AGILE 2015 pp 235-252 | Cite as

Automated Generation of Indoor Accessibility Information for Mobility-Impaired Individuals

  • Nemanja Kostic
  • Simon Scheider
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


One important issue in developing assistive navigation systems for people with disability is the accuracy and relevancy of the systems’ knowledge bases from the perspective of these special user groups. The theory of affordances coupled with computer-based simulation offers a solution for automating the extraction of the relevant information from readily available sources—architectural floor plans. Simulation of movement in a wheelchair can be used to compute the accessible space of an indoor environment by comparing the degree of match between geometrical demands of navigation and the relevant physical properties of the environment. We also investigate what constitutes the right level of representation of the environment and adopt the grid graph model as suitable both for accessibility computation and for deriving higher-level networks of places and their connections that facilitate orientation and user-system interaction.


Building accessibility Affordance simulation Grid graph User relative navigation support People with disability 



The work presented in this paper was conducted and financed as part of the LIFE project at the Institute for Geoinformatics (IFGI) of the University of Münster. The Universitäts- und Landesbibliothek were kind enough to provide floor plans for the library building. The authors owe gratitude to Dr. Pedro Ribeiro de Andrade of Brazil’s National Institute for Space (INPE) for his help with programming in the TerraME modelling environment, as well as Dr. Marco Painho of the NOVA School of Statistics and Information Management (ISEGI-NOVA) and Dr. Rui Li of IFGI for their valuable input.


  1. Afyouni, I., Ray, C., & Claramunt, C. (2010) A fine-grained context-dependent model for indoor spaces. In Proceedings of 2nd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness.Google Scholar
  2. Afyouni, I., Ray, C., & Claramunt, C. (2012). Spatial models for context-aware indoor navigation systems: A survey. Journal of Spatial Information Science, 4, 85–123.Google Scholar
  3. Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22, 577–660.Google Scholar
  4. Barsalou, L. W. (2003). Situated simulation in the human conceptual system. Language and Cognitive Processes, 18(5/6), 513–562.CrossRefGoogle Scholar
  5. Budziszewski, P., Grabowski, A., Milanowicz, M., Jankowski, J., & Dzwiarek, M. (2011). Designing a workplace for workers with motion disability with computer simulation and virtual reality techniques. International Journal on Disability and Human Development, 10(4), 355–358.CrossRefGoogle Scholar
  6. Carneiro, T., Camara, G., de Andrade, P. R., & Pereira, R. R. (2011, February). An introduction to terrame. In INPE and UFOP Report, Version 1.5.Google Scholar
  7. Chemero, A. (2003). An outline of a theory of affordances. Ecological Psychology, 15(2), 181–195.CrossRefGoogle Scholar
  8. Fallah, N., Apostolopoulos, I., Bekris, K., & Folmer, E. (2013). Indoor human navigation systems: A survey. Interacting with Computers, 25(1), 21–33.Google Scholar
  9. Gibson, J. J. (1977). The theory of affordances. In R. Shaw & J. Bransford (Eds.), Perceiving (pp. 67–82)., Acting, and knowing: Toward an ecological psychology Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  10. Höllerer, T., Hallaway, D., Tinna, N., & Feiner, S. (2001). Steps toward accommodating variable position tracking accuracy in a mobile augmented reality system. In AIMS01: Second Int. Workshop on Artificial Intelligence in Mobile Systems (pp. 31–37), Seattle, WA, August 2001.Google Scholar
  11. Jonietz, D., Schuster, W., & Timpf, S. (2013) Modelling the suitability of urban networks for pedestrians: An affordance-based framework. In D. Vandenbroucke et al. (Eds.). Geographic information science at the heart of Europe. Lecture Notes in Geoinformation and Cartography. Switzerland: Springer.Google Scholar
  12. Jonietz, D., & Timpf, S. (2013) An affordance-based simulation framework for assessing spatial suitability. In T. Tenbrink et al. (Eds.), COSIT 2013. LNCS 8116 (pp. 169–184). Switzerland: Springer International Publishing.Google Scholar
  13. Li, X., Claramunt, C., & Ray, C. (2010). A grid graph-based model for the analysis of 2d indoor spaces. Computers, Environment and Urban Systems, 34, 532–540.CrossRefGoogle Scholar
  14. Neufert, E. (2005). Bauentwurfslehre. Wiesbaden: Vieweg Verlag.Google Scholar
  15. Ortmann, J., & Kuhn, W. (2010). Affordances as qualities. In Proceedings of the 2010 conference on Formal Ontology in Information Systems: Proceedings of the Sixth International Conference (FOIS 2010).Google Scholar
  16. Raubal, M. (2001a). Agent-based simulation of human wayfinding: A perceptual model for unfamiliar buildings (Ph.D. Thesis, Vienna University of Technology).Google Scholar
  17. Raubal, M. (2001b). Ontology and epistemology for agent-based wayfinding simulation. International Journal of Geographical Information Science, 15(7).Google Scholar
  18. Raubal, M. (2008). Wayfinding: Affordances and agent simulation. In Encyclopedia of GIS.Google Scholar
  19. Raubal, M., & Worboys, M. (1999). A formal model of the process of wayfinding in built environments. In C. Freksa, D. M. Marks (Eds.), COSIT99. LNCS 1661 (pp. 381–399).Google Scholar
  20. Richter, K.-F., Winter, S., & Santosa, S. (2011). Hierarchical representations of indoor spaces. Environment and Planning B: Planning and Design, 38(6), 1052–1070.CrossRefGoogle Scholar
  21. Rüetschi, U.-J., & Timpf, S. (2005). Modelling wayfinding in public transport: Network space and scene space. In C. Freksa et al. (Eds.), Spatial Cognition IV. LNAI 3343 (pp. 24–41). Heidelberg: Springer.Google Scholar
  22. Scarantino, A. (2003). Affordances explained. Philosophy of Science, 70, 949–961.CrossRefGoogle Scholar
  23. Scheider, S. (2011). Grounding geographic information in perceptual operations (Ph.D. Thesis, Westfälische Wilhelms-Universität Münster).Google Scholar
  24. Scheider, S., & Janowicz, K. (2014). Place reference systems. Applied Ontology, 9, 97–127.Google Scholar
  25. Swobodzinski, M., & Raubal, M. (2008). An indoor routing algorithm for the blind: Development and comparison to a routing algorithm for the sighted. International Journal of Geographical Information Science, 00(00), 1–28.Google Scholar
  26. Warren, W. H. (1984). Perceiving affordances: Visual guidance of stair climbing. Journal of Experimental Psychology, 10(5).Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute for GeoinformaticsUniversity of MünsterMünsterGermany
  2. 2.Institut für Kartografie und GeoinformatikETH ZürichZurichSwitzerland

Personalised recommendations