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 paper, 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 and to increase the robustness of the classification. 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
