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Link to original content: https://dx.doi.org/10.1007/s10846-011-9594-0
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Virtual 3D City Model for Navigation in Urban Areas

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Abstract

In this paper, we propose to study the integration of a new source of a priori information, which is the virtual 3D city model. We study this integration for two tasks: vehicles geo-localization and obstacles detection. A virtual 3D city model is a realistic representation of the evolution environment of a vehicle. It is a database of geographical and textured 3D data. We describe an ego-localization method that combines measurements of a GPS (Global Positioning System) receiver, odometers, a gyrometer, a video camera and a virtual 3D city model. GPS is often consider as the main sensor for localization of vehicles. But, in urban areas, GPS is not precise or even can be unavailable. So, GPS data are fused with odometers and gyrometer measurements using an Unscented Kalman Filter (UKF). However, during long GPS unavailability, localization with only odometers and gyrometer drift. Thus, we propose a new observation of the location of the vehicle. This observation is based on the matching between the current image acquired by an on-board camera and the virtual 3D city model of the environment. We also propose an obstacle detection method based on the comparison between the image acquired by the on-board camera and the image extracted from the 3D model. The following principle is used: the image acquired by the on-board camera contains the possible dynamic obstacles whereas they are absent from the 3D model. The two proposed concepts are tested on real data.

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References

  1. Georgiev, A., Allen P.K.: Localization methods for a mobile robot in urban environments. IEEE Trans. Robot. 20(5), 851–864 (2004)

    Article  Google Scholar 

  2. Comport, A.I., Malis, E., Rives P.: Accurate quadrifocal tracking for robust 3d visual odometry. In: IEEE International Conference on Robotics and Automation 2007, pp. 40–45 (2007)

  3. Comport, A.I., Marchand, E., Pressigout, M., Chaumette, F.: Real-time markerless tracking for augmented reality: the virtual visual servoing framework. IEEE Trans. Vis. Comput. Graph. 12(4), 615–628 (2006)

    Article  Google Scholar 

  4. Davison, A.J.: Real-time simultaneous localisation and mapping with a single camera. In: Proceedings of the Ninth IEEE International Conference on Computer Vision ICCV ’03, pp. 1403 (2003)

  5. Fouque, C., Bonnifait, P.: Vehicle localization in urban canyons using geo-reference data and few GNSS-satellites. In: 6th IFAC Symposium on Intelligent Autonomous Vehicles, 3–5 Sept 2007

  6. Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–151 (1988)

  7. Laugier, C., Vasquez, D., Yguel, M., Fraichard, T., Aycard, O.: Geometric and Bayesian models for safe navigation in dynamic environments. Intelligent Service Robotics 1, 51–72 (2008)

    Article  Google Scholar 

  8. Wang, C.-C., Thorpe, C.E., Thrun, S., Hebert, M., Durrant-Whyte, H.F.: Simultaneous localization, mapping and moving object tracking. Int. J. Rob. Res. 26, 889–916 (2007). doi:10.1177/0278364907081229

    Article  Google Scholar 

  9. Wang, C.M.: Location estimation and uncertainty analysis for mobile robots. In: Autonomous Robot Vehicles, pp. 90–95 (1990)

  10. Bernstein, D., Kornhauser, A.: Map matching for personal navigation assistants. In: 77th Annual Meeting, The Transport Research Board, 11–15 January 1988

  11. Nister, D., Naroditsky, O., Bergen, J.: Visual odometry for ground vehicle applications. J. Field Rob. 23, 2006 (2006)

    Google Scholar 

  12. Mouragnon, E., Lhuillier, M., Dhome, M., Dekeyser, F., Sayd, P.: Real time localization and 3d reconstruction. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR ’06, pp. 363–370 (2006)

  13. Royer, E., Bom, J., Dhome, M., Thuillot, B., Lhuillier, M., Marmoiton, F.: Outdoor autonomous navigation using monocular vision. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3395–3400 (2005)

  14. Wan, E., Van der Merwe, R.: The unscented kalman filter for nonlinear estimation. In: IEEE Symposium on Adaptive Systems for Signal Processing, Communication and Control (2000)

  15. Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson, J., Karlsson, R., Nordlund, P.: Particle filters for positioning, navigation, and tracking. IEEE Trans. Signal Process. 50, 425–437 (2002)

    Article  Google Scholar 

  16. Welch, G., Bishop, G.: An Introduction to the Kalman Filter. University of North Carolina, Chapell Hill, TR 95-041 (2002)

  17. Nieto, J., Bailey, T., Nebot, E.: Recursive scan-matching slam. IEEE Trans. Signal Process. 55, 39–49 (2007)

    Google Scholar 

  18. Tardif, J.-P., Pavlidis, Y., Daniilidis, K.: Monocular visual odometry in urban environments using an omnidirectional camera. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008, pp. 2531–2538 (2008)

  19. Kim K., Chalidabhongse T.H., Harwood D., Davis L. Real-time foreground-background segmentation using codebook model. Real-time Imaging, 11(3):172–185, 2005.

