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A Hybrid Bayesian Framework for Map Matching: Formulation Using Switching Kalman Filter

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Abstract

This paper addresses an important issue for intelligent transportation system, namely the ability of vehicles to safely and reliably localize themselves within an a priori known road map network. For this purpose, we propose an approach based on hybrid dynamic bayesian networks enabling to implement in a unified framework two of the most successful families of probabilistic model commonly used for localization: linear Kalman filters and Hidden Markov Models. The combination of these two models enables to manage and manipulate multi-hypotheses and multi-modality of observations characterizing Map Matching problems and it improves integrity approach. Another contribution of the paper is a chained-form state space representation of vehicle evolution which permits to deal with non-linearity of the used odometry model. Experimental results, using data from encoders’ sensors, a DGPS receiver and an accurate digital roadmap, illustrate the performance of this approach, especially in ambiguous situations.

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References

  1. Abbott, E., Powell, D.: Land-vehicle navigation using GPS. Proc. IEEE 87(1), 145–162 (1999)

    Article  Google Scholar 

  2. Bar-Shalom, Y., Li, X.R.: Estimation and Tracking: Principles, Techniques, and Software. Artech House Inc, Boston (1993)

    MATH  Google Scholar 

  3. Bernstein, D., Kornhauser, A.: Map matching for personal navigation assistants. In: 77th Annual Meeting. The Transport Research Board, Washinton DC (1993)

  4. Bonnifait, P., Jabbour, M., Cherfaoui, V.: Autonomous navigation in urban areas using GIS-managed information. Int. J. Veh. Auton. Syst. Adv. Autonom. Vehicle Intell. Transport. (Special Issue) 6(1/2), 84–103 (2008)

  5. Castillo, E., José, M.G., Ali, S.H.: Expert Systems and Probabilistic Network Models. Springer, New York Berlin Heidelberg (1997)

    Book  Google Scholar 

  6. Cowell, R.G., Dawid, A.P.: Probabilistic Networks and Expert System. Springer, New York (1999)

    Google Scholar 

  7. Cox, I.J., Leonard, J.J.: Modeling a dynamic environment using a multiple hypothesis approach. Artif. Intell. 66(1), 311–344 (1994)

    Article  MATH  Google Scholar 

  8. De Luca, A.D., Oriolo, G., Vendittelli, M.: Control of Wheeled Mobile Robots: An Experimental Overview, vol. 270/2001, pp. 181–226. Springer, Berlin/Heidelberg (2001)

  9. Dissanayake, M.W.M.G., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A solution to the simultaneous localization and map building (SLAM) problem. IEEE Trans. Robot. Autom. 17(3), 229–241 (2001)

    Article  Google Scholar 

  10. Doucet, A.: On sequential simulation-based methods for Bayesian filtering. Technical Report CUED/F-INFENG/TR.310, Department of Engineering, Cambridge University (1998)

  11. El Najjar, M.E., et Bonnifait, Ph.: A road-matching method for precise vehicle localization using Kalman filtering and belief theory. In: Journal of Autonomous Robots, S.I. on Robotics Technologies for Intelligent Vehicles. Kluwer Academic Publishers (2005)

  12. Fabrizi, E., Oriolo, G., Panzieri, S., Ulivi, G.: A KF-based localization algorithm for nonholonomic mobile robots. In: 6th IEEE Mediterranean Conference on Control and Automation, Alghero, I (1998)

  13. Greenfeld, J.: Matching GPS observations to locations on a digital map. In: 81th Annual Meeting of the Transportation Research Board, Washington, DC (2002)

  14. Gustafsson, E., Bergman, N., Forsell, U.: Particles filters for positioning, navigation and tracking. In: IEEE Trans. on Signal Processing. Special issue on Monte Carlo Methods for Statistical Signal Processing, vol. 50 (2002)

  15. Isidori, A.: Nonlinear Control Systems, 2nd edn. Springer (1989)

  16. Jensfelt, P., Kristensen, S.: Active global localisation for a mobile robot using multiple hypothesis tracking. IEEE Trans. Robot. Autom. 17(5), 748–760 (2001)

    Article  Google Scholar 

  17. Kim, S., Kim, J.H.: Adaptive fuzzy-network-based C-measure map-matching algorithm for car navigation system. Proc. IEEE Trans. Ind. Electron. 48(2), 432–441 (2001)

