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Person Localization Using Sensor Information Fusion

  • Ricardo Anacleto
  • Lino Figueiredo
  • Ana Almeida
  • Paulo Novais
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 291)

Abstract

Nowadays the incredible grow of mobile devices market led to the need for location-aware applications. However, sometimes person location is difficult to obtain, since most of these devices only have a GPS (Global Positioning System) chip to retrieve location. In order to suppress this limitation and to provide location everywhere (even where a structured environment doesn’t exist) a wearable inertial navigation system is proposed, which is a convenient way to track people in situations where other localization systems fail. The system combines pedestrian dead reckoning with GPS, using widely available, low-cost and low-power hardware components. The system innovation is the information fusion and the use of probabilistic methods to learn persons gait behavior to correct, in real-time, the drift errors given by the sensors.

Keywords

Pedestrian Navigation System Inertial Navigation System Indoor Location GPS Probabilistic Algorithms 

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References

  1. 1.
    Anacleto, R., Figueiredo, L., Luz, N., Almeida, A., Novais, P.: Recommendation and planning through mobile devices in tourism context. In: Novais, P., Preuveneers, D., Corchado, J. (eds.) Ambient Intelligence - Software and Applications. AISC, vol. 92, pp. 133–140. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Anacleto, R., Luz, N., Figueiredo, L.: Personalized sightseeing tours support using mobile devices. In: Forbrig, P., Paternó, F., Mark Pejtersen, A. (eds.) HCIS 2010. IFIP AICT, vol. 332, pp. 301–304. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Bebek, O., Suster, M.A., Rajgopal, S., Fu, M.J., Huang, X., Cavusoglu, M.C., Young, D.J., Mehregany, M., van den Bogert, A.J., Mastrangelo, C.H.: Personal navigation via high-resolution gait-corrected inertial measurement units. IEEE Transactions on Instrumentation and Measurement 59(11), 3018–3027 (2010)CrossRefGoogle Scholar
  4. 4.
    Castaneda, N., Lamy-Perbal, S.: An improved shoe-mounted inertial navigation system. In: 2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–6 (2010)Google Scholar
  5. 5.
    Chintalapudi, K., Padmanabha Iyer, A., Padmanabhan, V.: Indoor localization without the pain. In: Proceedings of the Sixteenth Annual International Conference on Mobile Computing and Networking, MobiCom 2010, pp. 173–184. ACM, New York (2010)Google Scholar
  6. 6.
    Faulkner, W., Alwood, R., Taylor, D., Bohlin, J.: Altitude accuracy while tracking pedestrians using a boot-mounted IMU. In: IEEE/ION Position Location and Navigation Symposium (PLANS), pp. 90–96 (May 2010)Google Scholar
  7. 7.
    Feliz, R., Zalama, E., Gmez, J.: Pedestrian tracking using inertial sensors. Journal of Physical Agent 1, 35–42 (2009)Google Scholar
  8. 8.
    Ferreira, H., Figueiredo, L.: INPERLYS - independent personal location system. In: Cech, P., Bures, V., Nerudova, L. (eds.) Ambient Intelligence and Smart Environments, Ambient Intelligence Perspectives II, vol. 5, pp. 93–100 (2009)Google Scholar
  9. 9.
    Kinney, P.: Zigbee technology: Wireless control that simply works. In: Communications Design Conference, vol. 2 (2003)Google Scholar
  10. 10.
    McClendon, B.: A new frontier for google maps: mapping the indoors (2011)Google Scholar
  11. 11.
    Nilsson, J., Skog, I., Handel, P.: Performance characterisation of foot-mounted ZUPT-aided INSs and other related systems, pp. 1–7. IEEE (2010)Google Scholar
  12. 12.
    Raab, F., Blood, E., Steiner, T., Jones, H.: Magnetic position and orientation tracking system. IEEE Transactions on Aerospace and Electronic Systems (5), 709–718 (1979)Google Scholar
  13. 13.
    Ramos, J., Anacleto, R., Costa, Â., Novais, P., Figueiredo, L., Almeida, A.: Orientation system for people with cognitive disabilities. In: Novais, P., Hallenborg, K., Tapia, D.I., Rodríguez, J.M.C. (eds.) Ambient Intelligence - Software and Applications. AISC, vol. 153, pp. 43–50. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Saunders, J., Inman, V., Eberhart, H.: The major determinants in normal and pathological gait. The Journal of Bone & Joint Surgery 35(3), 543–558 (1953)Google Scholar
  15. 15.
    Terra, R., Figueiredo, L., Barbosa, R., Anacleto, R.: Step count algorithm adapted to indoor localization. In: Proceedings of the International C* Conference on Computer Science and Software Engineering, C3S2E 2013, pp. 128–129. ACM, New York (2013)Google Scholar
  16. 16.
    Vaughan, C., Davis, B., O’connor, J.: Dynamics of human gait. Human Kinetics Publishers Champaign, Illinois (1992)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ricardo Anacleto
    • 1
  • Lino Figueiredo
    • 1
  • Ana Almeida
    • 1
  • Paulo Novais
    • 2
  1. 1.GECAD - Knowledge Engineering and Decision Support Research Center, School of EngineeringPolytechnic of PortoPortoPortugal
  2. 2.CCTC/Informatics DepartmentUniversity of MinhoBragaPortugal

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