    Article  Google Scholar 

  20. Clemente, L., Davison, A., Reid, I., Neira, J., Tardós, J.D.: Mapping large loops with a single hand-held camera. In: Proc. Robotics: Science and Systems Conference (2007)

  21. Agrawal, M., Konolige, K.: Real-time localization in outdoor environments using stereo vision and inexpensive gps. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 3, pp. 1063–1068 (2006)

  22. Dodge, M., Doyle, S., Smith, A., Fleetwood, S.: Towards the virtual city: VR & internet GIS for urban planning. In: Virtual Reality and Geographical Information Systems Workshop (1998)

  23. El Badaoui El Najjar, M., Bonnifait, P.: A road-matching method for precise vehicle localization using Kalman filtering and belief theory. J. Auton. Robots 19(2), 173–191 (2005)

    Article  Google Scholar 

  24. Fishler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. Assoc. Comput. Mach. 24(6), 381–395 (1981)

    Google Scholar 

  25. Quddus, M.A., Noland, R.B., Ochieng, W.Y.: A high accuracy fuzzy logic based map matching algorithm for road transport. J. Intell. Transp. Syst. 10(3), 103–115 (2006)

    MATH  Google Scholar 

  26. Quddus, M.A., Ochieng, W.Y., Noland, R.B.: Current map-matching algorithms for transport applications: state-of-the-art and future research directions. In: Transportation Research C: Emerging Technologies, pp. 312–328 (2007)

  27. Grewal, M.S., Weill, L.R., Andrews, A.P.: Global Positioning Systems, Inertial Navigation and Integration. Wiley, ISBN 0471-35032-X (2001)

  28. Bonnifait, P., Bouron, P., Crubille, P., Meizel, D.: Data fusion of four abs sensors and gps for an enhanced localization of car-like vehicles. IEEE International Conference on Robotics and Automation, 2001. Proceedings 2001 ICRA, vol. 2, pp. 1597–1602 (2001)

  29. Lothe, P., Bourgeois, S., Dekeyser, F., Royer, E., Dhome, M.: Towards geographical referencing of monocular slam reconstruction using 3d city models: application to real-time accurate vision-based localization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR’09) (2009)

  30. Bonnifait, P., Jabbour, M., Cherfaoui, V.: Autonomous navigation in urban areas using GIS-managed information. In: Special Issue in the International Journal of Vehicle Autonomous Systems on Advances in Autonomous Vehicles and Intelligent Transportation, vol. 6(1/2), pp. 84–103 (2008)

  31. Laurini, R., Servigne, S.: Panorama des potentialités SIG en 3 dimensions: vers des modèles virtuels 3D de villes. Revue XYZ 114, 18–22 (2008)

    Google Scholar 

  32. Talluri, R., Aggarwal, J.K.: Mobile robot self-location using model-image feature correspondence. IEEE Trans. Robot. Autom. 12(1), 63–77 (1996)

    Article  Google Scholar 

  33. Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME, J. Basic Eng. 82, 35–45 (1960)

    Article  Google Scholar 

  34. Julier, S., Uhlmann, J.: A new extension of the Kalman filter to nonlinear systems. In: International Symposium on Aerospace/Defense Sensing, Simulation and Controls (1997)

  35. Renault, S., LeMeur, A., Meizel, D.: GPS/GIS localization for management of vision referenced navigation in urban environments. In: 8th International IEEE Conference on Intelligent Transportation Systems, 13–15 Sept 2005

  36. Rodriguez, S., Fremont, V., Bonnifait, P.: An experiment of a 3d real-time robust visual odometry for intelligent vehicles. In: 12th International IEEE Conference on Intelligent Transportation Systems, ITSC ’09, pp. 1–6 (2009)

  37. Thrun, S., Liu, Y.: Multi-Robot Slam with Sparse Extended Information Filters. Springer (2003)

  38. Miyasaka, T., Ohama, Y., Ninomiya, Y.: Ego-motion estimation and moving object tracking using multi-layer lidar. In: IEEE Intelligent Vehicles Symposium, pp. 151–156 (2009)

  39. Trung-Dung, V., Aycard, O., Appenrodt, N.: Online localization and mapping with moving object tracking in dynamic outdoor environments. In: IEEE Intelligent Vehicles Symposium, pp. 190–195 (2007)

  40. Takase, Y., Sho, N., Sone, A., Shimiya, K.: Generation of digital city model. J. Viz. Soc. Jpn. 23(88), 21–27 (2003)

    Google Scholar 

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Authors and Affiliations

  1. Laboratoire Systèmes et Transports (SeT), Université de Technologie de Belfort-Montbéliard (UTBM), Belfort, France

    Cindy Cappelle

  2. Laboratoire d’Automatique, Génie Informatique et Signal (LAGIS), Université Lille Nord de France, Lille, France

    Maan E. El Najjar & Denis Pomorski

  3. Laboratoire Lorrain d’Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et Automatique (INRIA), Nancy, France

    François Charpillet

Authors
  1. Cindy Cappelle
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  2. Maan E. El Najjar
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  3. François Charpillet
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  4. Denis Pomorski
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Correspondence to Cindy Cappelle.

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Cappelle, C., El Najjar, M.E., Charpillet, F. et al. Virtual 3D City Model for Navigation in Urban Areas. J Intell Robot Syst 66, 377–399 (2012). https://doi.org/10.1007/s10846-011-9594-0

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