Visual navigation in an open environment without map

We describe how a mobile robot controlled only by visual information can retrieve a particular goal location in an open environment. Our model does not need a precise map nor to learn all the possible positions in the environment. The system is a neural architecture inspired from neurobiological studies using the recognition of visual patterns called landmarks. The robot merges this visual information and its azimuth to build a plastic representation of its location. This representation is used to learn the best movement to reach the goal. A simple and fast online learning of a few places located near the goal allows the robot to reach the goal from anywhere in its neighborhood. The system uses only an egocentric representation of the robot environment and presents very high generalization capabilities. We describe an efficient implementation tested on our robot in two real indoor environments. We show the limitations of the model and its possible extensions to create autonomous robots only guided by visual information.