Advertisement

Task-Oriented Evaluation of Indoor Positioning Systems

  • Robert JackermeierEmail author
  • Bernd Ludwig
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

The performance of indoor positioning systems is usually measured by their accuracy in meters. This facilitates the comparison of different systems, but does not necessarily give information about how well they perform in real-life scenarios, e. g. during indoor navigation of walking persons. In this paper, we present a task-oriented evaluation that adapts the idea of landmark navigation: Instead of specifying the error metrically, system performance is measured by the ability to determine the correct segment of an indoor route, which in turn enables the navigation system to give correct instructions. We introduce the area match metric in order to identify areas where positioning proves problematic. In order to evaluate the described metric, we use a pedestrian dead reckoning approach to compute indoor positions. Without any external correction, the correct segment of the test route is identified in 88.4% of all trials. Based on these results, we explore options how to identify and predict erroneous situations during the navigation process as well as beforehand.

References

  1. Basso S, Frigo G, Giorgi G (2015) A smartphone-based indoor localization system for visually impaired people. In: 2015 IEEE international symposium on medical measurements and applications (MeMeA) proceedings, pp 543–548Google Scholar
  2. Bohannon RW, Williams Andrews A (2011) Normal walking speed: a descriptive meta-analysis. Physiotherapy 97(3):182–189CrossRefGoogle Scholar
  3. Davidson P, Piché R (2016) A survey of selected indoor positioning methods for smartphones. IEEE Commun Surv TutorGoogle Scholar
  4. Ebner F, Fetzer T, Deinzer F, Köping L, Grzegorzek M (2015) Multi sensor 3D indoor localisation. In: 2015 international conference on indoor positioning and indoor navigation (IPIN)Google Scholar
  5. Guo H, Uradzinski M, Yin H, Yu M (2015) Indoor positioning based on foot-mounted imu. Bull Pol Acad Sci Tech Sci 63(629–634):3Google Scholar
  6. Harle R (2013) A survey of indoor inertial positioning systems for pedestrians. IEEE Commun Surv Tutor 15(3):1281–1293CrossRefGoogle Scholar
  7. Herrera JCA, Plger PG, Hinkenjann A, Maiero J, Flores M, Ramos A (2014) Pedestrian indoor positioning using smartphone multi-sensing, radio beacons, user positions probability map and indoor-osm floor plan representation. In: 2014 international conference on indoor positioning and indoor navigation (IPIN), pp 636–645Google Scholar
  8. Hilsenbeck S, Bobkov D, Schroth G, Huitl R, Steinbach E (2014) Graph-based data fusion of pedometer and wifi measurements for mobile indoor positioning. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computingGoogle Scholar
  9. Kattenbeck M (2016) Empirically measuring salience of objects for use in pedestrian navigation. Dissertation, University RegensburgGoogle Scholar
  10. Liao L, Fox D, Hightower J, Kautz H, Schulz D (2003) Voronoi tracking: location estimation using sparse and noisy sensor data. In: Proceedings 2003 IEEE/RSJ international conference on intelligent robots and systems (IROS 2003), vol 1, pp 723–728Google Scholar
  11. Link JB, Smith P, Viol N, Wehrle K (2013) Accurate map-based indoor navigation on the mobile. J Locat Based Serv 7(1):23–43CrossRefGoogle Scholar
  12. Lymberopoulos D, Liu J, Yang X, Choudhury RR, Handziski V, Sen S (2015) A realistic evaluation and comparison of indoor location technologies: experiences and lessons learned. In: Proceedings of the 14th international conference on information processing in sensor networks, ACM, pp 178–189Google Scholar
  13. Muro-de-la Herran A, Garcia-Zapirain B, Mendez-Zorrilla A (2014) Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications. Sensors (Basel, Switzerland) 14(2):3362–3394Google Scholar
  14. Oberg T, Karsznia A, Oberg K (1993) Basic gait parameters: reference data for normal subjects, 10–79 years of age. J Rehabil Res Dev 30(2):210–23Google Scholar
  15. Ohm C, Müller M, Ludwig B (2015) Displaying landmarks and the user’s surroundings in indoor pedestrian navigation systems. J Ambient Intell Smart Environ 7(5):635–657CrossRefGoogle Scholar
  16. Pham DD, Suh YS (2016) Pedestrian navigation using foot-mounted inertial sensor and lidar. Sensors 16(1)Google Scholar
  17. Potortì F, Barsocchi P, Girolami M, Torres-Sospedra J, Montoliu R (2015) Evaluating indoor localization solutions in large environments through competitive benchmarking: the EvAAL-ETRI competition. In: 2015 international conference on indoor positioning and indoor navigation (IPIN). IEEE, pp 1–10Google Scholar
  18. Romanovas M, Goridko V, Klingbeil L, Bourouah M, Al-Jawad A, Traechtler M, Manoli Y (2013) Pedestrian indoor localization using foot mounted inertial sensors in combination with a magnetometer, a barometer and RFID. Springer, Berlin, Heidelberg, pp 151–172Google Scholar
  19. Rossi M, Seiter J, Amft O, Buchmeier S, Tröster G (2013) Roomsense: an indoor positioning system for smartphones using active sound probing. In: Proceedings of the 4th augmented human international conference, ACM, New York, NY, USA, AH ’13, pp 89–95.  https://doi.org/10.1145/2459236.2459252, http://doi.acm.org/10.1145/2459236.2459252
  20. Sprager S, Juric MB (2015) Inertial sensor-based gait recognition: a review. Sensors 15(9):22, 089–22, 127Google Scholar
  21. Susi M, Renaudin V, Lachapelle G (2013) Motion mode recognition and step detection algorithms for mobile phone users. Sensors (Basel, Switzerland) 13(2):1539–1562Google Scholar
  22. Thrun S, Burgard W, Fox D (2005) Probabilistic robotics (Intelligent robotics and autonomous agents). The MIT PressGoogle Scholar
  23. Torres-Sospedra J, Moreira A, Knauth S, Berkvens R, Montoliu R, Belmonte O, Trilles S, João Nicolau M, Meneses F, Costa A et al (2017) A realistic evaluation of indoor positioning systems based on wi-fi fingerprinting: the 2015 EvAAL-ETRI competition. J Ambient Intell Smart Environ 9(2):263–279CrossRefGoogle Scholar
  24. Verma S, Omanwar R, Sreejith V, Meera GS (2016) A smartphone based indoor navigation system. In: 2016 28th international conference on microelectronics (ICM), pp 345–348Google Scholar
  25. Waqar W, Chen Y, Vardy A (2016) Smartphone positioning in sparse wi-fi environments. Comput Commun 73:108–117CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Chair for Information ScienceUniversity RegensburgRegensburgGermany

Personalised recommendations