An Indoor Navigation Ontology for Production Assets in a Production Environment

  • Johannes Scholz
  • Stefan Schabus
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8728)


This article highlights an indoor navigation ontology for an indoor production environment. The ontology focuses on the movement of production assets in an indoor environment, to support autonomous navigation in the indoor space. Due to the fact that production environments have a different layout than ordinary indoor spaces, like buildings for office or residential use, an ontology focusing on indoor navigation looks different than ontologies in recent publications. Hence, rooms, corridors and doors to separate rooms and corridors are hardly present in an indoor production environment. Furthermore, indoor spaces for production purposes are likely to change in terms of physical layout and in terms of equipment location. The indoor navigation ontology highlighted in this paper utilizes an affordance based approach, which can be exploited for navigation purposes. A brief explanation of the routing methodology based on affordances is given in this paper, to justify the need for an indoor navigation ontology.


Indoor Environment Production Environment Building Information Modeling Clean Room Production Step 
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.


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  1. 1.
    Jenkins, P., Phillips, T., Mulberg, E., Hui, S.: Activity patterns of Californians: Use of and proximity to indoor pollutant sources. Atmospheric Environment — Part A General Topics 26A(12), 2141–2148 (1992)CrossRefGoogle Scholar
  2. 2.
    Worboys, M.: Modeling indoor space. In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness, pp. 1–6. ACM (2011)Google Scholar
  3. 3.
    Yang, L., Worboys, M.: A navigation ontology for outdoor-indoor space (work-in-progress). In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness, pp. 31–34. ACM (2011)Google Scholar
  4. 4.
    Klepeis, N., Nelson, W., Ott, W., Robinson, J., Tsang, A., Switzer, P., Behar, J., Hern, S., Engelmann, W.: The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants. Journal of Exposure Analysis and Environmental Epidemiology 11(3), 231–252 (2001)CrossRefGoogle Scholar
  5. 5.
    Raubal, M., Worboys, M.: A formal model of the process of wayfinding in built environments. In: Freksa, C., Mark, D.M. (eds.) COSIT 1999. LNCS, vol. 1661, pp. 381–399. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  6. 6.
    Raubal, M.: Ontology and epistemology for agent-based wayfinding simulation. International Journal of Geographical Information Science 15(7), 653–665 (2001)CrossRefGoogle Scholar
  7. 7.
    Goetz, M.: Using Crowdsourced Indoor Geodata for the Creation of a Three-Dimensional Indoor Routing Web Application. Future Internet 4, 575–591 (2012)CrossRefGoogle Scholar
  8. 8.
    Goetz, M., Zipf, A.: Formal definition of a user-adaptive and length-optimal routing graph for complex indoor environments. Geo-Spatial Information Science 14(2), 119–128 (2011)CrossRefGoogle Scholar
  9. 9.
    Meijers, M., Zlatanova, S., Preifer, N.: 3D geoinformation indoors: structuring for evaluation. In: Proceedings of the Next Generation 3D City Models, Bonn, Germany, pp. 11–16 (2005)Google Scholar
  10. 10.
    Jensen, C.S., Lu, H., Yang, B.: Graph model based indoor tracking. In: Tenth International Conference on Mobile Data Management: Systems, Services and Middleware, pp. 122–131. IEEE (2009)Google Scholar
  11. 11.
    Howell, I., Batcheler, B.: Building Information Modeling Two Years Later – Huge Poten-tial, Some Success and Several Limitations. The Laiserin Letter 22 (2005), (last accessed: December 7, 2013
  12. 12.
    Stoffel, E.P., Schoder, K., Ohlbach, H.J.: Applying hierarchical graphs to pedestrian indoor navigation. In: Proceedings of the 16th ACM SIG Spatial International Conference on Advances in Geographic Information Systems (2008)Google Scholar
  13. 13.
    Lorenz, B., Ohlbach, H.J., Stoffel, E.-P.: A hybrid spatial model for representing indoor environments. In: Carswell, J.D., Tezuka, T. (eds.) W2GIS 2006. LNCS, vol. 4295, pp. 102–112. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Becker, T., Nagel, C., Kolbe, T.: A multilayered space-event model for navigation in indoor spaces. In: 3D Geo-Information Sciences, pp. 61–77. Springer, Berlin (2009)CrossRefGoogle Scholar
  15. 15.
    Hagedorn, B., Trapp, M., Glander, T., Döllner, J.: Towards an indoor level-of-detail model for route visualization. In: Tenth International Conference on Mobile Data Management: Systems, Services and Middleware, pp. 692–697 (2009)Google Scholar
  16. 16.
    Stoffel, E.P., Lorenz, B., Ohlbach, H.J.: Towards a semantic spatial model for pedestrian indoor navigation. In: Hainaut, J.-L., et al. (eds.) ER Workshops 2007. LNCS, vol. 4802, pp. 328–337. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Richter, K.F., Winter, S., Rüetschi, U.J.: Constructing hierarchical representations of indoor spaces. In: Tenth International Conference on Mobile Data Management: Systems, Services and Middleware, pp. 686–691. IEEE (2009)Google Scholar
