp. Vis. 11(3), 267–282 (1993)CrossRefGoogle Scholar
  12. 12.
    Lobo, N.V., Tsotsos, J.K.: Computing ego-motion and detecting independent motion from image motion using collinear points. Comp. Vis. and Img. Underst. 64(1), 21–52 (1996)CrossRefGoogle Scholar
  13. 13.
    Longuet-Higgins, H.C., Prazdny, K.: The interpretation of a moving retinal image. Proc. of the Royal Soc. of London. Series B, Biol. Sci. 208(1173), 385–397 (1980)CrossRefGoogle Scholar
  14. 14.
    MacLean, W.J.: Removal of translation bias when using subspace methods. IEEE Int. Conf. on Comp. Vis. 2, 753–758 (1999)Google Scholar
  15. 15.
    MacLean, W.J., Jepson, A.D., Frecker, R.C.: Recovery of egomotion and segmentation of independent object motion using the EM algorithm. Brit. Mach. Vis. Conf. 1, 175–184 (1994)Google Scholar
  16. 16.
    Pauwels, K., Van Hulle, M.M.: Segmenting independently moving objects from egomotion flow fields. In: Proc. of the Early Cognitive Vision Workshop (ECOVISION 2004), Isle of Skye, Scotland (2004)Google Scholar
  17. 17.
    Pauwels, K., Van Hulle, M.M.: Robust instantaneous rigid motion estimation. Proc. of Comp. Vis. and Pat. Rec. 2, 980–985 (2005)Google Scholar
  18. 18.
    Pauwels, K., Van Hulle, M.M.: Optimal instantaneous rigid motion estimation insensitive to local minima. Comp. Vis. and Im. Underst. 104(1), 77–86 (2006)CrossRefGoogle Scholar
  19. 19.
    Torr, P.H.S.: Outlier Detection and Motion Segmentation. PhD thesis, Engineering Dept., University of Oxford (1995)Google Scholar
  20. 20.
    Zhang, T., Tomasi, C.: Fast, robust, and consistent camera motion estimation. Proc. of Comp. Vis. and Pat. Rec. 1, 164–170 (1999)Google Scholar
  21. 21.
    Zhuang, X., Huang, T.S., Ahuja, N., Haralick, R.M.: A simplified linear optic flow-motion algorithm. Comp. Graph. and Img. Proc. 42, 334–344 (1988)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Florian Raudies
    • 1
  • Heiko Neumann
    • 1
  1. 1.Institute of Neural Information ProcessingUniversity of UlmUlmGermany

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