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Computer Vision Based Indoor Navigation: A Visual Markers Evaluation

  • Gaetano C. La DelfaEmail author
  • V. Catania
  • S. Monteleone
  • Juan F. De Paz
  • J. Bajo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 376)

Abstract

The massive diffusion of smartphones and the exponential rise of location based services (LBS) have made the problem of localization and navigation inside buildings one of the most important technological challenges of the last years. Indoor positioning systems have a huge market in the retail sector and contextual advertising; moreover, they can be fundamental to increase the quality of life for the citizens. Various approaches have been proposed in scientific literature. Recently, thanks to the high performances of the smartphones’ cameras, marker-less and marked-based computer vision approaches have been investigated. In a previous paper, we proposed a technique for indoor navigation using both Bluetooth Low Energy (BLE) and a 2D visual markers system deployed into the floor. In this paper, we present a qualitative performance evaluation of three 2D visual markers suitable for real-time applications.

Keywords

Indoor location systems Computer vision Fiducial markers 

References

  1. 1.
    A. Chandgadkar, W. Knottenbelt, An Indoor Navigation System for Smartphones (Imperial College, London, 2013)Google Scholar
  2. 2.
    C. Danakis, M.Z. Afgani, G. Povey, I. Underwood, H. Haas, Using a CMOS camera sensor for visible light communication, in Workshops Proceedings of the Global Communications Conference, GLOBECOM 2012 (Anaheim, California, USA, 2012), pp. 1244–1248, http://dx.doi.org/10.1109/GLOCOMW.2012.6477759. Accessed 3–7 Dec 2012
  3. 3.
    B.L. Ecklbauer, A mobile positioning system for android based on visual markers. Ph.D. thesis, Hagenberg, Austria (2014)Google Scholar
  4. 4.
    G.C. La Delfa, V. Catania, Accurate indoor navigation using smartphone, bluetooth low energy and visual tags, in Proceedings of the 2nd Conference on Mobile and Information Technologies in Medicine (2014), http://mobmed.org/download/proceedings2014/mobileMed2014_paper_6.pdf
  5. 5.
    S. Garrido-Jurado, R. Muñoz-Salinas, F.J. Madrid-Cuevas, M.J. Marín-Jiménez, Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognit. 47(6), 2280–2292 (2014)CrossRefGoogle Scholar
  6. 6.
    D. Han, S.H. Jung, M. Lee, G. Yoon, Building a practical wi-fi-based indoor navigation system. IEEE Pervasive Comput. 13(2), 72–79 (2014). http://dx.doi.org/10.1109/MPRV.2014.24
  7. 7.
    Q. Incorporated, Qualcomm vuforia (2014), https://developer.vuforia.com/
  8. 8.
    A. Jovicic, J. Li, T. Richardson, Visible light communication: opportunities, challenges and the path to market. IEEE Commun. Mag. 51(12), 26–32 (2013). http://dblp.uni-trier.de/db/journals/cm/cm51.html#JovicicLR13
  9. 9.
    H. Kato, M. Billinghurst, Marker tracking and hmd calibration for a video-based augmented reality conferencing system, in IWAR ’99: Proceedings of the 2Nd IEEE and ACM International Workshop on Augmented Reality (IEEE Computer Society, Washington, DC, USA, 1999), pp. 85–94. http://dl.acm.org/citation.cfm?id=857202.858134
  10. 10.
    Y. Liu, M. Dashti, J. Zhang, Indoor localization on mobile phone platforms using embedded inertial sensors, in WPNC (IEEE, 2013), pp. 1–5. http://dblp.uni-trier.de/db/conf/wpnc/wpnc2013.html#LiuDZ13
  11. 11.
    A. Mohan, G. Woo, S. Hiura, Q. Smithwick, R. Raskar, Bokode: imperceptible visual tags for camera based interaction from a distance, in SIGGRAPH ’09: ACM SIGGRAPH 2009 Papers (ACM, New York, NY, USA, 2009), pp. 98:1–98:8. http://doi.acm.org/10.1145/1576246.1531404
  12. 12.
    L. Naimark, E. Foxlin, Circular data matrix fiducial system and robust image processing for a wearable vision-inertial self-tracker, in ISMAR ’02: Proceedings of the 1st International Symposium on Mixed and Augmented Reality (IEEE Computer Society, Washington, DC, USA, 2002), pp. 27–36. http://dl.acm.org/citation.cfm?id=850976.854961
  13. 13.
    E. Olson, AprilTag: a robust and flexible visual fiducial system, in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2011), pp. 3400–3407Google Scholar
  14. 14.
    H. Wang, S. Sen, A. Elgohary, M. Farid, M. Youssef, R.R. Choudhury, No need to war-drive: unsupervised indoor localization, in Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (MobiSys ’12). (ACM, New York, NY, USA, 2012), pp. 197–210. http://doi.acm.org/10.1145/2307636.2307655

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Gaetano C. La Delfa
    • 1
    Email author
  • V. Catania
    • 1
  • S. Monteleone
    • 1
  • Juan F. De Paz
    • 2
  • J. Bajo
    • 3
  1. 1.Department of Electrical, Electronics and Computer Engineering (DIEEI)University of CataniaCataniaItaly
  2. 2.BISITE Research Group, Faculty of ScienceUniversity of SalamancaSalamancaSpain
  3. 3.Department of Artificial IntelligencePolytechnic University of MadridMadridSpain

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