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Modeling indoor space

Published:01 November 2011Publication History

ABSTRACT

This paper begins by reviewing the motivation for an informatics of indoor space. It then discusses application domains and consider why current geospatial technology, with its focus on outdoor space, needs to be extended. We review existing formal models of indoor space, along with their applications, and introduce a new model that is the subject of the author's current research. We conclude with some observations about the development of a unified model of both indoor and outdoor space.

References

  1. I. Afyouni, C. Ray, and C. Claramunt. A fine-grained context-dependent model for indoor spaces. In Proceedings of the 2nd ACM SIGSpatial International Workshop on Indoor Spatial Awareness, pages 33--38. ACM, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. C. Anagnostopoulos, V. Tsetsos, P. Kikiras, and S. Hadjiefthymiades. OntoNav: A semantic indoor navigation system. In 1st Workshop on Semantics in Mobile Environments (SME05), Cyprus, 2005.Google ScholarGoogle Scholar
  3. G. Bahmutov, V. Popescu, and M. Mudure. Efficient large scale acquisition of building interiors. Computer Graphics Forum, 25(3):655--662, Sept. 2006.Google ScholarGoogle ScholarCross RefCross Ref
  4. B. Baumgart. Winged edge polyhedron representation. Technical Report CS-TR-72-320, Stanford university, CA, 1972. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. T. Becker, C. Nagel, and T. Kolbe. A multilayered space-event model for navigation in indoor spaces. 3D Geo-Information Sciences, pages 61--77, 2009.Google ScholarGoogle Scholar
  6. T. Becker, C. Nagel, and T. H. Kolbe. Supporting contexts for indoor navigation using a multilayered space model. In 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware, pages 680--685. IEEE, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. M. Bhatt, F. Dylla, and J. Hois. Spatio-terminological inference for the design of ambient environments. Spatial Information Theory, LNCS 5756:371--391, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. O. Buneman. A grammar for the topological analysis of plane figures. In B. Meltzer and D. Michie, editors, Machine Intelligence, volume 5, pages 383--393. Elselvier, 1970.Google ScholarGoogle Scholar
  9. CityGML: Exchange and Storage of Virtual 3D City Models. http://www.citygml.org.Google ScholarGoogle Scholar
  10. J. Edmonds. A combinatorial representation for polyhedral surfaces. Notices Amer. Math. Soc., 7:646, 1960.Google ScholarGoogle Scholar
  11. J. J. Gibson. The Ecological Approach to Visual Perception. Psychology Press, 1979.Google ScholarGoogle Scholar
  12. N. Giudice, L. Walton, and M. Worboys. The informatics of indoor and outdoor space: a research agenda. In Proceedings of the 2nd ACM SIGSpatial International Workshop on Indoor Spatial Awareness, pages 47--53. ACM, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. M. Goetz and A. Zipf. Formal definition of a user-adaptive and length-optimal routing graph for complex indoor environments. Geo-Spatial Information Science, 14(2):119--128, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  14. G. Gröger and L. Plümer. Derivation of 3D indoor models by grammars for route planning. Photogrammetrie, Fernerkundung, Geoinformation, 3:193--210, 2010.Google ScholarGoogle Scholar
  15. B. Hagedorn, M. Trapp, T. Glander, and J. Döllner. Towards an indoor level-of-detail model for route visualization. 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware, pages 692--697, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Industry Foundation Classes. http://buildingsmart.com/standards/ifc.Google ScholarGoogle Scholar
  17. P. Jenkins, T. Phillips, E. Mulberg, and S. Hui. Activity patterns of Californians: Use of and proximity to indoor pollutant sources. Atmospheric Environment - Part A General Topics, 26A(12):2141--2148, 1992.Google ScholarGoogle Scholar
  18. C. S. Jensen, H. Lu, and B. Yang. Graph model based indoor tracking. In 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware, pages 122--131. IEEE, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. H. Kim, C. Jun, Y. Cho, and G. Kim. Indoor spatial analysis using space syntax. International Archives of Photogrammetry, Remote Sensing and Spatial Information Scienes, 37(B2):1065--1070, 2008.Google ScholarGoogle Scholar
  20. B. Kuipers and Y. Byun. A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations. Robotics and autonomous systems, 8(1--2):47--63, 1991.Google ScholarGoogle Scholar
