Towards Robust Object Categorization for Mobile Robots with Combination of Classifiers

Abstract. An efficient object perception is a crucial component of a mobile service robot. In this work we present a solution for visual cat-egorization of objects. We developed a prototypic categorization sys-tem which classifies unknown objects based on their visual properties to a corresponding category of predefined domestic object categories. The system uses the Bag of Features approach which does not rely on global geometric object information. A major contribution of our work is the enhancement of the categorization accuracy and robustness through a selected combination of a set of supervised machine learners which are trained with visual information from object instances. Experimen-tal results are provided which benchmark the behavior and verify the performance regarding the accuracy and robustness of the proposed sys-tem. The system is integrated on a mobile service robot to enhance its perceptual capabilities, hence computational cost and robot dependent properties are considered as essential design criteria