IndoorML: A Data Model and Exchange Format for Indoor Navigation
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1 Technische Universität Berlin IndoorML: A Data Model and Exchange Format for Indoor Navigation Thomas H. Kolbe Director Institute for Geodesy and Geoinformation Science Berlin University of Technology kolbe@igg.tu-berlin.de 29th of September 2009 OGC TC Meeting at Fraunhofer IGD, Darmstadt
2 Indoor Navigation Vision: Illustration: Project u-gis: Indoor Spatial Awareness, Korea 2 T. H. Kolbe IndoorML for Indoor Navigation
3 Applications for Indoor Navigation Emergency and Disaster Management escape route planning, simulation of evacuations guidance of rescue personell in buildings (Activity) Tracking of persons (e.g. rescue personnel) and objects (e.g. goods, material, or robots) Navigation in public buildings like airports, museums, administrations, shopping malls (check in) counters, exits, toilets, police station, exhibits, offices generally: Location Based Services 3 T. H. Kolbe IndoorML for Indoor Navigation
4 Navigation Navigation comprises Determination of position and (usually) orientation communication of position needs geoinformation (typ. maps) Addressing and Route Planning requires geoinformation about the navigable requires addressing / georeferencing schema (naming of locations): coordinate reference systems and/or textual Route Tracking (Homing) alignment actual position target position motion control to reduce the distance, i.e. keeping on track communication of navigation commands 4 T. H. Kolbe IndoorML for Indoor Navigation
5 What does a navigation system needs? Localization method / technology Geoinformation about the navigable navigable route sections list of localities (named places and their coordinates) Spatial reference systems (nowadays typically two!). spatial reference system of the localization method (often locale or world coordinate system) 2. natural spatial reference system of the user (for naming of start and end points; often not a coordinate system but e.g. addresses, Points of Interest) Methods for position and route communication 5 T. H. Kolbe IndoorML for Indoor Navigation
6 Robot navigation Route planning geometric route planning, i.e. the exact trajectory of the robot has to be computed requires topological and geometric information about the navigable, at least 2D floor plans / footprints Localization typically uses (laser / ultrasonic) range finders or photogrammetric / computer vision methods for localization often Simultaneous Localisation and Mapping (SLAM) is applied; nowadays 3D models are used 6 T. H. Kolbe IndoorML for Indoor Navigation Picture: CS Dept. III Univ. of Bonn
7 Robot navigation Route planning geometric route planning, i.e. the exact trajectory of the robot has to be Mode computed of Locomotion: Flying! requires topological and geometric information about the navigable, at least 2D floor plans / footprints Localization typically uses (laser / ultrasonic) range finders or photogrammetric / computer vision methods for localization often Simultaneous Localisation and Image: Mapping Microdrones (SLAM) is applied; nowadays 3D models are used 7 T. H. Kolbe IndoorML for Indoor Navigation Picture: CS Dept. III Univ. of Bonn
8 Geodata for Indoor Navigation 8 T. H. Kolbe IndoorML for Indoor Navigation
9 Problems of Indoor Navigation Different, varying localisation methods nevertheless: sensors are different, but most have comparable spatial characteristics (visibility area, coverage area, signal propagation). Absolute position can be determined, if the locality of a sensor or sender and its covered area is known uncertainty is equal to the size of the respective area Route network and addressing principle for navigation targets required (coordinates are unsuited) Wanted: model for localisation + route planning 9 T. H. Kolbe IndoorML for Indoor Navigation
10 The Project Indoor Spatial Awareness Indoor Navigation, Simulations, Applications funded by the ministry of transport of South Korea Partners: Ki-Joune Li, Pusan National Univ., Korea Jyeong Lee, Univ. of Seoul, Korea Mike Worboys, Univ. of Maine, USA Christian S. Jensen, Aalborg Univ., Denmark Research goals at TU Berlin: Creation of a model that integrates the notions of Euclidean and cellular as well as + activity Development of a data model comprising both 3D topography (of buildings) and the spatial characteristics of all sensors Mapping to an exchange format: IndoorML 0 T. H. Kolbe IndoorML for Indoor Navigation
