Visual recognition of workspace landmarks for topological navigation

In this work, robot navigation is approached using visual landmarks. Landmarks are not preselected or otherwise defined a priori; they are extracted automatically during a learning phase. To facilitate this, a saliency map is constructed on the basis of which potential landmarks are highlighted. This is used in conjunction with a model-driven segregation of the workspace to further delineate search areas for landmarks in the environment. For the sake of robustness, no semantic information is attached to the landmarks; they are stored as raw patterns, along with information readily available from the workspace segregation. This subsequently facilitates their accurate recognition during a navigation session, when similar steps are employed to locate landmarks, as in the learning phase. The stored information is used to transform a previously learned landmark pattern, according to the current position of the observer, thus achieving accurate landmark recognition. Results obtained using this approach demonstrate its validity and applicability in indoor workspaces.