This work concerns visual functionalities to be embedded in a mobile robot for navigation purposes. More specifically, it relates to methods of dense stereoscopic vision based perception, grid occupancy based environment modeling and object tracking for autonomous navigation of mobile robots in indoor environments. We consider that is important for visual perception methods to be robust and fast. While in previous works, there are global stereo matching methods which are known for their robustness, but less likely to be employed in real-time applications. There are also local methods which are more suitable for real time but imprecise. To this aim, this work tries to find a compromise between robustness and real-time by proposing a semi-local method based on the definition of possibility distributions built around a fuzzy formalization of stereoscopic constraints. We consider also important for a mobile robot to better model its environment. To better fit a model to the reality we have to take uncertainty and inaccuracy into account. This work presents an occupancy grid environment modeling based on stereoscopic sensor inaccuracy.. Model updating relies on the definition of credibility values for the measures taken. Finally, perception and environment modeling are not goals but tools to provide robot high-level tasks. This work deals with visual tracking of a moving object such as high-level task.
