Tracking Camera Parameters of an Active Stereo Rig

  • Thao Dang
  • Christian Hoffmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


This contribution presents an approach for the continuous self-calibration of an active stereo rig with verging cameras. The proposed self-calibration recovers extrinsic parameters up to scale as well as the focal lengths of both cameras. Three different categories of constraint equations are evaluated and formulated as a Gauss-Helmert model for self-calibration: bundle adjustment with reduced parameter vector, the epipolar constraint, and the trilinear constraints. The optimization of the constraints is implemented as a robust Iterated Extended Kalman Filter that allows initial stereo calibration as well as continuous tracking of the camera parameters. The performance of the algorithm is demonstrated on synthetic and real imagery.


Camera Parameter Stereo Camera Bundle Adjustment Extrinsic Parameter World Coordinate System 
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  1. 1.
    Gehrig, S.K.: Large-field-of-view stereo for automotive applications. In: OmniVis 2005, Beijing (2005)Google Scholar
  2. 2.
    Bjorkman, M., Eklundh, J.: Real-time epipolar geometry estimation of binocular stereo heads. PAMI 24(3), 425–432 (2002)Google Scholar
  3. 3.
    Pettersson, N., Petersson, L.: Online stereo calibration using FPGAs. In: IEEE Intelligent Vehicles Symposium (2005)Google Scholar
  4. 4.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2002)Google Scholar
  5. 5.
    Förstner, W.: On weighting and choosing constraints for optimally reconstructing the geometry of image triplets. In: ECCV, vol. 2, pp. 669–684 (2000)Google Scholar
  6. 6.
    Longuet-Higgins, H.: A computer algorithm for reconstructing a scene from two projections. Nature 293, 133–135 (1981)CrossRefGoogle Scholar
  7. 7.
    Shashua, A.: Algebraic functions for recognition. PAMI 17(8), 779–789 (1995)Google Scholar
  8. 8.
    Hartley, R.: A linear method for reconstruction from lines and points. In: International Conference on Computer Vision, pp. 885–887 (1995)Google Scholar
  9. 9.
    Zhang, Z., Faugeras, O.: 3D Dynamic Scene Analysis. Springer, Heidelberg (1992)zbMATHGoogle Scholar
  10. 10.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  11. 11.
    Dang, T., Hoffmann, C.: Stereo calibration in vehicles. In: IEEE Intelligent Vehicles Symposium, Parma, Italy, pp. 268–273 (2004)Google Scholar
  12. 12.
    Hartley, R.I., Sturm, P.: Triangulation. Computer Vision and Image Understanding 68(2), 146–157 (1997)CrossRefGoogle Scholar
  13. 13.
    Hirschmüller, H., Innocent, P.R., Garibaldi, J.M.: Real-time correlation-based stereo vision with reduced border errors. IJCV 47(1-3), 229–246 (2002)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Thao Dang
    • 1
  • Christian Hoffmann
    • 1
  1. 1.Institut für Mess und RegelungstechnikUniversity of KarlsruheGermany

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