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.
['Ehsan Fazl-Ersi', 'John K. Tsotsos']
Visual Place Categorization in Indoor Environments