Earth Science Informatics

, Volume 10, Issue 1, pp 69–83 | Cite as

Free multi-floor indoor space extraction from complex 3D building models

  • Qing Xiong
  • Qing Zhu
  • Zhiqiang DuEmail author
  • Sisi Zlatanova
  • Yeting Zhang
  • Yan Zhou
  • Yun Li
Research Article


Intelligent navigation and facility management in complex indoor environments are issues at the forefront of geospatial information science. Indoor spaces with fine geometric and semantic descriptions provide a solid foundation for various indoor applications, but it is difficult to comprehensively extract free multi-floor indoor spaces from complex three-dimensional building models, such as those described using CityGML LoD4, with existing methods for the subdivision or extraction of indoor spaces based on vector topology processing. Therefore, this paper elaborates a new voxel-based approach for extracting free multi-floor indoor spaces from 3D building models. It transforms the complicated vector processing tasks into a simple raster process that consists of three steps: voxelization with semantic enhancement, voxel classification, and boundary extraction. Experiments illustrate that the proposed method can automatically and correctly extract free multi-floor indoor spaces, especially two typical kinds of open indoor spaces, namely, lobbies and staircases.


Free multi-floor indoor space CityGML LoD4 Indoor space extraction Voxel 



This paper was supported by the National Nature Science Foundation of China (No. 41571390, 41471320, and 41471332) and the National High Technology Research and Development Program of China (2015AA123901).


