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Generating Navigation Models From Existing Building Data. Liu Liu, Sisi Zlatanova. Presenter: Liu Liu. Content. Background Prerequisites Workflow Examples Summary. Background. Navigation Model: Geometric models Logical models: e.g. adjacency, connectivity, etc.
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Generating Navigation Models From Existing Building Data Liu Liu, Sisi Zlatanova Presenter: Liu Liu
Content • Background • Prerequisites • Workflow • Examples • Summary
Background • Navigation Model: • Geometric models • Logical models: e.g. adjacency, connectivity, etc. • Semantic models: meaning of building parts • Regularly structured building -- relatively easy. • Difficulties: complex indoor ---- subdivision is required • Subdivision: • Semantic Subdivision, e.g. reception, coffee corner • Geometric Subdivision, e.g. grid, voxel, trangulation
Background • Challenges • automatic space subdivision • different semantics • No algorithms on available building data • Indoor Navigation Space Model (INSM) – original subdivision • INSM facilitates the construction of logical models Navigable Space Cell Opening Horizontal Connector Vertical Unit End
Goal • To generate logical models • from existing building geometry • with the help of INSM • Construct INSM from existing building data • Summarize necessary preparations for automatic generation
Prerequisites: Original Structure • Preserves original indoor structural division as much as possible • Give explicit connection to some special cases. • Topological relations can be detected by geometry comparison • Accessibility of subspaces needs to be provided.
Examples of CityGML Data • Data source: Reconstruction from 2D drawing (Marcus Goetz, IndoorOSM project)
Re-constructed CityGML File Door Stair
Path Pattern End/Start Horizontal Connector Vertical Connector Vertical Unit Horizontal Connector Destination Vertical Connector End – HC – End End --- HC –VC –VU – VC – HC--- End.
Analysis • Semantically rich vs. semantically poor data • for populating INSM and extracting the connectivity network • Manual work? • Vertical building components (CityGML) • Minimum semantics for semantic poor datasets.
Summary • Semantic rich data -- better • Following Standard structure -- better (e.g. CityGML v2.0.0) • Floor plans -- no standard method • Enrich navigation semantics – using INSM
Outlook • Semantic annotations – to be standardized • Semantic models – real data would follow the suggested structure • CityGML should explicitly provide connections between floors.
