  Particle filtering is a powerful approach to sequential state estimation and
finds application in many domains, including robot localization, object
tracking, etc. To apply particle filtering in practice, a critical challenge is
to construct probabilistic system models, especially for systems with complex
dynamics or rich sensory inputs such as camera images. This paper introduces
the Particle Filter Network (PFnet), which encodes both a system model and a
particle filter algorithm in a single neural network. The PF-net is fully
differentiable and trained end-to-end from data. Instead of learning a generic
system model, it learns a model optimized for the particle filter algorithm. We
apply the PF-net to a visual localization task, in which a robot must localize
in a rich 3-D world, using only a schematic 2-D floor map. In simulation
experiments, PF-net consistently outperforms alternative learning
architectures, as well as a traditional model-based method, under a variety of
sensor inputs. Further, PF-net generalizes well to new, unseen environments.