    Article  Google Scholar 

  18. Kim, J.S., Lee, J.H., Kang, T.H., Lee, W.Y., Kim, Y.G.: Node based map matching algorithm for car navigation system. In: Proc. 29th ISATA Symp. Florence, vol. 10, pp. 121–126 (1996)

  19. Murphy, K.P.: Dynamic Bayesian networks: representation, inference and learning. Ph.D. thesis, UC Berkley, Computer Science Division (2002)

  20. Ochieng, W.Y., Quddus, M.A., Noland, R.B.: Map matching in complex urban road networks. Braz. J. Cartogr. (Rev. Bras. Cartogr.) 55(2), 1–18 (2004)

    Google Scholar 

  21. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 2nd edn. Publishers, Inc, San Mateo (1988)

    Google Scholar 

  22. Pyo, J.S., Shin, D.H., Sung, T.K.: Development of a map matching method using the multiple hypothesis technique. In: Proc. IEEE conf. Intelligent Transportation Systems, pp. 23–27 (2001)

  23. Quddus, M.A., Ochieng, W.Y., Noland, R.B.: Review of map-matching algorithms for ITS applications. In: 07–2216, 86th Annual Meeting of the Transportation Research Board, 25 Jan. Washington, DC, U.S.A. (2007)

  24. Quddus, A.M., Ochieng, W.Y., Zhao, L., Noland, R.B.: A general map matching algorithm for transport telematics applications. GPS Solutions 7(3), 157–167 (2003)

    Article  Google Scholar 

  25. Samson, C.: Control of chained systems: application to path following and time-varying point stabilization of mobile robot. IEEE Trans. Autom. Control 40(1), 64–77 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  26. Smaili, C., El Najjar, M.E., Charpillet, F.: A road matching method for precise vehicle localization using hybrid Bayesian network. J. Intell. Transp. Syst. 12(4), 176–188 (2008)

    Article  MATH  Google Scholar 

  27. Syed, S., Cannon, M.E.: Fuzzy logic based-map matching algorithm for vehicle navigation system in urban canyons. In: Proc. of the ION National Technical Meeting, San Diego, CA, pp. 982–993 (2004)

  28. Taylor, G., Blewitt, G.: Road reduction filtering using GPS. In: 3th AGILE Conference on Geographic Information Science, Helsinki, pp. 114–120 (2000)

  29. Thrun, S., Fox, D., Burgard, W., Dellaert, F.: Robust Monte-Carlo localisation for mobile robots. J. Art. Intell. (AI) 128(1–2), 99–141 (2001)

    Article  MATH  Google Scholar 

  30. Uri, L.: Hybrid bayesian networks for reasoning about complex systems. Ph.D. thesis, Stanford University, Department of Computer Science (2002)

  31. Wan, E.A., Van der Merwe, R.: The unscented Kalman filter for nonlinear estimation. In: Proc. IEEE Symp. Adaptive Systems for Signal Proc. Comm. and Control (AS-SPCC), pp. 153–158. Lake Louise, Alberta, Canada (2000)

  32. Welch, G., Bishop, G.: An introduction to the Kalman filter. Technical Report TR 95–041, University of North Carolina, Department of Computer Science (2004)

  33. Zhao, Y.: Vehicle Location Navigation Systems. Artech House Inc (1997)

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Authors and Affiliations

  1. Département Informatique de l’université U.S.T.H.B, 32 El Alia, Bab Ezzouar Alger, 16111, Algerie, France

    Cherif Smaili

  2. LAGIS-CNRS UMR 8219 Université de Lille 1, Avenue Paul Langevevin, 59650, Villeneuve D’Ascq Cedex, France

    Maan El Badaoui El Najjar

  3. MAIA Team, INRIA Nancy Grand EST, Loria Campus Scientifique, BP 239, 54506, Vandoeuvre-lès-Nancy, France

    François Charpillet

Authors
  1. Cherif Smaili
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  2. Maan El Badaoui El Najjar
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  3. François Charpillet
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Correspondence to Maan El Badaoui El Najjar.

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Smaili, C., Najjar, M.E.B.E. & Charpillet, F. A Hybrid Bayesian Framework for Map Matching: Formulation Using Switching Kalman Filter. J Intell Robot Syst 74, 725–743 (2014). https://doi.org/10.1007/s10846-013-9844-4

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  • DOI: https://doi.org/10.1007/s10846-013-9844-4

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