Table of Contents
Overview
Landmark Navigation
- IndoorGML - Indoor Geographic Markup Language
- Data Flow
- Solvers
- IndoorGML - Indoor Geographic Markup Language
Indoor Map Representation
Path Planning
Simulation and Results
References
Overview
Landmark Navigation
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.
Indoor Map Representation
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.