Robot Semantic Localization Through CNN Descriptors

Abstract

Semantic localization for mobile robots involves an accurate determination of the kind of place where a robot is located. Therefore, the representation of the knowledge of this place is crucial for the robot. In this paper we present a study for finding a robust model for scene classification procedure for a mobile robot. The proposed system uses CNN descriptors for representing the input perceptions of the robot. First, we develop comparative experiments in order for finding a model. Experiments include the evaluation of several pre-trained CNN models and training a classifier with different classifications algorithms. These experiments were carried out using the ViDRILO dataset and compared with the benchmark provided by their authors. The results demonstrate the goodness of using CNN descriptors for semantic classification.