In this paper we propose an efficient data-driven solution to self-localization within a floorplan.Floorplan data is readily available, long-term persistent and inherently robust to changes in the visual appearance.Our method does not require retraining per map and location or demand a large database of images of the area of interest.We propose a novel probabilistic model consisting of an observation and a novel temporal filtering module.Operating internally with an efficient ray-based representation, the observation module consists of a single and a multiview module to predict horizontal depth from images and fuses their results to benefit from advantages offered by either methodology.Our method operates on conventional consumer hardware and overcomes a common limitation of competing methods that often demand upright images.Our full system meets real-time requirements, while outperforming the state-of-the-art by a significant margin.