  21. J. Lee. 3D GIS for geo-coding human activity in micro-scale urban environments. In M. J. Egenhofer, C. Freksa, and H. J. Miller, editors, Geographic Information Science, volume 3234 of Lecture Notes in Computer Science, pages 162--178. Springer, 2004.Google ScholarGoogle Scholar
  22. J. Lee. GIS-based geocoding methods for area-based addresses and 3D addresses in urban areas. Environment and Planning B: Planning and Design, 36(1):86--106, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  23. K.-J. Li. Indoor space: A new notion of space. Personal and Ubiquitous Computing, pages 1--3, 2008.Google ScholarGoogle Scholar
  24. B. Lorenz and H. Ohlbach. A hybrid spatial model for representing indoor environments. Web and Wireless Geographical Information Systems, pages 102--112, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. B. Lorenz, H. Ohlbach, and E. Stoffel. A hybrid model for indoor spatial reasoning. In Proc. 1st Int. Workshop on Mobile Geospatial Augmented Reality, pages 2--7, 2006.Google ScholarGoogle Scholar
  26. M. Raubal and M. Worboys. A formal model of the process of wayfinding in built environments. Spatial information theory. Cognitive and Computational Foundations of Geographic Information Science, Lecture Notes in Computer Science, 1661:381--399, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. K.-F. Richter, S. Winter, and U.-J. Rüetschi. Constructing hierarchical representations of indoor spaces. In 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware, pages 686--691. IEEE, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. J. Stell and M. Worboys. Relations between adjacency trees. Journal of Theoretical Computer Science, 412:4452--4468, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  29. E. Stoffel, B. Lorenz, and H. Ohlbach. Towards a semantic spatial model for pedestrian indoor navigation. Advances in Conceptual Modelingâ Foundations and Applications, pages 328--337, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. E.-P. Stoffel, K. Schoder, and H. J. Ohlbach. Applying hierarchical graphs to pedestrian indoor navigation. Proceedings of the 16th ACM SIGSpatial international conference on advances in geographic information systems - GIS '08, page 1, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. S. Thrun. Learning metric-topological maps for indoor mobile robot navigation. Artificial Intelligence, 99:21--71, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. V. Tsetsos, C. Anagnostopoulos, P. Kikiras, and S. Hadjiefthymiades. Semantically enriched navigation for indoor environments. International Journal of Web and Grid Services, 2(4):453--478, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. A. Vanclooster, P. De Maeyer, and V. Fack. On the way of integrating evacuation approaches. International Archives of Photgrammetry, Remote Sensing and Spatial Information Sciences, 38(4/W15):169, 2010.Google ScholarGoogle Scholar
  34. L. Walton and M. Worboys. An algebraic approach to image schemas for geographic space. In Spatial Information Theory, pages 357--370. Springer LNCS 5756, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. A. White. Graphs of Groups on Surfaces. North-Holland, 2001.Google ScholarGoogle Scholar
  36. M. Worboys. Representing the topology of spatial scenes. Technical Report, www.worboys.org/publications/techrep.pdf.Google ScholarGoogle Scholar
  37. L. Yang and M. Worboys. Similarities and differences between outdoor and indoor space from the perspective of navigation. Poster presented at COSIT 2011, www.worboys.org/publications/Cosit2011poster.pdf.Google ScholarGoogle Scholar
  38. L. Yang and M. Worboys. A navigation ontology for outdoor-indoor space. In Proceedings of the 3rd ACM SIGSpatial International Workshop on Indoor Spatial Awareness. ACM, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. H. Zender, O. Martinezmozos, P. Jensfelt, G. Kruijff, and W. Burgard. Conceptual spatial representations for indoor mobile robots. Robotics and Autonomous Systems, 56(6):493--502, June 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Conferences
          ISA '11: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness
          November 2011
          64 pages
          ISBN:9781450310352
          DOI:10.1145/2077357

          Copyright © 2011 Author

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 1 November 2011

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