  1. Afyouni I, Cyril R, Christophe C (2012) Spatial models for context-aware indoor navigation systems: a survey. J Spat Inf Sci 1:85–123Google Scholar
  2. Becker T, Nagel C, Kolbe TH (2008) A multilayered space-event model for navigation in indoor spaces. In: Lee J, Zlatanova S (eds) 3D Geo-information sciences. Springer, Heidelberg, Berlin, pp 61–77Google Scholar
  3. Brown G, Nagel C, Zlatanova S, Kolbe TH (2012) Modelling 3D topographic space against indoor navigation requirements. In: Pouliot J, Daniel S, Hubert F, Zamyadi A (eds) Progress and new trends in 3D geoinformation sciences. Springer, Heidelberg, Berlin, pp 1–22Google Scholar
  4. Geraerts R (2010) Planning short paths with clearance using explicit corridors. In: 2010 I.E. International Conference on Robotics and Automation (ICRA), May 3–8, 2010, Anchorage, Alaska, pp 1997–2004Google Scholar
  5. Girard G, Côté S, Zlatanova S, Barette Y, St-Pierre J, van Oosterom P (2011) Indoor pedestrian navigation using foot-mounted IMU and portable ultrasound range sensors. Sensors 11:7606–7624. doi: 10.3390/s110807606 CrossRefGoogle Scholar
  6. Isikdag U, Zlatanova S, Underwood J (2013) A BIM-oriented model for supporting indoor navigation requirements. Comput Environ Urban Syst 41:112–123. doi: 10.1016/j.compenvurbsys.2013.05.001 CrossRefGoogle Scholar
  7. Jensen CS, Lu H, Yang B (2010) Indoor-a new data management frontier. Bull IEEE Comput Soc Tech Comm Data Eng 33:12–17Google Scholar
  8. Jones MW, Satherley R (2000) Voxelisation: modelling for volume graphics. In: Girod B, Greiner G, Niemann H, Seidel H-P (eds) Vision modeling and visualization, November 22–24, Saarbrücken, Germany, pp 319–326Google Scholar
  9. Kim J, Yoo S, Li K (2014) Integrating IndoorGML and CityGML for indoor space. In: Dieter P, Ki-Joune L (eds) Web and wireless geographical information systems. Springer, Heidelberg, Berlin, pp 184–196CrossRefGoogle Scholar
  10. Krūminaitė M, Zlatanova S (2014) Indoor space subdivision for indoor navigation. In: The Sixth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness, November 04–07, 2014, Dallas/Fort Worth, TX, pp 25–31Google Scholar
  11. Lamarche F, Donikian S (2004) Crowd of virtual humans: a new approach for real time navigation in complex and structured environments. Comput Graphics Forum 23:509–518. doi: 10.1111/j.1467-8659.2004.00782.x CrossRefGoogle Scholar
  12. Lee J (2004) A spatial access-oriented implementation of a 3-D GIS topological data model for Urban Entities. GeoInformatica 8:237–264. doi: 10.1023/b:gein.0000034820.93914.d0 CrossRefGoogle Scholar
  13. Li K (2008) Indoor space: a new notion of space. In: Bertolotto M, Ray C, Li X (eds) Web and wireless geographical information systems. Springer, Berlin, pp 1–3CrossRefGoogle Scholar
  14. Li X, Claramunt C, Ray C (2010) A grid graph-based model for the analysis of 2D indoor spaces. Comput Environ Urban Syst 34:532–540. doi: 10.1016/j.compenvurbsys.2010.07.006 CrossRefGoogle Scholar
  15. Liu L, Zlatanova S (2011) A “door-to-door” path-finding approach for indoor navigation. In: Gi4DM 2011: GeoInformation for Disaster Management, May 3–8, Antalya, TurkeyGoogle Scholar
  16. Liu L, Zlatanova S (2012) A semantic data model for indoor navigation. In: The Fourth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness, November 07–09, 2012, Redondo Beach, CA, pp 1–8Google Scholar
  17. Lorenz B, Ohlbach HJ, Stoffel E (2006) A hybrid spatial model for representing indoor environments. In: James DC, Taro T (eds) Web and wireless geographical information systems. Springer, Heidelberg, Berlin, pp 102–112CrossRefGoogle Scholar
  18. Meagher D (1982) Geometric modeling using octree encoding. Comput Graphics Image Process 19:129–147. doi: 10.1016/0146-664x(82)90104-6 CrossRefGoogle Scholar
  19. Meijers M, Zlatanova S, Pfeifer N (2005) 3D geoinformation indoors: structuring for evacuation. In: Proceedings of Next generation 3D city models, June 21–22, 2005, Bonn, Germany, pp 21–22Google Scholar
  20. OGC IndoorGML (2014) Indoor geography markup language (IndoorGML) encoding standard. Accessed 3 Dec 2014
  21. Oomes S, Snoeren P, Dijkstra T (1997) 3D shape representation: transforming polygons into voxels. In: Romeny BT, Florack L, Koenderink J et al (eds) Scale-space theory in computer vision. Springer, Heidelberg, Berlin, pp 349–352CrossRefGoogle Scholar
  22. Schaap J, Zlatanova S, van Oosterom PJM (2011) Towards a 3D geo-data model to support pedestrian routing in multimodal public transport travel advices. In: Rumor M (ed) Urban and regional data management. CRC Press, Boca Raton, pp 63–78CrossRefGoogle Scholar
  23. Stoffel E, Lorenz B, Ohlbach HJ (2007) Towards a semantic spatial model for pedestrian indoor navigation. In: Hainaut JL, Rundensteiner EA, Kirchberg M, Bertolotto M, Brochhausen M, Chen P et al (eds) Advances in conceptual modeling - foundations and applications. Springer, Heidelberg, Berlin, pp 328–337CrossRefGoogle Scholar
  24. Thill J-C, Dao THD, Zhou Y (2011) Traveling in the three-dimensional city: applications in route planning, accessibility assessment, location analysis and beyond. J Transp Geogr 19:405–421. doi: 10.1016/j.jtrangeo.2010.11.007 CrossRefGoogle Scholar
  25. Wallgrün JO (2005) Autonomous construction of hierarchical voronoi-based route graph representations. In: Freksa C, Knauff M, Krieg-Brückner B, Nebel B, Barkowsky T (eds) Spatial cognition IV, reasoning, action, interaction. Springer, Berlin, pp 413–433CrossRefGoogle Scholar
  26. Xie X, Zhu Q, Du Z, Xu W, Zhang Y (2013) A semantics-constrained profiling approach to complex 3D city models. Comput Environ Urban Syst 41:309–317. doi: 10.1016/j.compenvurbsys.2012.07.003 CrossRefGoogle Scholar
  27. Xiong Q, Zhu Q, Zlatanova S, Huang L, Zhou Y, Du Z (2013) Multi-dimensional indoor location information model. In: Acquisition and modelling of indoor and enclosed environments, December 11–13, Cape Town, South AfricaGoogle Scholar
  28. Yuan W, Schneider M (2010) iNav: an indoor navigation model supporting length-dependent optimal routing. In: Painho M, Santos MY, Pundt H (eds) Geospatial thinking. Springer, Heidelberg, Berlin, pp 299–313CrossRefGoogle Scholar
  29. Zlatanova S, Liu L, Sithole G (2013a) A conceptual framework of space subdivision for indoor navigation. In: The Fifth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness, November 05–08, 2013, Orlando, FL, pp 37–41Google Scholar
  30. Zlatanova S, Sithole G, Nakagawa M, Zhu Q (2013b) Problems in indoor mapping and modelling. In: Acquisition and modelling of indoor and enclosed environments 2013, December 11–13, 2013, Cape Town, South AfricaGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Qing Xiong
    • 1
  • Qing Zhu
    • 1
    • 2
    • 3
  • Zhiqiang Du
    • 1
    • 3
    Email author
  • Sisi Zlatanova
    • 4
  • Yeting Zhang
    • 1
    • 3
  • Yan Zhou
    • 5
  • Yun Li
    • 2
  1. 1.State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina
  2. 2.Faculty of Geosciences and Environmental EngineeringSouthwest Jiaotong UniversityChengduChina
  3. 3.Collaborative Innovation Center for Geospatial TechnologyWuhanChina
  4. 4.3D Geoinformation, UrbanismDelft University of TechnologyDelftNetherlands
  5. 5.School of Resources and EnvironmentUniversity of Electric Science and Technology of ChinaChengduChina

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