- freely available
ISPRS International Journal of Geo-Information 2017, 6(4), 116; doi:10.3390/ijgi6040116
2. Related Work on Indoor Spatial Data Models and Standards
2.1. Indoor Spatial Data Models
2.2. Standards for Indoor Spatial Information
3. Requirements for Indoor Spatial Data Models
3.1. Indoor Distance
3.2. Complex Structures of Indoor Space
3.3. Cell-Based Context Awareness
3.4. Integrating Multiple Datasets
4. Basic Concepts of IndoorGML
4.1. Cellular Space Model
- ⋃ and
- each cell c has its cell identifier .
4.2. Cell Geometry
- Option 1, no geometry: The first option is to exclude any geometric properties from IndoorGML data and to include only topological relationships between cells, which will be explained in the next subsection.
- Option 2, geometry in IndoorGML: The second option is to represent its geometry within IndoorGML data by geometric types defined in ISO 19107. For example, the three-dimensional geometry of a cell is defined as a solid of ISO 19107. Note that the geometry of the cell is an open primitive as defined in ISO 19107, which means that the boundary of the cell geometry does not belong to the cell. This definition is consistent with the non-overlapping condition of the cellular space defined in Section 4.1.
- Option 3, external reference: The third option is to include external references to the object in another dataset that contains geometric data. For example, a cell in IndoorGML data only points to an object in CityGML via the GML identifier that contains geometric properties.
4.3. Topology between Cells
4.4. Cell Semantics
4.5. Multi-Layered Space Model
4.6. Modular Structure of IndoorGML
4.7. Implementation of IndoorGML Core Module
5. Cell Determination in IndoorGML
5.1. Cell Determination and Subspacing
- different properties: if a space has different properties such as kitchen area and living room, it is desirable to partition it into two cells with virtual boundaries.
- big space as a cell: if a space is too big, like a long hall-wall or a big convention hall, it is recommended to split it into smaller subspaces.
- sensor coverages: it is also possible to divide a space in terms of sensor coverage, such as CCTV viewshed or WiFi and RFID coverages .
- cell without spatial extent: while cells have spatial extents in most cases, there are also cases where no spatial extent is necessarily required except a point. For example, each image spot in a panoramic image service shown in Figure 13 is represented as a cell without spatial extent except a point. Note that the panorama spot image layer is defined as a separate space layer of IndoorGML, and we define inter-layer connections with the cells in the topographic layer. We also assume that each navigation arrow connecting two image spots is considered as an edge in the connectivity graph for the panorama spot image layer, as in Figure 13.
5.2. Thick-Wall Model vs. Thin-Wall Model
5.3. Representing Hierarchical Structures
- , and
6. Computing Indoor Distance Using IndoorGML
6.1. Horizontal Distance
|Algorithm 1. Indoor_Distance ()|
|- topographic layer C: set of indoor cells with cell geometry,|
|- door-to-door (D2D) layer graph ,|
|- multi-layered space model graph , and|
|- starting and ending points|
|Output: horizontal indoor distance|
|1. the cell containing point p;|
|2. the cell containing point q;|
|3. where L is the set of inter-layer connections;|
|7. is the shortest path from to from D2D layer graph;|
|8. where , and ;|
|9. return ;|
6.2. Vertical Distance
6.3. Multi-Modal Distance
7. Context-Awareness by IndoorGML
- Step 1: indoor map matching:
- Step 2: context reasoning from staying interval:
- Step 3: context reasoning from visit sequence:
7.1. Indoor Map Matching by IndoorGML
|Algorithm 2. Indoor_Map_Matching_From_Point ()|
|- topographic layer C: set of indoor cells with cell geometry,|
|- accessibility graph ,|
|- current point and past trajectory , and|
|- sensor readings S.|
|Output: current cell c|
|1. Find containing ;|
|2. Correct by analyzing the past trajectory and accessibility graph ;|
|3. Improve by analyzing other sensor readings;|
|4. return c;|
7.2. Context Reasoning from the Staying and Visit Sequence
Conflicts of Interest
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