Landmarks are distinct features that a mobile platform or robot can recognize from its sensory input. Landmarks can be geometric shapes (e.g., rectangles, lines, circles), and they may include additional information (e.g., in the form of bar-codes). In general, landmarks have a fixed and known position, relative to which a robot can localize itself. Landmarks are carefully chosen to be easy to identify; for example, there must be sufficient contrast to the background. Before a robot can use landmarks for navigation, the characteristics of the landmarks must be known and stored in the robot's memory. The main task in localization is then to recognize the landmarks reliably and to calculate the robot's position.
In order to simplify the problem of landmark acquisition it is often assumed that the current robot position and orientation are known approximately, so that the robot only needs to look for landmarks in a limited area. For this reason good odometry accuracy is a prerequisite for successful landmark detection. The picture below shows the general procedure for performing landmark-based positioning.
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A map model in the context of this report is a digital representation of an indoor environment. By creating a map model of an indoor environment, this will create a simplified manageable view of the environment. A model that will give us a better understanding of elements, description of elements, and predict how those elements will behave and interact with one another.
The application for this model will enable mobile platforms to achieve real-time indoor navigation. The complexity of an indoor environment ranges from each application, therefore to capture all the aspects of an indoor environment we need to structure the map model representation for a specific application.
Indoor Geographical Markup Language (IndoorGML) is a standard created by the Open Geospatial Consortium (OGC) to represent and exchange geographical information, which is to build and operate indoor navigation systems. Indoor navigation comprises of route planning, localization, and tracking of subjects (i.e. people) and objects (e.g. robots or other indoor vehicles). In accordance to our application the system will incorporate a cellular space model, landmark positioning, indoor localization, and representation of mobile agents. As an established standard the focus will be using IndoorGML to create the virtual indoor environment that will achieve the desired map model representation for navigation.
Cellular space defined as an area that is divided into cells where each particular cell has information for navigation. Cellular space has three significant properties. First, a symbolic code or a cell identifier such as a room number represents each cell. Furthermore, every cell shares a common boundary amongst other cells that never overlap. Finally, cellular space employs an (x, y, z) coordinate system to determine a precise location.
Example Indoor Environment:
In IndoorGML representation, connectivity regarding the possibility to navigate through cells is primarily derived from the semantics and topology of cells. Semantics allows for encoding characteristic information about each cell. It is used to classify cells to provide an identity for better interpretation along with using the topology to determine the connection level with other cells. An example of this would be doors, elevators, rooms, walls; all would fall into a topological layer where each cell is aware of the other cells around it to make up an entire floor plan.
It is also possible to set navigation constraints by generating options between navigable and non-navigable paths.
IndoorGML contains 5 (five) different layers:
L1 – TOPOGRAPHIC - Geometry | L2 - TOPOGRAPHIC - Navigation | L3 - SENSOR - Camera | L4 - SENSOR - Localization | L5 - TAGS - Semantic |
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| Design | Generated MAP (Isometric View) | Example of GML file |
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1 U.S. Government. National Space-Based Positioning, Navigation, and Timing Coordination Office’s GPS information, 2010.
2 Open Geospatial Consortium, Inc. GML Documentation, 2010.
3 Open Geospatial Consortium, Inc. KML Documentation, 2010.
4 Open Geospatial Consortium, Inc. IndoorGML Documentation, 2014.
5 OpenStreetMap. OpenStreetMap, 2010.
