  Ultra-Wideband (UWB) technology is an emerging low-cost solution for
localization in a generic environment. However, UWB signal can be affected by
signal reflections and non-line-of-sight (NLoS) conditions between anchors;
hence, in a broader sense, the specific geometry of the environment and the
disposition of obstructing elements in the map may drastically hinder the
reliability of UWB for precise robot localization. This work aims to mitigate
this problem by learning a map-specific characterization of the UWB quality
signal with a fingerprint semi-supervised novelty detection methodology. An
unsupervised autoencoder neural network is trained on nominal UWB map
conditions, and then it is used to predict errors derived from the introduction
of perturbing novelties in the environment. This work poses a step change in
the understanding of UWB localization and its reliability in evolving
environmental conditions. The resulting performance of the proposed method is
proved by fine-grained experiments obtained with a visual tracking ground
truth.
