Advertisement

Determining an Initial Image Pair for Fixing the Scale of a 3D Reconstruction from an Image Sequence

  • Christian Beder
  • Richard Steffen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)

Abstract

Algorithms for metric 3d reconstruction of scenes from calibrated image sequences always require an initialization phase for fixing the scale of the reconstruction. Usually this is done by selecting two frames from the sequence and fixing the length of their base-line. In this paper a quality measure, that is based on the uncertainty of the reconstructed scene points, for the selection of such a stable image pair is proposed. Based on this quality measure a fully automatic initialization phase for simultaneous localization and mapping algorithms is derived. The proposed algorithm runs in real-time and some results for synthetic as well as real image sequences are shown.

Keywords

Covariance Matrice Image Pair Image Point Simultaneous Localization Epipolar Geometry 
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.
    Beardsley, P.A., Torr, P.H.S., Zisserman, A.: 3d model acquisition from extended image sequences. In: Proc. of ECCV (2), pp. 683–695 (1996)Google Scholar
  2. 2.
    Criminisi, A., Reid, I., Zisserman, A.: A plane measuring device. Image and Vision Computing 17(8), 625–634 (1999)CrossRefGoogle Scholar
  3. 3.
    Davison, A.J., Murray, D.W.: Simultaneous localisation and map-building using active vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 865–880 (2002)CrossRefGoogle Scholar
  4. 4.
    Davison, A.J.: Real-time simultaneous localisation and mapping with a single camera. In: Proc. International Conference on Computer Vision, Nice, pp. 1403–1410 (October 2003)Google Scholar
  5. 5.
    Diebel, J., Reuterswärd, K., Thrun, S., Davis, J., Gupta, R.: Simultaneous localization and mapping with active stereo vision. In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3436–3443 (2004)Google Scholar
  6. 6.
    Förstner, W.: A framework for low level feature extraction. In: Proc. of European Conference on Computer Vision, pp. 383–394 (1994)Google Scholar
  7. 7.
    Harris, C.G., Stephens, M.J.: A combined corner and edge detector. In: Fourth Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  8. 8.
    Heuel, S., Förstner, W.: Matching, reconstructing and grouping 3d lines from multiple views using uncertain projective geometry. In: CVPR 2001. IEEE, Los Alamitos (2001)Google Scholar
  9. 9.
    Heuel, S.: Uncertain Projective Geometry. LNCS, vol. 3008. Springer, Heidelberg (2004)MATHCrossRefGoogle Scholar
  10. 10.
    Kanatani, K.: Uncertainty modeling and model selection for geometric inference. IEEE Transaction on Pattern Analysis and Machine Intelligence 26(10), 1307–1319 (2004)CrossRefGoogle Scholar
  11. 11.
    Kanatani, K., Morris, D.D.: Gauges and gauge transformations for uncertainty description of geometric structure with indeterminacy. IEEE Transactions on Information Theory 47(5), 2017–2028 (2001)MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Koch, R., Pollefeys, M., Van Gool, L.: Robust calibration and 3d geometric modeling from large collections of uncalibrated images. In: Förstner, W., Buhmann, J., Faber, A., Faber, P. (eds.) Proceedings of the DAGM, Informatik Aktuell, pp. 412–420. Springer, Heidelberg (1999)Google Scholar
  13. 13.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  14. 14.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: BMVC, pp. 384–393 (2002)Google Scholar
  15. 15.
    Meltzer, J., Gupta, R., Ming-Hsuan, Y., Soatto, S.: Simultaneous localization and mapping using multiple view feature descriptors. In: Proc. of IROS (2004)Google Scholar
  16. 16.
    Nistér, D.: Frame decimation for structure and motion. In: Pollefeys, M., Van Gool, L., Zisserman, A., Fitzgibbon, A.W. (eds.) SMILE 2000. LNCS, vol. 2018, pp. 17–34. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  17. 17.
    Nistér, D.: An efficient solution to the five-point relative pose problem. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(6), 756–777 (2004)CrossRefGoogle Scholar
  18. 18.
    Pollefeys, M., Koch, R., Vergauwen, M., Van Gool, L.: Automated reconstruction of 3d scenes from sequences of images. ISPRS Journal of Photogrammetry and Remote Sensing 55(4), 251–267 (2000)CrossRefGoogle Scholar
  19. 19.
    Pollefeys, M., Van Gool, L.: A stratified approach to metric self-calibration. In: Proc. CVPR, pp. 407–412 (1997)Google Scholar
  20. 20.
    Pollefeys, M., Van Gool, L.: Stratified self-calibration with the modulus constraint. IEEE Trans. on Pattern Analysis and Machine Intelligence 21(8), 707–724 (1999)CrossRefGoogle Scholar
  21. 21.
    Pollefeys, M., Van Gool, L., Vergauwen, M., Cornelis, K., Verbiest, F., Tops, J.: Video-to-3d. In: Proceedings of Photogrammetric Computer Vision (2002)Google Scholar
  22. 22.
    Repko, J., Pollefeys, M.: 3d models from extended uncalibrated video sequences: Addressing key-frame selection and projective drift. In: Proc. of 3DIM (2005)Google Scholar
  23. 23.
    Se, S., Lowe, D.G., Little, J.J.: Vision-Based Global Localization and Mapping for Mobile Robots. IEEE Transactions on Robotics 21(3), 364–375 (2005)CrossRefGoogle Scholar
  24. 24.
    Shi, J., Tomasi, C.: Good features to track. In: 1994 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 1994), pp. 593–600 (1994)Google Scholar
  25. 25.
    Stewenius, H., Engels, C., Nister, D.: Recent developments on direct relative orientation. ISPRS Journal (to appear)Google Scholar
  26. 26.
    Thormählen, T., Broszio, H., Weissenfeld, A.: Keyframe selection for camera motion and structure estimation from multiple views. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 523–535. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  27. 27.
    Thrun, S., Montemerlo, M., Koller, D., Wegbreit, B., Nieto, J., Nebot, E.: Fastslam: An efficient solution to the simultaneous localization and mapping problem with unknown data association. Journal of Machine Learning Research (to appear)Google Scholar
  28. 28.
    Torr, P.H.S.: Bayesian model estimation and selection for epipolar geometry and generic manifold fitting. International Journal of Computer Vision 50(1), 35–61 (2002)MATHCrossRefGoogle Scholar
  29. 29.
    Torr, P., Zisserman, A.: Feature based methods for structure and motion estimation. In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) ICCV-WS 1999. LNCS, vol. 1883, pp. 278–294. Springer, Heidelberg (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Christian Beder
    • 1
  • Richard Steffen
    • 1
  1. 1.Institute for PhotogrammetryBonn UniversityGermany

Personalised recommendations