A strategy for building topological maps through scene observation

Mobile robots remain idle during significant amounts of time in many applications, while a new task is not assigned to it. In this paper, we propose a framework to use such periods of inactivity to observe the surrounding environment and learn information that can be used later on during navigation. Events like someone entering or leaving a room, someone approaching a printer to pick a document up, etc., convey important information about the observed space and the role played by the objects therein. Information implicitly present in the motion patterns people describe in a certain workspace is then explored, to allow the robot to infer a "meaningful" spatial description. Such spatial representation is not driven by abstract geometrical considerations but, rather, by the role or function associated to locations or objects (affordances) and learnt by observing people's behaviour. Map building is thus bottom-up driven by the observation of human activity, and not simply a top-down oriented geometric construction.