  We consider the problem of building visual anomaly detection systems for
mobile robots. Standard anomaly detection models are trained using large
datasets composed only of non-anomalous data. However, in robotics
applications, it is often the case that (potentially very few) examples of
anomalies are available. We tackle the problem of exploiting these data to
improve the performance of a Real-NVP anomaly detection model, by minimizing,
jointly with the Real-NVP loss, an auxiliary outlier exposure margin loss. We
perform quantitative experiments on a novel dataset (which we publish as
supplementary material) designed for anomaly detection in an indoor patrolling
scenario. On a disjoint test set, our approach outperforms alternatives and
shows that exposing even a small number of anomalous frames yields significant
performance improvements.
