Training, Optimization and Validation of a CNN for Room Retrieval and Description of Omnidirectional Images

Abstract

The aim of this paper is to achieve the optimal hyperparameters setup of a convolutional neural network (CNN) to address the localization of a mobile robot. The localization problem is solved with a hierarchical approach by using omnidirectional images as provided by a catadioptric visual sensor, with no panoramic conversion. In this way, we propose adapting and re-training AlexNet with a double purpose. First, to perform the rough localization step by means of a room retrieval task. Second, to carry out the fine localization step within the retrieved room, in which the CNN is used to obtain a holistic descriptor that is compared with the visual model of the retrieved room by means of a nearest neighbour search. To achieve this, a CNN has been adapted and re-trained to address both the room retrieval problem and the obtention of holistic descriptors from raw omnidirectional images. The novelty of this work is the use of a data augmentation technique and Bayesian optimization to address the training process robustly. As shown in the present paper, these tools have proven to be an efficient and robust solution to the localization problem even with substantial changes of the lighting conditions of the target environment.