ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4, 45-51, 2014
https://doi.org/10.5194/isprsannals-II-4-45-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
23 Apr 2014
"Improved Geometric Network Model" (IGNM): a novel approach for deriving Connectivity Graphs for Indoor Navigation
F. Mortari1, S. Zlatanova2, L. Liu2, and E. Clementini3 1Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, Via Vetoio, 67100 L'Aquila, Italy
2GIS Technology, OTB Research Institute for the Built Environment, Delft University of Technology, Jaffalaan 9, 2628 BX Delft, The Netherlands
3Department of Industrial and Information Engineering and Economics, University of L'Aquila, Via G. Gronchi 18, 67100 L'Aquila, Italy
Keywords: Indoor navigation, route graphs, Geometric Network Model, Semantic building, path finding, 2D floor plan Abstract. Over the past few years Personal Navigation Systems have become an established tool for route planning, but they are mainly designed for outdoor environments. Indoor navigation is still a challenging research area for several reasons: positioning is not very accurate, users can freely move between the interior boundaries of buildings, path network construction process may not be easy and straightforward due to complexity of indoor space configurations. Therefore the creation of a good network is essential for deriving overall connectivity of a building and for representing position of objects within the environment. This paper reviews current approaches to automatic derivation of route graphs for indoor navigation and discusses some of their limitations. Then, it introduces a novel algorithmic strategy for extracting a 3D connectivity graph for indoor navigation based on 2D floor plans.
Conference paper (PDF, 1454 KB)


Citation: Mortari, F., Zlatanova, S., Liu, L., and Clementini, E.: "Improved Geometric Network Model" (IGNM): a novel approach for deriving Connectivity Graphs for Indoor Navigation, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4, 45-51, https://doi.org/10.5194/isprsannals-II-4-45-2014, 2014.

BibTeX EndNote Reference Manager XML