11 Modeling of Navigable Indoor Spaces Semantic 3D city and building models provide (nowadays) the geometry and a thematic differentiation of the indoor areas (at least separation in building parts, storeys and rooms) International standards CityGML and IFC Thematic differentiation already suitable for addressing, route descriptions and route tracking (homing) e.g. by room numbers T. H. Kolbe IndoorML for Indoor Navigation
12 Representation of s within buildings Interiors will be represented in Euclidean as volumes (3D IR³) by using the boundary representation: Euclidean is a metric and induces a natural topology. Adjacency; Interior, exterior, boundary Geometrical-topological modeling of volumes by cell complexes 3-cell: Solid 2-cell: Face -cell: edge 0-cell: node 2 T. H. Kolbe IndoorML for Indoor Navigation
13 Poincaré Duality (Algebraic Topology) For a compact, orientiented manifold the i-th homology group is isomorphic to the (n-i)-th cohomology. Mapping of i-dimensional cells within cell complexes in primal onto (n-i) cells in dual Isomorphism preserves topological properties Example: Primal Space: Cell complex consisting of two volumes (e.g. rooms within building) and their bounding faces, edges, and nodes Dual Space: Volume (3D) mapped onto node (0D), face (2D) onto edge (D) Poincaré Duality 3 T. H. Kolbe IndoorML for Indoor Navigation
![Example [from Lee 2004] 3D Building Model Primal](/docs-images/76/74038761/images/14-0.jpg "Space 3D Connectivity Graph Dual Space 3D")
14 Example [from Lee 2004] 3D Building Model Primal Space 3D Connectivity Graph Dual Space 3D Node-relation-structure (NRS) 3D NRS is a topologic structure with (geometric) embedding in IR³ facilitates determination of shortest paths in the network 4 T. H. Kolbe IndoorML for Indoor Navigation
15 Structured Space Model Geometrical (IR³) Topological The size of the cells determines ambiguity of the absolute position Skeletonization 3D buildings algebr. topology of buildings models Poincaré-Duality Primal Dual Node-Relation-Structure [Lee 2004] 5 T. H. Kolbe IndoorML for Indoor Navigation
16 Multilayered Space Model Sensor Space model Further Layer Primal Physical Topographic Space model Alternative Space concepts Dual State + Events Geometry Topology 6 T. H. Kolbe IndoorML for Indoor Navigation
17 Multilayered Space Model st layer: Topographic model modeling of the building s structure (topography) Primal : geometric-topological model Dual : network for route planning 2nd layer: Sensor model Modeling of sensor / transmitter structure Primal : coverage of sensor areas Dual : transition between sensor areas Further layers: i.e. further sensor model 7 T. H. Kolbe IndoorML for Indoor Navigation
18 st Layer: Topographic Space Geometry in IR³ Topology 3D building models GM_Solid ISO 907 TP_Solid Primal Events: movement into an another room ISO 907 Dual Euclidean embedding of NRS Dual graph 8 T. H. Kolbe IndoorML for Indoor Navigation
19 Example for Topographic Space (I) Primal, geometric model: Dual, topological model: Rooms and their adjacencies resp. state transition graph (presence in a room and changes of rooms) Planned route (e.g. an escape route) Traced route 9 T. H. Kolbe IndoorML for Indoor Navigation
20 Example for Topographic Space (II) Topographic primal as 3D Solids Rooms are connected via doors Topographic dual as connectivity graph Rooms are connected via edges 20 T. H. Kolbe IndoorML for Indoor Navigation
21 Example for Topographic Space (II) Dual to primal connection Affected s State-transition diagram Rooms and their adjacencies resp. state transition graph (presence in a room and changes of rooms) Traced route 2 T. H. Kolbe IndoorML for Indoor Navigation
22 Relation of Space and Event If s are represented in a non-overlapping way within a model, then: a subject or object must be exactly in one cell the model dual graph describes a state transition diagram Node area (e.g. room within a building) in primal ; at the same time state of a subject or object with respect to occupation edge connections, adjacencies (e.g. doors, passages) in primal ; at the same time event indicating movement / transition from one area into another 22 T. H. Kolbe IndoorML for Indoor Navigation
