Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
권호 표지
DOI QR코드

DOI QR Code

Using Omnidirectional Images for Semi-Automatically Generating IndoorGML Data

  • Received : 2018.09.11
  • Accepted : 2018.10.17
  • Published : 2018.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

As human beings spend more time indoors, and with the growing complexity of indoor spaces, more focus is given to indoor spatial applications and services. 3D topological networks are used for various spatial applications that involve navigation indoors such as emergency evacuation, indoor positioning, and visualization. Manually generating indoor network data is impractical and prone to errors, yet current methods in automation need expensive sensors or datasets that are difficult and expensive to obtain and process. In this research, a methodology for semi-automatically generating a 3D indoor topological model based on IndoorGML (Indoor Geographic Markup Language) is proposed. The concept of Shooting Point is defined to accommodate the usage of omnidirectional images in generating IndoorGML data. Omnidirectional images were captured at selected Shooting Points in the building using a fisheye camera lens and rotator and indoor spaces are then identified using image processing implemented in Python. Relative positions of spaces obtained from CAD (Computer-Assisted Drawing) were used to generate 3D node-relation graphs representing adjacency, connectivity, and accessibility in the study area. Subspacing is performed to more accurately depict large indoor spaces and actual pedestrian movement. Since the images provide very realistic visualization, the topological relationships were used to link them to produce an indoor virtual tour.

