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Particle Filters for Location Estimation in Ubiquitous Computing: A Case Study

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UbiComp 2004: Ubiquitous Computing (UbiComp 2004)

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Abstract

Location estimation is an important part of many ubiquitous computing systems. Particle filters are simulation-based probabilistic approximations which the robotics community has shown to be effective for tracking robots’ positions. This paper presents a case study of applying particle filters to location estimation for ubiquitous computing. Using trace logs from a deployed multi-sensor location system, we show that particle filters can be as accurate as common deterministic algorithms. We also present performance results showing it is practical to run particle filters on devices ranging from high-end servers to handhelds. Finally, we discuss the general advantages of using probabilistic methods in location systems for ubiquitous computing, including the ability to fuse data from different sensor types and to provide probability distributions to higher-level services and applications. Based on this case study, we conclude that particle filters are a good choice to implement location estimation for ubiquitous computing.

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References

  1. Misra, P., Burke, B.P., Pratt, M.M.: GPS performance in navigation. Proceedings of the IEEE (Special Issue on GPS) 87, 65–85 (1999)

    Google Scholar 

  2. Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing 7, 275–286 (2003)

    Article  Google Scholar 

  3. Patterson, D.J., Liao, L., Fox, D., Kautz, H.A.: Inferring high-level behavior from low-level sensors. In: Proceedings of the Fifth International Conference on Ubiquitous Computing (Ubicomp 2003), pp. 73–89. Springer, Heidelberg (2003)

    Google Scholar 

  4. Want, R., Hopper, A., Falcao, V., Gibbons, J.: The active badge location system. ACM Transactions on Information Systems 10, 91–102 (1992)

    Article  Google Scholar 

  5. Want, R., Schilit, B., Adams, N., Gold, R., Petersen, K., Goldberg, D., Ellis, J., Weiser, M.: The parctab ubiquitous computing experiment. In: Imielinski, T. (ed.) Mobile Computing, pp. 45–101. Kluwer Publishing, Dordrecht (1997) ISBN 0-7923-9697-9

    Google Scholar 

  6. Priyantha, N.B., Chakraborty, A., Balakrishnan, H.: The cricket location-support system. In: Proceedings of the Sixth Annual ACM International Conference on Mobile Computing and Networking (MOBICOM), pp. 32–43. ACM Press, Boston (2000)

    Chapter  Google Scholar 

  7. Addlesee, M., Curwen, R., Hodges, S., Newman, J., Steggles, P., Ward, A., Hopper, A.: Implementing a sentient computing system. Computer 34, 50–56 (2001)

    Article  Google Scholar 

  8. Bahl, P., Padmanabhan, V.: RADAR: An in-building RF-based user location and tracking system. Proceedings of IEEE INFOCOM 2, 775–784 (2000)

    Google Scholar 

  9. Bhasker, E.S., Brown, S.W., Griswold, W.G.: Employing user feedback for fast, accurate, low-maintenance geolocationing. Technical Report CS2003-0765, UC San Diego, Computer Science and Engineering (2003)

    Google Scholar 

  10. Brumitt, B., Meyers, B., Krumm, J., Kern, A., Shafer, S.A.: Easyliving: Technologies for intelligent environments. In: 2nd Intl. Symposium on Handheld and Ubiquitous Computing, pp. 12–27 (2000)

    Google Scholar 

  11. Hightower, J., Borriello, G.: Location systems for ubiquitous computing. Computer 34, 57–66 (2001); This article is also excerpted in “IT Roadmap to a Geospatial Future,” a 2003 report from the Computer Science and Telecommunications Board of the National Research Council

    Article  Google Scholar 

  12. Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10 (2000)

    Google Scholar 

  13. Doucet, A., de Freitas, N., Gordon, N. (eds.): Sequential Monte Carlo in Practice. Springer, New York (2001)

    MATH  Google Scholar 

  14. Fox, D., Hightower, J., Liao, L., Schulz, D., Borriello, G.: Bayesian filtering for location estimation. IEEE Pervasive Computing 2, 24–33 (2003)