23 2nd Layer: Sensor Space Geometry in IR³ Topology i.e. Wi-Fi coverage area GM_Solids ISO 907 TP_Solids Primal events: change of coverage area or signal strength ISO 907 Dual Euclidean embedding of NRS Dual graph 23 T. H. Kolbe IndoorML for Indoor Navigation
24 Examples for Sensor Space Model Wi-Fi RFID Scanner 24 T. H. Kolbe IndoorML for Indoor Navigation
25 Examples for Sensor Space Model overlapping Wi-Fi reception areas Outer Wi-Fi or RFID- areas without overlapping Outer 25 T. H. Kolbe IndoorML for Indoor Navigation
26 Combination of Space Models (I) Primal Dual Cells in IR Dual graph A AB B BC C 6 5 A AB B BC C Cells in IR 3 A AB Dual graph B C BC 26 T. H. Kolbe IndoorML for Indoor Navigation
27 Combination of Space Models (I) Primal 2 3 Dual 2 Bipartite dual graph Cells in IR Dual graph A AB B BC C 6 5 A AB B BC C Cells in IR 3 A AB Dual graph B C BC AB B 3 5 BC C 27 T. H. Kolbe IndoorML for Indoor Navigation
28 Combination of Space Models (II) Sensor model Topographic model Route planning Tracking / Localization AB B Connection of dual graphs of several layer in one multi-layered graph the dashed edges express which states can be mutually active within the different models A BC C 28 T. H. Kolbe IndoorML for Indoor Navigation
29 Formal Background of the Multi-Layered Graph Definition using topology: Partitioning of nodes according to their membership to the different models n models n sets of nodes A AB B BC C 6 5 Edges within each partition express topologic adjacency (3D cells have a common face) Edges between nodes from different partitions express topological overlaps (intersection of the interiors of the corresponding cells is not empty) A AB B 3 5 BC C 29 T. H. Kolbe IndoorML for Indoor Navigation
30 Example for a Multi-Layered Graph 30 T. H. Kolbe IndoorML for Indoor Navigation
31 Joint State Every dual graph of a model describes a state transition diagram If the partition of the is non-overlapping, only one state is active at one point in time The existing joint states of several models are represented in just one multi-layered graph The joint state of navigation is given by the simultaneously active states of all models. Combinations are constrained by the edges of the multi-layered graph. Every clique of size n, where each node comes from a different model, describes a possible joint state of navigation. 3 T. H. Kolbe IndoorML for Indoor Navigation
32 Example for Joint State Outer Rooms and their adjacencies within the topographic model RFID scanner coverage areas and their adjacencies within the sensor model Joint state 32 T. H. Kolbe IndoorML for Indoor Navigation
33 Example for Using the Joint State multilayered model (primal) topographic (blue) additional cell (cyan) lower security zones (green) high security zones (orange) Wi-Fi transmitter (light red) 33 T. H. Kolbe IndoorML for Indoor Navigation
34 Example for Using the Joint State multilayered model (primal) topographic (blue) additional cell (cyan) lower security zones (green) high security zones (orange) Wi-Fi transmitter (light red) 34 T. H. Kolbe IndoorML for Indoor Navigation
35 Example for Using the Joint State multilayered model (primal) topographic (blue) additional cell (cyan) lower security zones (green) high security zones (orange) Wi-Fi transmitter (light red) multilayered model (dual) topographic (blue) additional cell (cyan) lower security zones (green) high security zones (orange) Wi-Fi transmitter (light red) additional: joint states between the layer 35 T. H. Kolbe IndoorML for Indoor Navigation
36 Example for Using the Joint State multilayered model (primal) topographic (blue) additional cell (cyan) lower security zones (green) high security zones (orange) Wi-Fi transmitter (light red) multilayered model (dual) topographic (blue) additional cell (cyan) lower security zones (green) high security zones (orange) Wi-Fi transmitter (light red) additional: joint states between the layer 36 T. H. Kolbe IndoorML for Indoor Navigation
