  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.
