A Multilayered Space-Event Model for Navigation in Indoor Spaces

  • Thomas Becker
  • Claus Nagel
  • Thomas H. Kolbe
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


In this paper a new conceptual framework for indoor navigation is proposed. While route planning requires models which reflect the internal structure of a building, localization techniques require complementary models reflecting the characteristics of sensors and transmitters. Since the partitioning of building space differs in both cases, a conceptual separation of different space models into a multilayer representation is proposed. Concrete space models for topographic space and sensor space are introduced. Both are systematically subdivided into primal and dual space on the one hand and (Euclidean) geometry and topology on the other hand. While topographic space describes 3D models of buildings and their semantically subdivisions into storey’s and rooms, sensor space describes the positions and ranges of transmitters and sensors like Wi-Fi access points or RFID sensors. It is shown how the connection of the different layers of the space models describe a joint state of a moving subject or object and reduces uncertainty about its current position.


Space Model Building Information Modeling Dual Graph Route Planning Primal Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Herring, J., 2001: The OpenGIS Abstract Specification, Topic 1: Feature Geometry (ISO 19107 Spatial Schema), Version 5. OGC Document Number 01-101. / prepared by Technical Committee ISO/TC211 Geographic information — spatial schema, available at:
  2. 2.
    Lorenz, B., Ohlbach, H. J., Stoffel, E.-P., Rosner, M., September 2006: NL Navigation Commands from Indoor WLAN fingerprinting position data, Technical Report of REWERSE-Project, Munich, Germany, (Accessed June 2008),
  3. 3.
    Anagnostopoulus, C., Testsos, V., Kikiras, P., Hadjiefthymiades, S. P., 2003: OntoNav: A Semantic Indoor Navigation System. In: First International Workshop on Managing Context Information in Mobile and Pervasive Envi-ronments, Vol. 165, Ayia Napa, ZypernGoogle Scholar
  4. 4.
    Kulyukin, V., Gharpure, C., Nicholson, J., 2005: RoboCart: Toward Robot-Assisted Navigation of Crocery Stores by the Visually Impaired. In: Proceed-ings of the international Conference on Intelligent Robots and Systems, IROS 2005, IEEE/RSJGoogle Scholar
  5. 5.
    Lefebvre S, Hornus S: Automatic cell-and-portal decomposition. Technical Report 4898, INRIA, 2003. (Ac-cessed June 2008).
  6. 6.
    Lee, J., 2004: 3D GIS for Geocoding Human Activity in Microscale Urban Environments. In: M.J. Egenhofer, C. Freksa, and H.J. Miller (Eds.): GIS-cience 2004 , Springer, Berlin, GermanyGoogle Scholar
  7. 7.
    Lee, J., Zlatanova, S., 2008: A 3D data model and topological analyses for emergency response in urban areas. Geospatial Information Technology for Emergency Response – Zlatanova & Li (eds), Taylor & Francis Group, London, UKGoogle Scholar
  8. 8.
    Munkres, J. R., 1984.: Elements of Algebraic Topology. Addison-Wesley, Menlo Park, CAGoogle Scholar
  9. 9.
    Kolodziej, K. W., Hjelm, J., 2006: Local Positioning Systems – LBS Applications and Services, Taylor & Francis Group, London, UKGoogle Scholar
  10. 10.
    Gröger, G., Kolbe, T.H., Czerwinski, A., 2007: OpenGIS City Geography Markup Language (CityGML), Version 0.4.0, OGC Best Practices Paper Doc. No. 07-062Google Scholar
  11. 11.
    Kolbe, T.H., Gröger, G. & Plümer, L. 2005: CityGML – Interoperable Access to 3D City Models. In P. van Oosterom, S. Zlatanova & E.M. Fendel (eds), Geo-information for Disaster Management; Proc. of the 1st International Symposium on Geo-information for Disaster Management’, Delft, The Neth-erlands, March 21–23, 2005. Springer.Google Scholar
  12. 12.
    Adachi, Y., Forester, J., Hyvarinen, J., Karstila, K., Liebich, T., Wix, J. 2003: Industry Foundation Classes IFC2x Edition 3, International Alliance for Inter-operability,
  13. 13.
    Liao, L., Fox, D., Hightower, J. Kautz, H., Schulz, D., 2003: Voronoi Tracking: Location Estimation Using Sparse and Noisy Sensor Data. In: Proc. of the International Conference on Intelligent Robots and Systems, IROS 2003, IEEE/RSJGoogle Scholar
  14. 14.
    Lewin, B., Cassimeris, L., Lingappa, V. R., Plopper, G., 2006: Cells. Jones & Bartlett, USAGoogle Scholar
  15. 15.
    Mishra, A. R., 2004. Fundamentals of Cellular Network Planning and Optimisation. John Wiley & Sons, Chichester, UKGoogle Scholar
  16. 16.
    LaValle, S. M, 2006: Planning Algorithms. Cambridge University Press, USAGoogle Scholar
  17. 17.
    Lorenz, B., Ohlbach, H. J., Stoffel, E.-P., Rosner, M., September 2007: Towards a Semantic Spatial Model for Pedestrian Indoor Navigation. In: Lecture Notes in Computer Science - Advances in Conceptual Modeling – Foundations and Applications, Volume 4802/2007, Springer, Berlin, GermanyGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Thomas Becker
    • 1
  • Claus Nagel
    • 1
  • Thomas H. Kolbe
    • 1
  1. 1.Institute for Geodesy and Geoinformation Science TechnischeUniversität BerlinGermany

Personalised recommendations