37 Position Determination using the Joint State R I Outer R II 2 3 Outer AB B 2 4 A AB B BC C R 6 R2 5 A C BC The joint state of navigation is given by the synchronously active states of all models. R I, State 6 and State A constitute a clique of Inter-Space connections, i.e. they overlap in primal 37 T. H. Kolbe IndoorML for Indoor Navigation
38 Position Determination using the Joint State R I Outer R II 2 3 Outer AB B 2 4 A AB B BC C R 6 R2 5 A C BC The joint state of navigation Intersecting is given geometries by the synchronously active states of all models. R AI, State 6 and State A constitute a clique of Inter-Space connections, i.e. they overlap in primal RI Overlaid s 38 T. H. Kolbe IndoorML for Indoor Navigation 6 actual position area in topogr. Space
39 Summary of Multilayered Space Model The multilayered model allows for distinct spatial decompositions with respect to different semantics e.g. topography, sensors changes (e.g. modifications, installation of new sensors) within one model do not influence data within other models The multilayered graph enables the propagation of events between several models i.e. when moving into an another sensor area, the possible locations in topographic can be constrained the joint state reduces the uncertainty of the absolute position of an object or subject 39 T. H. Kolbe IndoorML for Indoor Navigation
40 Context of Navigation three main factors constitute the context of navigation Supporting contexts using a layer for each within the multilayered model impact impact Mode of Locomotion Walking, Flying, Driving (robot, wheel chair) impact impact Localization techniques and infrastructure Wi-Fi, RFID, Noise Sensors, Cell Phones impact 40 T. H. Kolbe IndoorML for Indoor Navigation Logical contexts representing pre- knowledge or navigation constraints
41 Mapping Context to the Multilayered Graph Spaces within a model may be subdivided due to specific considerations e.g. by the mode of locomotion each specific consideration or context leads to an new layer within the model but they are not independent of the higher level layer therefore the inter- connections can be topological qualified as contains or equals Toplevel layer Subs Is-A Is-A 4 T. H. Kolbe IndoorML for Indoor Navigation
42 Subspacing wrt. Mode of Locomotion staircase staircase Use of Staircase and a part of the floor is prohibited in case of a wheel chair driver equals contains contains 42 T. H. Kolbe IndoorML for Indoor Navigation
43 Context Handling for Navigation Scenarios DB Storage of n- partite Graph 43 T. H. Kolbe IndoorML for Indoor Navigation
44 Context Handling for Navigation Scenarios Subsetting of the n partite graph Selection A R AB R2 B DB Storage of n- partite Graph Determination of possible Joint states 44 T. H. Kolbe IndoorML for Indoor Navigation
45 IndoorML - Data Model based on the ISO 9xx standards family and mapped to GML topoface toposolid 0 <<Feature>> Space..2 Boundary 0..* <<Feature>> SpaceBoundary primal dual 0.. <<Geometry>> GM_Solid realization <<Geometry>> GM_OrientableSurface 0.. realization..* <<Topology>> TP_DirectedFace Poincare - duality boundedby <<Topology>> TP_Solid 2 topoedge <<Topology>> TP_Face 0....* transition <<Feature>> Transition..* transition 0..* 0..* 0..* <<Feature>> SpaceLayer Boundary Boundary * * <<Feature>> TypeOfRelation realtionto <<Geometry>> GM_Point <<Geometry>> GM_Curve 0.. realization..* <<Topology>> TP_Edge boundedby 2 <<Topology>> TP_Node -layername : string -layerclassifier : layerclassifier LayerMember realization 2 transition state state <<enumeration>> layerclassifier +topographic +sensor +logical +unknown <<enumeration>> TopoExpression +contains +equals +overlap +unknown Geometry Topology topoedge 0.. toponode state <<Feature>> State..* interspaceconnection <<Feature>> InterSpaceConnection interspaceconnectionmember +comment : string -typeoftopoexpression : TopoExpression 2 0..* state <<Feature>> n-partite-graph 45 T. H. Kolbe IndoorML for Indoor Navigation
