Visual Place Categorization in Indoor Environments

This paper addresses the problem of visual place categorization, which aims at augmenting different locations of the environment visited by an autonomous robot with information that relates them to human-understandable concepts. We formulate the problem of visual place categorization in terms of energy minimization. To label visual observations with place categories we present a global image representation that is invariant to common changes in dynamic environments and robust against intra-class variations. To satisfy temporal consistency, a general solution is presented that incorporates statistical cues, without being restricted by constant and small neighbourhood radii, or being dependent on the actual path followed by the robot. A set of experiments on publicly available databases demonstrates the advantages of the presented system and show a significant improvement over available methods.