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Smartphone-based pedestrian tracking in indoor corridor environments

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Abstract

As the use of smartphones spreads rapidly, user localization becomes an important issue for providing diverse location-based services (LBS). While tracking users in outdoor environments is easily done with GPS, the solution for indoor tracking is not trivial. One common technique for indoor user tracking is to employ inertial sensors, but such a system needs to be capable of handling noisy sensors that would normally lead to cumulative locating errors. To reduce such error, additional infrastructure has often been deployed to adjust for these cumulative location errors. As well, previous work has used highly accurate sensors or sensors that are strapped to the body. This paper presents a stand-alone pedestrian tracking system, using only a magnetometer and an accelerometer in a smartphone in indoor corridor environments that are normally laid out in a perpendicular design. Our system provides reasonably accurate pedestrian locations without additional infrastructure or sensors. The experiment results show that the location error is less than approximately 7 m, which is considered adequate for indoor LBS applications.

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References

  1. Bahl P, Padmanabhan VN (2000) Radar an in-building rf-based user location and tracking system. pp 775–784

  2. Cho DK, Mun M, Lee U, Kaiser W, Gerla M (2010) Autogait: a mobile platform that accurately estimates the distance walked. In: Pervasive computing and communications (PerCom), 2010 IEEE international conference on, pp 116–124

  3. Constandache I, Bao X, Azizyan M, Choudhury RR (2010) Did you see bob?: human localization using mobile phones. In: Proceedings of the sixteenth annual international conference on mobile computing and networking, MobiCom ’10, ACM, New York, NY, USA, pp 149–160

  4. Constandache I, Choudhury R, Rhee I (2010) Towards mobile phone localization without war-driving. In: INFOCOM, 2010 proceedings IEEE, pp 1–9

  5. Evennou F, Marx F (2006) Advanced integration of wifi and inertial navigation systems for indoor mobile positioning. EURASIP J Appl Signal Process 2006:164

  6. Google: Google android platform. http://developer.android.com/index.html. Accessed 15 July 2010

  7. Gustafsson F, Gunnarsson F, Bergman N, Forssell U, Jansson J, Karlsson R, Nordlund PJ (2002) Particle filters for positioning, navigation, and tracking. Signal Proc IEEE Trans 50(2):425–437

    Article  Google Scholar 

  8. Jekeli C (2000) Inertial navigation systems with geodetic applications. Walter de Gruyter, Berlin

    Google Scholar 

  9. Jin Y, Motani M, Soh WS, Zhang J (2010) Sparsetrack: enhancing indoor pedestrian tracking with sparse infrastructure support. In: INFOCOM, 2010 proceedings IEEE, pp 1–9

  10. Mladenov M, Mock M (2009) A step counter service for java-enabled devices using a built-in accelerometer. In: Proceedings of the 1st international workshop on context-aware middleware and services: affiliated with the 4th international conference on communication system software and middleware (COMSWARE 2009), CAMS ’09, ACM, New York, NY, USA, pp 1–5

  11. Robertson P, Angermann M, Krach B (2009) Simultaneous localization and mapping for pedestrians using only foot-mounted inertial sensors. In: Proceedings of the 11th international conference on Ubiquitous computing, Ubicomp ’09, ACM, New York, NY, USA, pp 93–96

  12. Russell S, Norvig P (2010) Artificial intelligence: a modern approach. Prentice Hall series in artificial intelligence. Prentice Hall, NJ

    Google Scholar 

  13. Skyhook (2010) Lba counts in apple store and android marketplace. http://www.skyhookwireless.com/locationapps/. Accessed 15 July 2010

  14. Smith I, Tabert J, Wild T, Lamarca A, et al. (2005) Place lab: device positioning using radio beacons in the wild. In: In proceedings of the third international conference on pervasive computing, Springer, Berlin, pp 116–133

  15. Smith R, Self M, Cheeseman P (1987) Estimating uncertain spatial relationships in robotics. In: Robotics and automation. Proceedings of 1987 IEEE international conference on, vol. 4, p 850

  16. Steinhoff U, Schiele B (2010) Dead reckoning from the pocket- an experimental study. 2010 IEEE international conference on pervasive computing and communications (PerCom), pp 162–170

  17. Teixeira T, Jung D, Savvides A (2010) Tasking networked cctv cameras and mobile phones to identify and localize multiple people. In: Proceedings of the 12th ACM international conference on Ubiquitous computing, Ubicomp ’10, ACM, New York, NY, USA, pp 213–222

  18. Wikipedia: Precision and recall. http://en.wikipedia.org/wiki/Precision_and_recall. Accessed 15 July 2011

  19. Woodman O, Harle R (2009) Rf-based initialisation for inertial pedestrian tracking. In: Proceedings of the 7th international conference on pervasive computing, pervasive ’09, Springer, Berlin, Heidelberg, pp 238–255

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Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government, Ministry of Education, Science and Technology under Grant No.2011-0000156, No.2011-0015332.

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Authors and Affiliations

  1. Department of Computer Science, Yonsei University, Seoul, Korea

    Kwanghyo Park, Hyojeong Shin & Hojung Cha

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  1. Kwanghyo Park
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  2. Hyojeong Shin
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  3. Hojung Cha
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Correspondence to Hojung Cha.

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Park, K., Shin, H. & Cha, H. Smartphone-based pedestrian tracking in indoor corridor environments. Pers Ubiquit Comput 17, 359–370 (2013). https://doi.org/10.1007/s00779-011-0499-5

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  • DOI: https://doi.org/10.1007/s00779-011-0499-5

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