    Article  Google Scholar 

  15. Fox, D., Burgard, W., Kruppa, H., Thrun, S.: A probabilistic approach to collaborative multi-robot localization. Autonomous Robots 8, 244–325 (2000)

    Article  Google Scholar 

  16. Haehnel, D., Burgard, W., Fox, D., Thrun, S.: An efficient FastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE/RSJ (2003)

    Google Scholar 

  17. Krumm, J., Horvitz, E.: Locadio: Inferring motion and location from wi-fi signal strengths. In: Proceedings of the First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, Boston, MA (2004)

    Google Scholar 

  18. Fox, D.: KLD-sampling: Adaptive particle filters. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems 14 (NIPS), pp. 713–720. MIT Press, Cambridge (2002)

    Google Scholar 

  19. Hightower, J., Want, R., Borriello, G.: SpotON: An indoor 3d location sensing technology based on RF signal strength. UW CSE 00-02-02, University of Washington, Department of Computer Science and Engineering, Seattle, WA (2000)

    Google Scholar 

  20. Bulusu, N., Heidemann, J., Estrin, D.: GPS-less low cost outdoor localization for very small devices. IEEE Personal Communications, Special Issue on Smart Spaces and Environments 7, 28–34 (2000)

    Google Scholar 

  21. Hightower, J., Brumitt, B., Borriello, G.: The location stack: A layered model for location in ubiquitous computing. In: Proceedings of the 4th IEEE Workshop on Mobile Computing Systems & Applications (WMCSA 2002), pp. 22–28. IEEE Computer Society Press, Callicoon (2002)

    Chapter  Google Scholar 

  22. Graumann, D., Lara, W., Hightower, J., Borriello, G.: Real-world implementation of the location stack: The universal location framework. In: Proceedings of the 5th IEEE Workshop on Mobile Computing Systems & Applications (WMCSA 2003), pp. 122–128. IEEE Computer Society Press, Los Alamitos (2003)

    Google Scholar 

  23. Schulz, D., Fox, D., Hightower, J.: People tracking with anonymous and id-sensors using rao-blackwellised particle filters. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI), pp. 921–926. Morgan Kaufmann, San Francisco (2003)

    Google Scholar 

  24. Liao, L., Fox, D., Hightower, J., Kautz, H., Schulz, D.: Voronoi tracking: Location estimation using sparse and noisy sensor data. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE/RSJ, pp. 723–728 (2003)

    Google Scholar 

  25. Gellersen, H.W., Schmidt, A., Beigl, M.: Multi-sensor context-awareness in mobile devices and smart artifacts. Mobile Networks and Applications 7, 341–351 (2002)

    Article  MATH  Google Scholar 

  26. Schilit, B., LaMarca, A., Borriello, G., Griswold, W., McDonald, D., Lazowska, E., Balachandran, A., Hong, J., Iverson, V.: Challenge: Ubiquitous locationaware computing and the place lab initiative. In: Proceedings of the First ACM International Workshop on Wireless Mobile Applications and Services on WLAN, WMASH (2003)

    Google Scholar 

  27. Kwok, C., Fox, D., Meilă, M.: Adaptive real-time particle filters for robot localization. In: Proceedings of the IEEE International Conference on Robotics & Automation (ICRA), vol. 2, pp. 2836–2841 (2003)

    Google Scholar 

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Hightower, J., Borriello, G. (2004). Particle Filters for Location Estimation in Ubiquitous Computing: A Case Study. In: Davies, N., Mynatt, E.D., Siio, I. (eds) UbiComp 2004: Ubiquitous Computing. UbiComp 2004. Lecture Notes in Computer Science, vol 3205. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30119-6_6

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  • DOI: https://doi.org/10.1007/978-3-540-30119-6_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22955-1

  • Online ISBN: 978-3-540-30119-6

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