Advertisement

Bootstrapping the Dynamic Generation of Indoor Maps with Crowdsourced Smartphone Sensor Data

  • Georgios PipelidisEmail author
  • Christian Prehofer
  • Ilias Gerostathopoulos
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 936)

Abstract

Although there is a considerable progress in mapping the indoor places, most of the existing techniques are either expensive or difficult to apply. In this paper, we articulate our view on the future of indoor mapping, which is based on customized, crowdsourced and scalable approaches. On the basis of this approach, we discuss the research challenges that we envision to face in this world of customized bootstrapping and diverse techniques and services. We focus our interest in the combination of multiple of indoor mapping generation techniques and discuss challenges and various indoor mapping techniques. We introduce our adaptive method for bootstrapping the procedure of indoor mapping in multiple ways through intermediate services. Those emerged services enable the obtaining of useful data for this procedure, while they increase the quality of those data. We discuss the necessary components for such approach and we give an example of a bootstrapping procedure.

Keywords

Indoor mapping Crowdsourcing Bootstrapping process 

Notes

Acknowledgments

This work is part of the TUM Living Lab Connected Mobility project and has been funded by the Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie.

References

  1. 1.
    Mautz, R.: Indoor positioning technologies. ETH Zurich, Department of Civil, Environmental and Geomatic Engineering (2012)Google Scholar
  2. 2.
    Alzantot, M., Youssef, M.: CrowdInside: automatic construction of indoor floorplans. In: SIGSPATIAL 2012, pp. 99–108. ACM (2012)Google Scholar
  3. 3.
    Gao, R., et al.: Jigsaw: indoor floor plan reconstruction via mobile crowdsensing. In: Proceedings of MobiCom 2014, pp. 249–260. ACM (2014)Google Scholar
  4. 4.
    El-Hakim, S.F., Boulanger, P.: Mobile system for indoor 3-d mapping and creating virtual environments. US Patent 6,009,359 (1999)Google Scholar
  5. 5.
    Goetz, M., Zipf, A.: Towards crowdsoursing geographic information about indoor spaces; mapping the indoor world. GIM Int. 26, 30–34 (2012)Google Scholar
  6. 6.
    Pipelidis, G., Su, X., Prehofer, C.: Generation of indoor navigable maps with crowdsourcing. In: Proceedings of the 15th International Conference on Mobile and Ubiquitous Multimedia, MUM 2016, pp. 385–387. ACM, New York (2016)Google Scholar
  7. 7.
    Pipelidis, G., Prehofer, C., Gerostathopoulos, I.: Adaptive bootstrapping for crowdsourced indoor maps. In: Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management: GISTAM, INSTICC, vol. 1, pp. 284–289. SciTePress (2017)Google Scholar
  8. 8.
    Chen, J., Clarke, K.C.: Modeling standards and file formats for indoor mapping. In: Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management: GISTAM, INSTICC, vol. 1, pp. 268–275. SciTePress (2017)Google Scholar
  9. 9.
    Karlsson, N., Di Bernardo, E., Ostrowski, J., Goncalves, L., Pirjanian, P., Munich, M.E.: The vSLAM algorithm for robust localization and mapping. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, ICRA 2005, pp. 24–29. IEEE (2005)Google Scholar
  10. 10.
    ISO 16739:2013 - Industry Foundation Classes (IFC) for data sharing in the construction and facility management industries (2013)Google Scholar
  11. 11.
    ISO/TS 12911:2012 - Framework for building information modelling (BIM) guidance (2012)Google Scholar
  12. 12.
    OGC IndoorGML version 1.0.2 (2016). http://www.opengeospatial.org/standards/indoorgml
  13. 13.
    Liu, H., Shi, R., Zhu, L., Jing, C.: Conversion of model file information from IFC to GML. In: IGARSS 2014, pp. 3133–3136. IEEE (2014)Google Scholar
  14. 14.
    Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: using Kinect-style depth cameras for dense 3D modeling of indoor environments. Int. J. Robot. Res. 31, 647–663 (2012)CrossRefGoogle Scholar
  15. 15.
    Eaglin, T., Subramanian, K., Payton, J.: 3D modeling by the masses: a mobile app for modeling buildings. In: Proceedings of PERCOM 2013 Workshops, pp. 315–317. IEEE (2013)Google Scholar
  16. 16.
    Tomlein, M., Bielik, P., Krátky, P., Mitrík, S., Barla, M., Bieliková, M.: Advanced pedometer for smartphone-based activity tracking. In: HEALTHINF, pp. 401–404. (2012)Google Scholar
  17. 17.
    Pipelidis, G., Rad, O.R.M., Iwaszczuk, D., Prehofer, C., Hugentobler, U.: A novel approach for dynamic vertical indoor mapping through crowd-sourced smartphone sensor data. In: 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8 (2017)Google Scholar
  18. 18.
    Combettes, C., Renaudin, V.: Comparison of misalignment estimation techniques between handheld device and walking directions. In: IPIN 2015, pp. 1–8 (2015)Google Scholar
  19. 19.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. SIGKDD Explor. Newsl. 12, 74–82 (2011)CrossRefGoogle Scholar
  20. 20.
    Zhou, P., Zheng, Y., Li, Z., Li, M., Shen, G.: IODetector: a generic service for indoor outdoor detection. In: SenSys 2012, pp. 113–126. ACM (2012)Google Scholar
  21. 21.
    Kourogi, M., Kurata, T.: A method of pedestrian dead reckoning for smartphones using frequency domain analysis on patterns of acceleration and angular velocity. In: Proceedings of PLANS 2014, pp. 164–168. IEEE (2014)Google Scholar
  22. 22.
    Wu, M., Pathak, P.H., Mohapatra, P.: Monitoring building door events using barometer sensor in smartphones. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015, pp. 319–323. ACM, New York (2015)Google Scholar
  23. 23.
    Pipelidis, G., Prehofer, C.: Models and tools for indoor maps. In: Digital Mobility Platforms and Ecosystems, p. 154 (2016)Google Scholar
  24. 24.
    Roy, N., Wang, H., Roy Choudhury, R.: I am a smartphone and i can tell my user’s walking direction, pp. 329–342. ACM Press (2014)Google Scholar
  25. 25.
    Nguyen, P., et al.: User-friendly activity recognition using SVM classifier and informative features. In: IPIN 2015, pp. 1–8 (2015)Google Scholar
  26. 26.
    Kaiser, S., Lang, C.: Detecting elevators and escalators in 3D pedestrian indoor navigation. In: 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–6 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Georgios Pipelidis
    • 1
    Email author
  • Christian Prehofer
    • 1
  • Ilias Gerostathopoulos
    • 1
  1. 1.Fakultät für InformatikTechnische Universtität MünchenMunichGermany

Personalised recommendations