1 / 19

Automating Navigation Model Generation from Existing Building Data: The INSM Approach

This presentation by Liu Liu and Sisi Zlatanova explores the challenges and methodologies of generating indoor navigation models from existing building data. Focusing on geometric, logical, and semantic models, it addresses the complexities of indoor navigation, including space subdivision and automatic generation processes. Key workflows demonstrate utilizing CityGML data and methods for constructing logical models based on original building geometry. The goal is to enhance semantic connections, streamline indoor navigation, and provide a framework for future advancements in navigation model generation.

lerato
Télécharger la présentation

Automating Navigation Model Generation from Existing Building Data: The INSM Approach

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Generating Navigation Models From Existing Building Data Liu Liu, Sisi Zlatanova Presenter: Liu Liu

  2. Content • Background • Prerequisites • Workflow • Examples • Summary

  3. 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

  4. 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

  5. Indoor Navigation Semantic Model (INSM)

  6. 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

  7. 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.

  8. Workflow

  9. Examples of CityGML Data • Data source: Reconstruction from 2D drawing (Marcus Goetz, IndoorOSM project)

  10. Re-constructed CityGML File Door Stair

  11. Standard CityGML File

  12. Derive Logical Model from CityGML LoD4

  13. 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.

  14. Generating Logical Model

  15. Examples of Floor Plans

  16. 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.

  17. 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

  18. Outlook • Semantic annotations – to be standardized • Semantic models – real data would follow the suggested structure • CityGML should explicitly provide connections between floors.

  19. Thank you! & Questions?

More Related