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Fast Techniques for Monocular Visual Odometry

  • M. Hossein Mirabdollah
  • Bärbel Mertsching
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9358)

Abstract

In this paper, fast techniques are proposed to achieve real time and robust monocular visual odometry. We apply an iterative 5-point method to estimate instantaneous camera motion parameters in the context of a RANSAC algorithm to cope with outliers efficiently. In our method, landmarks are localized in space using a probabilistic triangulation method utilized to enhance the estimation of the last camera pose. The enhancement is performed by multiple observations of landmarks and minimization of a cost function consisting of epipolar geometry constraints for far landmarks and projective constraints for close landmarks. The performance of the proposed method is demonstrated through application to the challenging KITTI visual odometry dataset.

References

  1. 1.
  2. 2.
    Bradski, G.: OpenCv library. Dr. Dobb’s J. Softw. Tools 25(11), 120–126 (2000)Google Scholar
  3. 3.
    Civera, J., Davison, A., Montiel, J.: Inverse depth parametrization for monocular SLAM. IEEE Trans. Robot. 24(5), 932–945 (2008)CrossRefGoogle Scholar
  4. 4.
    Fischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Geiger, A., Ziegler, J., Stiller, C.: StereoScan: Dense 3d reconstruction in real-time. In: Proceeding of Intelligent Vehicles Symposium (2011)Google Scholar
  6. 6.
    Hartley, R.: In defense of the eight-point algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 19(6), 580–593 (1997)CrossRefGoogle Scholar
  7. 7.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004). (ISBN: 0521540518)Google Scholar
  8. 8.
    Hedborg, B., Felsberg, M.: Fast iterative five point relative pose estimation. In: Proceeding of IEEE Workshopp on Robot Vision, pp. 60–67 (2013)Google Scholar
  9. 9.
    Kwok, N.M., Dissanayake, G., Ha, Q.: Bearing-only slam using a SPRT based Gaussian sum filter. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 1109–1114 (2006)Google Scholar
  10. 10.
    Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)Google Scholar
  11. 11.
    Mirabdollah, M.H., Mertsching, B.: On the second order statistics of essential matrix elements. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 547–557. Springer, Heidelberg (2014) Google Scholar
  12. 12.
    Mur-Artal, R., Tardos, J.: ORB-SLAM: tracking and mapping recognizable features. In: Proceeding of Robotics: Science and Systems (RSS) Workshop on Multi View Geometry in Robotics (2014)Google Scholar
  13. 13.
    Nistér, D.: An Efficient solution to the five-point relative pose problem. IEEE Trans. Pattern Anal. Mach. Intell. 26(6), 756–777 (2004)CrossRefGoogle Scholar
  14. 14.
    Shi, J., Tomasi, C.: Good features to track. Technical report (1993)Google Scholar
  15. 15.
    Solà, J., Vidal-Calleja, T., Civera, J., Montiel, L.M.: Impact of landmark parametrization on monocular EKF-SLAM with points and lines. Int. J. Comput. Vis. 97(3), 339–368 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Sola, J., Monin, A., Devy, M., Lemaire, T.: Undelayed initialization in bearing-only SLAM. In: Proceedings IEEE International Conference on Intelligent Robots and Systems, pp. 2499–2504 (2005)Google Scholar
  17. 17.
    Song, S., Chandraker, M.: Robust scale estimation in real-time monocular SFM for autonomous driving. In: Proceeding of Computer Vision and Pattern Recognition (2014)Google Scholar
  18. 18.
    Song, S., Chandraker, M., Guest, C.: Parallel, real-time monocular visual odometry. In: Proceeding of IEEE International Conference on Robotics and Automation, pp. 4698–4705 (2013)Google Scholar
  19. 19.
    Strasdat, H., Montiel, J.M.M., Davison, A.: Scale drift-aware large scale monocular SLAM. In: Proceedings of Robotics: Science and Systems (2010)Google Scholar
  20. 20.
    Zhao, L., Huang, S., Yan, L., Dissanayake, G.: Parallax angle parametrization for monocular SLAM. In: Proceeding of IEEE International Conference on Robotics and Automation, pp. 3117–3124 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  1. 1.GET LabUniversity of PaderbornPaderbornGermany

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