Indoor environments can typically be divided into places with different functionalities like corridors, kitchens, offices, or seminar rooms. We believe that such semantic information enables a mobile robot to more efficiently accomplish a variety of tasks such as human-robot interaction, path-planning, or localization. In this work, we propose an approach to classify places in indoor environments into different categories. Our approach uses AdaBoost to boost simple features extracted from vision and laser range data. Furthermore, we apply a Hidden Markov Model to take spatial dependencies between robot poses into account when moving on a trajectory and to increase the robustness of the classification. Furthermore, we present some current work on learning topologicla maps using the semantic classification and applying probabilistic relaxation methods. Our technique has been implemented and tested on real robots as well as in simulation. Experiments presented in this paper demonstrate that our approach can be utilized to robustly classify places into semantic categories and to learn topological maps with semantic information.
Presentation paper: [pdf: 1858k]