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

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Pattern Recognition (DAGM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5748))

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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. Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. Int. J. of Comp. Vis. 12(1), 43–77 (1994)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  3. Bruss, A.R., Horn, B.K.P.: Passive navigation. Comp. Vis., Graph., and Im. Proc. 21, 3–20 (1983)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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. Farnebaeck, G.: Polynomial expansion for orientation and motion estimation. PhD thesis, Dept. of Electrical Engineering, Linkoepings universitet (2002)

    Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  8. Gibson, J.J.: The Perception of the Visual World. Houghton Mifflin, Boston (1950)

    Google Scholar 

  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)

    Article  Google Scholar 

  10. Helmholtz, H.: Treatise on physiological optics. In: Southhall, J.P, (ed.) (1925)

    Google Scholar 

  11. Kanatani, K.: 3-d interpretation of optical-flow by renormalization. Int. J. of Comp. Vis. 11(3), 267–282 (1993)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. 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. 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)

    Article  Google Scholar 

  19. Torr, P.H.S.: Outlier Detection and Motion Segmentation. PhD thesis, Engineering Dept., University of Oxford (1995)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Neural Information Processing, University of Ulm, 89069, Ulm, Germany

    Florian Raudies & Heiko Neumann

Authors
  1. Florian Raudies
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  2. Heiko Neumann
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Editor information

Editors and Affiliations

  1. Digitale Bildverarbeitung, Universität Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany

    Joachim Denzler  & Herbert Süße  & 

  2. Fraunhofer-Institut für Angewandte Optik und Feinmechanik, Albert-Einstein-Str. 7, 07745, Jena, Germany

    Gunther Notni

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© 2009 Springer-Verlag Berlin Heidelberg

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Raudies, F., Neumann, H. (2009). An Efficient Linear Method for the Estimation of Ego-Motion from Optical Flow. In: Denzler, J., Notni, G., Süße, H. (eds) Pattern Recognition. DAGM 2009. Lecture Notes in Computer Science, vol 5748. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03798-6_2

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  • DOI: https://doi.org/10.1007/978-3-642-03798-6_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03797-9

  • Online ISBN: 978-3-642-03798-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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