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Observing Dynamic Urban Environment through Stereo-Vision Based Dynamic Occupancy Grid Mapping

  • You Li
  • Yassine Ruichek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)

Abstract

Occupancy grid maps are popular tools of representing surrounding environments for mobile robots/ intelligent vehicles. When moving in dynamic real world, traditional occupancy grid mapping is required not only to be able to detect occupied areas, but also to be able to understand the dynamic circumstance. The paper addresses this issue by presenting a stereo-vision based framework to create dynamic occupancy grid map, for the purpose of intelligent vehicle. In the proposed framework, a sparse feature points matching and a dense stereo matching are performed in parallel for each stereo image pair. The former process is used to analyze motions of the vehicle itself and also surrounding moving objects. The latter process calculates dense disparity image, as well as U-V disparity maps applied for pixel-wise moving objects segmentation and dynamic occupancy grid mapping. Principal advantage of the proposed framework is the ability of mapping occupied areas and moving objects at the same time. Meanwhile, compared with some existing methods, the stereo-vision based occupancy grid mapping algorithm is improved. The proposed method is verified in real datasets acquired by our platform SeT-Car.

Keywords

Occupancy grid map Moving objects egmentation U-V disparity 

References

  1. 1.
    Camera calibration toolbox for matlab (2012), http://www.vision.caltech.edu/bouguetj/calib_doc
  2. 2.
    Badino, H., Franke, U., Mester, R.: Free space computation using stochastic occupancy grids and dynamic. In: Programming, Proc. Intl Conf. Computer Vision, Workshop Dynamical Vision (2007)Google Scholar
  3. 3.
    Braillon, C., Pradalier, C., Usher, K., Crowley, J., Laugier, C.: Occupancy grids from stereo and optical flow data. In: Proc. of the Int. Symp. on Experimental Robotics (2006)Google Scholar
  4. 4.
    Danescu, R., Oniga, F., Nedevschi, S., Meinecke, M.M.: Tracking multiple objects using particle filters and digital elevation maps. In: IEEE Intelligent Vehicles Symposium, pp. 88–93 (2009)Google Scholar
  5. 5.
    Dey, S., Reilly, V., Saleemi, I., Shah, M.: Detection of independently moving objects in non-planar scenes via multi-frame monocular epipolar constraint. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 860–873. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Geiger, A., Ziegler, J., Stiller, C.: Stereoscan: Dense 3d reconstruction in real-time. In: IEEE Intelligent Vehicles Symposium. Baden-Baden, Germany (June 2011)Google Scholar
  7. 7.
    Hirschmueller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Analysis and Machine Intelligence 30, 328–341 (2008)CrossRefGoogle Scholar
  8. 8.
    Kang, J., Cohen, I., Yuan, C.: Detection and tracking of moving objects from a moving platform in presence of strong parallax. In: Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV) (2005)Google Scholar
  9. 9.
    Labayarde, R., Aubert, D., Tarel, J.P.: Real time obstacle detection in stereovision on non flat road geometry through “v-disparity” representation. In: IEEE Intelligent Vehicle Symposium, vol. 2, pp. 646–651 (2002)Google Scholar
  10. 10.
    Lenz, P., Ziegler, J., Geiger, A., Roser, M.: Sparse scene flow segmentation for moving object detection in urban environments. In: IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany, pp. 926–932 (2011)Google Scholar
  11. 11.
    Murray, D., Little, J.J.: Using real-time stereo vision for mobile robot navigation. Autonomous Robots 8, 161–171 (2000)CrossRefGoogle Scholar
  12. 12.
    Nguyen, T.N., Michaelis, B.: Al-Hamadi: Stereo-camera-based urban environment perception using occupancy grid and object tracking. IEEE Transactions on Intelligent Transportation Systems 13, 154–165 (2012)CrossRefGoogle Scholar
  13. 13.
    Perrollaz, M., John-David, Y., Anne, S., Laugier, C.: Using the disparity space to compute occupancy grids from stereo-vision. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (October 2010)Google Scholar
  14. 14.
    Kaucic, R., et al.: A unified framework for tracking through occlusions and across sensor gaps. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (2005)Google Scholar
  15. 15.
    Rabe, C.: Fast detection of moving objects in complex scenarios. In: IEEE Intelligent Vehicles Symposium (2007)Google Scholar
  16. 16.
    Sawhney, H.: Independent motion detection in 3d scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1191–1199 (2000)CrossRefGoogle Scholar
  17. 17.
    Soquet, N., Aubert, D., Hautiere, N.: Road segmentation supervised by an extended v-disparity algorithm for autonomous navigation. In: IEEE Intelligent Vehicle Symposium, pp. 160–165 (June 2007)Google Scholar
  18. 18.
    Thrun, S.: Learning Occupancy Grid Maps with Forward Sensor Models. Autonomous Robots 15, 111–127 (2003)CrossRefGoogle Scholar
  19. 19.
    Dung, V.T., Aycard, O.: Online localization and mapping with moving object tracking in dynamic outdoor environments. In: Proceedings of the IEEE Intelligent Vehicles Symposium (2007)Google Scholar
  20. 20.
    Wang, J., Hu, Z., Lu, H., Uchimura, K.: Motion detection in driving environment using U-V-disparity. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3851, pp. 307–316. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    Wedel, A., Meißner, A., Rabe, C., Franke, U., Cremers, D.: Detection and segmentation of independently moving objects from dense scene flow. In: Cremers, D., Boykov, Y., Blake, A., Schmidt, F.R. (eds.) EMMCVPR 2009. LNCS, vol. 5681, pp. 14–27. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • You Li
    • 1
  • Yassine Ruichek
    • 1
  1. 1.Institut de Recherche sur les Transports, l’Energie et la Société, le laboratoire Systèmes et Transport (IRTES-SET)Université de Technology of Belfort-MontbéliardBelfortFrance

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