46 IndoorML topoface toposolid 0 <<Feature>> Space..2 Boundary 0..* <<Feature>> SpaceBoundary primal dual 0.. <<Geometry>> GM_Solid realization <<Geometry>> GM_OrientableSurface 0.. realization..* <<Topology>> TP_DirectedFace Poincare - duality boundedby <<Topology>> TP_Solid 2 topoedge <<Topology>> TP_Face 0....* transition <<Feature>> Transition..* transition 0..* 0..* 0..* <<Feature>> SpaceLayer Boundary Boundary * * <<Feature>> TypeOfRelation realtionto <<Geometry>> GM_Point <<Geometry>> GM_Curve 0.. realization..* <<Topology>> TP_Edge boundedby 2 <<Topology>> TP_Node -layername : string -layerclassifier : layerclassifier LayerMember realization 2 transition state state <<enumeration>> layerclassifier +topographic +sensor +logical +unknown <<enumeration>> TopoExpression +contains +equals +overlap +unknown Geometry Topology topoedge 0.. toponode state <<Feature>> State..* interspaceconnection <<Feature>> InterSpaceConnection interspaceconnectionmember +comment : string -typeoftopoexpression : TopoExpression 2 0..* state <<Feature>> n-partite-graph 46 T. H. Kolbe IndoorML for Indoor Navigation
47 Linking IndoorML with CityGML CityGML IFC Maybe other primal dual <<Geometry>> GM_Solid <<Geometry>> GM_Point <<enumeration>> layerclassifier +topographic +sensor +logical +unknown 0.. <<Geometry>> GM_OrientableSurface <<Geometry>> GM_Curve <<enumeration>> TopoExpression +contains +equals +overlap +unknown realization realization realization Geometry <<Topology>> TP_DirectedFace Poincare - duality Topology <<Topology>> TP_Edge <<Topology>> TP_Solid realization <<Topology>> TP_Face <<Topology>> TP_Node 47 T. H. Kolbe IndoorML for Indoor Navigation..*..* boundedby 2 topoedge topoedge boundedby 0.. toposolid 2 topoface toponode <<Feature>> Space <<Feature>> InterSpaceConnection +comment : string -typeoftopoexpression : TopoExpression state state..* <<Feature>> Transition <<Feature>> State..* transition..* transition 2 2 state 0..*..2 Boundary 0..* interspaceconnection 0..* 0..* <<Feature>> SpaceBoundary <<Feature>> SpaceLayer -layername : string -layerclassifier : layerclassifier transition state 0..* Boundary interspaceconnectionmember Boundary * * <<Feature>> TypeOfRelation realtionto <<Feature>> n-partite-graph LayerMember
48 Example WiFi RFID Low Security Zone High Security Zone Topographic Subs Main Topographic Layer 48 T. H. Kolbe IndoorML for Indoor Navigation
49 Example (II) Low security zone High security zone <transition> <Transition gml:id="transition_5"> <topoedge> <gml:edge> <gml:directednode xlink:href="#node_"/> <gml:directednode xlink:href="#outernode"/> <gml:curveproperty> <gml:linestring> <gml:poslist> </gml:poslist> </gml:linestring> </gml:curveproperty> </gml:edge> </topoedge> </Transition> </transition> 49 T. H. Kolbe IndoorML for Indoor Navigation OUTER Intra- connections must have a topological and may have a geometrical Embedding!
50 IndoorML: Review of Navigation Requirements coordinate system / addresses points of interest can be derived from Geoinformation about the navigable Geometry in IR³ GM_Solid ISO 907 Topology TP_Solid Primal Localization is now possible by using the sensor layers and the joint states Geometry in IR³ GM_Solids Euclidean embedding of NRS ISO 907 ISO 907 Topology TP_Solids Dual graph Primal Dual GM_Solids Geometry in IR³ Euclidean embedding of NRS ISO 907 ISO 907 Topology TP_Solids Dual graph navigable route sections Primal route network is required and now available Dual Geometry in IR³ GM_Solid ISO 907 Topology TP_Solid Primal Euclidean embedding of NRS ISO 907 Geometry in IR³ GM_Solids Dual graph Dual ISO 907 Topology TP_Solids Primal Euclidean embedding of NRS ISO 907 Dual graph Dual Euclidean embedding of NRS ISO 907 Dual graph Dual Geoinformation about the navigable is available 50 T. H. Kolbe IndoorML for Indoor Navigation
51 Summary and Conclusions Indoor navigation requires 3D models and 3D data for route planning and addressing for position determination of persons or objects Multilayered Space Model assesses the combination of different representations important models: topography, sensor (one per sensor type / localisation method) different subspacing of topography wrt. mode of locomotion logical s express navigation constraints / restrictions IndoorML is a data model and exchange format (based on GML) for the representation of the indoor navigation aspects complementary to CityGML, IFC, GDF 5 T. H. Kolbe IndoorML for Indoor Navigation
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