  18. 18.
    Niebel, B.W., Freivalds, A.: Methods, standards, and work design. McGraw-Hill (2003)Google Scholar
  19. 19.
    Nyström, R.H., Harjunkoski, I., Kroll, A.: Production optimization for continuously operated processes with optimal operation and scheduling of multiple units. Computers & Chemical Engineering 30(3), 392–406 (2006)CrossRefGoogle Scholar
  20. 20.
    Scholl, A., Becker, C.: State-of-the-art exact and heuristic solution procedures for simple assembly line balancing. European Journal of Operational Research 168(3), 666–693 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  21. 21.
    Bogorny, V., Palma, A.T., Engel, P., Alvares, L.O.: Weka-gdpm: Integrating classical data mining toolkit to geographic information systems. In: SBBD Workshop on Data Mining Algorithms and Aplications (WAAMD 2006), Florianopolis, Brasil, pp. 16–20 (2006)Google Scholar
  22. 22.
    Andrienko, G., Andrienko, N., Jankowski, P., Keim, D., Kraak, M.J., MacEachren, A., Wrobel, S.: Geovisual analytics for spatial decision support: Setting the research agenda. International International Journal of Geographic Information Science 21(8), 839–857 (2007)CrossRefGoogle Scholar
  23. 23.
    Compieta, P., Marion, D.S., Bertolotto, M., Ferrucci, F., Kechadi, T.: Exploratory spatio-temporal data mining and visualization. Journal of Visual Languages and Computing 18, 255–279 (2007)CrossRefGoogle Scholar
  24. 24.
    Smith, B.: Objects and their environments: from Aristotle to ecological ontology. In: Frank, A., Raper, J., Cheylan, J.P. (eds.) Life and Motion of Socio-economic Units, Taylor & Francis, London, pp. 79–97. Taylor & Francis, Abington (2001)Google Scholar
  25. 25.
    Davis, E.: Representations of Commonsense Knowledge. Representation and Reasoning. Morgan Kaufmann Publishers (1990)Google Scholar
  26. 26.
    Gibson, J.J.: The theory of affordances. In: Shaw, R., Bransford, J. (eds.) Perceiving, Acting, and Knowing, pp. 67–82. Lawrence Erlbaum (1977)Google Scholar
  27. 27.
    Gibson, J.J.: The Ecological Approach to Visual Perception. Houghton Mifflin Company (1979)Google Scholar
  28. 28.
    Raubal, M., Moratz, R.: A functional model for affordance-based agents. In: Rome, E., Hertzberg, J., Dorffner, G. (eds.) Towards Affordance-Based Robot Control. LNCS (LNAI), vol. 4760, pp. 91–105. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  29. 29.
    Turner, A., Penn, A.: Encoding natural movement as an agent-based system: an investigation into human pedestrian behaviour in the built environment. Environment and Planning B: Planning and Design 29, 473–490 (2002)CrossRefGoogle Scholar
  30. 30.
    Kapadia, M., Singh, S., Hewlett, B., Faloutsos, P.: Egocentric Affordance Fields in Pedestrian Steering. In: Proceedings of the 2009 Symposium on Interactive 3D Graphics and Games (2009)Google Scholar
  31. 31.
    Anagnostopoulos, C., Tsetsos, V., Kikiras, P., Hadjiefthymiades, S.: OntoNav: A semantic indoor navigation system. In: 1st Workshop on Semantics in Mobile Environments (SME 2005), Cyprus (2005)Google Scholar
  32. 32.
    Tsetsos, V., Anagnostopoulos, C., Kikiras, P., Hadjiefthymiades, S.: Semantically enriched navigation for indoor environments. International Journal of Web and Grid Services 2(4), 453–478 (2006)CrossRefGoogle Scholar
  33. 33.
    Geng, H. (ed.): Semiconductor manufacturing handbook. McGraw-Hill (2005)Google Scholar
  34. 34.
    Osswald, S., Weiss, A., Tscheligi, M.: Designing wearable devices for the factory: Rapid contextual experience prototyping. In: International Conference on Collaboration Technologies and Systems (CTS), pp. 517–521. IEEE (2013)Google Scholar
  35. 35.
    Thiesse, F., Fleisch, E., Dierkes, M.: LotTrack: RFID-based process control in the semiconductor industry. IEEE Pervasive Computing 5(1), 47–53 (2006)CrossRefGoogle Scholar
  36. 36.
    Jonietz, D., Timpf, S.: An Affordance-Based Simulation Framework for Assessing Spatial Suitability. In: Tenbrink, T., Stell, J., Galton, A., Wood, Z. (eds.) COSIT 2013. LNCS, vol. 8116, pp. 169–184. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  37. 37.
    Skupin, A.: Tri-space: Conceptualization, transformation, visualization. In: Proceedings of Sixth International Conference on Geographic Information Science, Zurich, pp. 14–17 (2010)Google Scholar
  38. 38.
    Skupin, A., Esperbé, A.: An alternative map of the united states based on an n-dimensional model of geographic space. Journal of Visual Languages & Computing 22(4), 290–304 (2011)CrossRefGoogle Scholar
  39. 39.
    Kohonen, T.: The self-organizing map. Neurocomputing 21(1), 1–6 (1998)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Johannes Scholz
    • 1
  • Stefan Schabus
    • 2
  1. 1.Studio iSPACEResearch Studios AustriaSalzburgAustria
  2. 2.School of GeoinformationCarinthia University of Applied SciencesVillachAustria

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