1 Challenge the future Automatic extraction of Improved Geometrical Network Model from CityGML for Indoor Navigation Filippo Mortari 2 Challenge the future Introduction to the problem What is the motivation for indoor navigation? 3 Challenge the future Introduction to the problem A navigation system comprises: Guidance along the path The determination of the position of a subject or object The determination of the best path from a start to a destination (best in the sense of the shortest, the fastest, or the cheapest) 4 Challenge the future Introduction to the problem Best path algorithms quite often refer to graph theory The creation of a good network and data model is essential for deriving the overall connectivity of a building and representing position of objects within the environment The scope of this research deals with automatic generation of connectivity graphs from semantically rich 3D building models for indoor navigation 5 Challenge the future Introduction to the problem Weaknesses of existing models Motivation They support only one type of locomotion Obstacles are not taken into consideration Granularity is too coarse They ignore semantics/architectural characteristics 6 Challenge the future Introduction to the problem Derivation of networks: strenghts and weaknesses of Medial Axis Transform 7 Challenge the future Introduction to the problem Visibility Graph approach: strenghts and weaknesses 8 Challenge the future Introduction to the problem A novel approach... Medial Axis Transform My approach Visibility graph 9 Challenge the future Introduction to the problem Motivation: it has not been found any approach that leads to automatic derivation of 2D and 3D GNM out of 3D digital models of complex buildings CityGML LOD4. How can it be beneficial to Indoor Navigation? 10 Challenge the future Research objective Is it possible to automatically extract an Improved Geometrical Network Model from CityGML LOD4 for Indoor Navigation? How indoor geometrical properties of buildings can be retrieved from CityGML LOD4 datasets? What is an Improved Geometrical Network Model? In other words what are the improvements we want to perform in order to achieve a richer connectivity graph? What are the semantic properties that can be extracted from CityGML that could be meaningful to this improvement? Is it possible to implement an automatic network extraction process? What could be the geometrical operations needed to perform network extraction? 11 Challenge the future Methodology The research process can be conceptually split into three components: Extraction of semantics and geometry information from CityGML Design and implementation of the data model Design and implementation of algorithms for network extraction 12 Challenge the future Methodology CityGML LOD4 datasets CityGML brief overview Definition of Data Model Design and Implementation of Algorithms for Network Extraction 2D floor plan data Tests and validation CityGML geometry and semantics extraction Linking layer implementation Tests and validation Automatic GNM generation 13 Challenge the future CityGML: Extraction of semantics and geometry information How indoor geometry and semantics of buildings can be retrieved from CityGML LOD4 datasets? 14 Challenge the future CityGML Concept of walkable drivable surface - floorplans CityGML Building Module 15 Challenge the future CityGML Concept of walkable drivable surface - floorplans CityGML Building Module 16 Challenge the future CityGML Concept of walkable drivable surface - floorplans CityGML Building Module 17 Challenge the future CityGML Concept of walkable drivable surface - floorplans 18 Challenge the future CityGML Connectivity between spaces 19 Challenge the future CityGML Concept of connectivity between spaces CityGML Building Module 20 Challenge the future CityGML Vertical spaces Ramp Escalator StairsElevator 21 Challenge the future CityGML Vertical spaces Code Lists 22 Challenge the future CityGML Obstacles: fixed and removable 23 Challenge the future CityGML CityGML Building Module Concept of Non-navigable Space 24 Challenge the future CityGML All of these informations extracted by a parser... 25 Challenge the future The definition of the Data Model An extension of IndoorGML 26 Challenge the future The definition of the Data Model represent and exchange the geoinformation that is required to build and operate indoor navigation systems. IndoorGML will provide the essential model and data for important applications like building evacuation, disaster management, personal indoor navigation, indoor robot navigation, indoor spatial awareness, indoor location based services, the support for tracking of people and goods IndoorGML 27 Challenge the future The definition of the Data Model IndoorGML 28 Challenge the future The definition of the Data Model IndoorGML Indoor space mapped to IndoorNavigation module classes 29 Challenge the future The definition of the Data Model How IndoorGML has been extended 30 Challenge the future The automatic network extraction process Concepts behind the formalization of the algorithms 31 Challenge the future The proposed method 1. Inward offsetting 32 Challenge the future The proposed method 1. Inward offsetting 33 Challenge the future The proposed method 2. Boundary sampling 34 Challenge the future The proposed method 3. Constrained Delaunay Triangulation 35 Challenge the future The proposed method 4. Limit to the domain 36 Challenge the future The proposed method 5. Subspace facets that have no constrained edges 37 Challenge the future The proposed method 6. Displace nodes according to some criteria 38 Challenge the future The proposed method 6. Displace nodes according to some criteria 39 Challenge the future The proposed method Regarding the datasets... 40 Challenge the future Conclusions 41 Challenge the future It has been a pleasure... Thank you for the attention, the hospitality, the patience, the words, the company, the support. 42 Challenge the future Questions?