In order to solve most of the existing mobile robotics applications, the robot needs some information about its spatial environment encoded in what it has been commonly called a map. The knowledge contained in such a map, whichever the approach used to obtain it, will mainly be used by the robot to have the ability to navigate in a given environment, that is, to reach any goal from any start point while avoiding static and dynamic obstacles. We are describing in this paper a method that allows a robot or team of robots to navigate in large urban areas for which an existing map in a standard human understandable fashion is available. As detailed maps of most urban areas already exist, it will be assumed that a map of the zone where the robot is supposed to work into is given which has not been constructed using the robot's own sensors. We propose in this paper the use of an existing Geographical Information System based map of an urban zone so that a robot or a team of robots can connect to this map and use it to navigation purposes. Details of the implemented system architecture as well as a position tracking experiment in a real outdoor environment, a University Campus, are provided.

The final publication is available at Springer via http://dx.doi.org/10.1007/s40571-016-0154-zA methodology to integrate geographical information system (GIS) data with large-scale pedestrian simulations has been developed. Advances in automatic data acquisition and archiving from GIS databases, automatic input for pedestrian simulations, as well as scalable pedestrian simulation tools have made it possible to simulate pedestrians at the individual level for complete cities in real time. An example that simulates the evacuation of the city of Barcelona demonstrates that this is now possible. This is the first step towards a fully integrated crowd prediction and management tool that takes into account not only data gathered in real time from cameras, cell phones or other sensors, but also merges these with advanced simulation tools to predict the future state of the crowd.Peer ReviewedPostprint (author's final draft