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.

我们提出了一种高效的数据驱动的解决方案，用于楼层平面图的自我定位。该方法不需要每张地图和位置的重新训练，也不需要大量图片数据库。我们提出了一个由观测模块和新型时间滤波模块组成的新颖概率模型。我们的方法适用于传统消费硬件，并克服了其他方法常常需要直立图像的共同限制。我们的系统满足实时要求，并在性能上显著超过现有技术。