Keywords

References

  1. Choi, J. W., Kwon, D. Y., Hwang, J. E. and Lertlakkhanakul, J. (2007), Real-time management of spatial information of design: A space-based floor plan representation of buildings, Automation in Construction, Vol. 16, No.4, pp. 449-459. https://doi.org/10.1016/j.autcon.2006.08.003
  2. Claridades, A. R., Villanueva, J. K. and Macatulad, E. (2016), Evacuation simulation in Kalayaan residence hall, UP Diliman using GAMA Simulation Software, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 42, pp. 83-87.
  3. Dao, T. H. D. and Thill, J. C. (2018), Three-dimensional indoor network accessibility auditing for floor plan design, Transactions in GIS, Vol. 22, No. 1, pp. 288-310. https://doi.org/10.1111/tgis.12310
  4. Daum, (2018), Daum Store View , http://map.daum.net/?nil_profile=title&nil_src=local (last date accessed: 22 May 2018).
  5. Diakite, A. A., Zlatanova, S. and Li, K. J. (2017), About the Subdivision of Indoor Spaces in IndoorGML, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 4, pp. 41-48.
  6. Ellul, C. and Haklay, M. (2006), Requirements for Topology in 3D GIS, Transactions in GIS, Vol. 10, No. 2, pp. 157-175. https://doi.org/10.1111/j.1467-9671.2006.00251.x
  7. Fernandez-Carames, C., Serrano, F.J., Moreno, V., Curto, B., Rodriguez-Aragon, J.F. and Alves, R. (2016), A real-time indoor localization approach integrated with a Geographic Information System (GIS), Robotics and Autonomous Systems, Vol. 75, pp. 475-489. https://doi.org/10.1016/j.robot.2015.08.005
  8. Frossard, P. and Khasanova, R. (2018), Graph-based classification of omnidirectional images, Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017, 22-29 October, Venice, Italy, pp. 860-869.
  9. Giudice, N. A., Walton, L. A. and Worboys, M. (2010), The imformatics of indoor and outdoor space, Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness - ISA '10, 3-5 November, San Jose, USA, pp. 47.
  10. Google, L. (2018a), Google Street View, https://www.google.com/streetview/understand/ (last date accessed: 13 April 2018).
  11. Google, L. (2018b), Google Art Project, https://artsandculture. google.com/ (last date accessed: 13 April 2018).
  12. Gunduz, M., Isikdag, U. and Basaraner, M. (2016), A review of recent research in Indoor modelling & mapping, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 41, pp. 289-294.
  13. Hong, S. C., Jung, J. H., Kim, S. M., Hong, S. H. and Heo, J. (2013), Semi-automatic method for constructing 2D and 3D indoor GIS maps based on point clouds from terrestrial LiDAR, Journal of the Korean Society for Geospatial Information System, pp. 99-105.
  14. Jung, H. J. and Lee, J. (2015), Indoor Subspacing to Implement IndoorGML for Indoor Navigation, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 21, No. 2, pp. 25-27.
  15. Jung, H. J. and Lee, J. (2017), Development of an omnidirectional-based data model through extending the IndoorGML concept to an Indoor Patrol Service, Journal of Sensors, pp. 1-15.
  16. Kang, H. Y., Jung, H. J. and Lee, J. (2015), A study of subspacing strategy for service applications in Indoor Space, Journal of Korea Spatial Information Society, Vol. 23, No. 3, pp. 113-122. https://doi.org/10.12672/ksis.2015.23.3.113
  17. Karas, I. R., Abdul-Rahman, A. and Atila, U. (2011), Automatic generation of 3D networks in CityGML via MUSCLE model, Joint ISG (2011) and ISPRS (2011) Conference, 27-29 September, Selangor, Malaysia, pp. 3.
  18. Khan, A. A. and Kolbe, T. H. (2012), Constraints and their role in Subspacing for the Locomotion types in Indoor Navigation, 2012 International Conference on Indoor Positioning and Indoor Navigation(IPIN)-2012, 13-15 November, Sydney, Australia, pp. 1-2.
  19. Kim, M. and Lee, J. (2015), Developing a method to generate IndoorGML data form the omni-directional image, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 40, pp. 17-19.
  20. Klepeis, N. E., Nelson, W. C., Ott, W. R., Robinson, J. P., Tsang, A. M., Switzer, P., Behar, J. V. and Hern, S. C. (2001), The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants, Journal of Exposure Analysis and Environmental Epidemiology, Vol. 11, No. 3, pp. 231-252. https://doi.org/10.1038/sj.jea.7500165
  21. Kolor. (2018), Panorour, http://www.kolor.com/wiki=en/action/view/Panotour_-_FAQ_-_Building_the_tour_and_the_software_itself (last date accessed: 22 May 2018).
  22. Kruminaite, M. and Zlatanova, S. (2014), Indoor Space Subdivision for Indoor Navigation, Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness-2014, 4-7 November, Dallas/Fort Worth, USA, pp. 25-31.
  23. Kwan, M. P. and Lee, J. (2005), Emergency response after 9/11: The potential of Real-Time 3D GIS for quick emergency response in Micro-Spatial Environments, Computers, Environment and Urban Systems, Vol. 29, No. 2, pp. 93-113. https://doi.org/10.1016/j.compenvurbsys.2003.08.002
  24. Lee, J. (2004), A Spatial Access-Oriented Implementation of a 3-D GIS Topological Data Model for urban entities. Geoinformatica, Vol. 8, No. 3, pp. 1-7.
  25. Lee, J. and Kwan, M. P. (2005), A Combinatorial Data Model for Representing Topological Relations among 3D Geographical Features in Micro-Spatial Environment, International Journal of Geographical Information Science, Vol. 19, No. 10, pp. 1039-1056. https://doi.org/10.1080/13658810500399043
  26. Lee, K. J. and Lee, J. (2013), A Geocoding method on character matching in Indoor Spaces. Journal of Korea Spatial Information Society, Vol. 21, No. 1, pp. 87-100. https://doi.org/10.12672/ksis.2013.21.1.087
  27. Li, K. J., Lee, J., Zlatanova, S., Kolbe, T. H., Nagel, C. and Becker, T. (2015), Open Geospatial Consortium., OGC(R) IndoorGML., OGC, Wayland, MA, USA, pp. 1-17.
  28. Macatulad, E. (2016), 3DGIS-based Multi-agent Geosimulation Model for Building Evacuation, Master's Thesis, University of the Philippines - Diliman, Philippines, 170p.
  29. MacGregor Smith, J. (1991), State-dependent Queueing models in Emergency Evacuation Networks, Transportation Research Part B: Methodological, Vol. 25, No. 6, pp. 373-389. https://doi.org/10.1016/0191-2615(91)90031-D
  30. Roe, G. V., O'Banion, M. S. and Olsen, M. J. (2016), Mobile Lidar guidelines to Support Utility Asset Management along highways, Proceedings of the Pipelines 2016 Conference-2016, 17-20 July, Kansas City, USA, pp. 922-932.
  31. Stoter, J. and Zlatanova, S. (2003), 3D GIS, where are we standing?, ISPRS Joint Workshop on'Spatial, Temporal and multi-dimensional data modelling and analysis'-2003, 2-3 October, Quebec, Canada ,pp. 8.
  32. Tang, S. J., Zhu, Q., Wang, W. W. and Zhang, Y. T. (2015), Automatic Topology Derivation from IFC building model for Indoor Intelligent Navigation, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 40, No. 4, pp. 7-11.
  33. Worboys, M. (2011), Modeling Indoor Space, Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness-2011, 1-4 November, Chicago, USA, pp. 1-6.
  34. Xiong, Q., Zhu, Q., Du, Z., Zlatanova, S., Zhang, Y., Zhou, Y. and Li, Y. (2017), Free Multi-floor Indoor Space Extraction from Complex 3D Building Models, Earth Science Informatics, Vol. 10, No. 1, pp.69-83. https://doi.org/10.1007/s12145-016-0279-x
  35. Zlatanova, S., Rahman, A. A. and Pilouk, M. (2002), 3D GIS: Current Status and Perspectives, International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, Vol. 34, No. 4, pp.66-71.

Cited by

  1. Integrating Image and Network-Based Topological Data through Spatial Data Fusion for Indoor Location-Based Services vol.2020, pp.None, 2018, https://doi.org/10.1155/2020/8877739
  2. 포인트 클라우드를 이용한 IndoorGML 데이터의 자동적 구축 vol.38, pp.6, 2020, https://doi.org/10.7848/ksgpc.2020.38.6.611