  Traditional place categorization approaches in robot vision assume that
training and test images have similar visual appearance. Therefore, any
seasonal, illumination and environmental changes typically lead to severe
degradation in performance. To cope with this problem, recent works have
proposed to adopt domain adaptation techniques. While effective, these methods
assume that some prior information about the scenario where the robot will
operate is available at training time. Unfortunately, in many cases this
assumption does not hold, as we often do not know where a robot will be
deployed. To overcome this issue, in this paper we present an approach which
aims at learning classification models able to generalize to unseen scenarios.
Specifically, we propose a novel deep learning framework for domain
generalization. Our method develops from the intuition that, given a set of
different classification models associated to known domains (e.g. corresponding
to multiple environments, robots), the best model for a new sample in the novel
domain can be computed directly at test time by optimally combining the known
models. To implement our idea, we exploit recent advances in deep domain
adaptation and design a Convolutional Neural Network architecture with novel
layers performing a weighted version of Batch Normalization. Our experiments,
conducted on three common datasets for robot place categorization, confirm the
validity of our contribution.
