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.

翻译：本文提出了一种高效的数据驱动解决方案，用于在楼层平面内实现自主定位。楼层平面数据易于获取、长期稳定且本质上对视觉外观变化具有鲁棒性。我们的方法无需针对每个地图或位置重新训练，也不需要存储大量目标区域的图像数据库。我们提出了一种新颖的概率模型，包含一个观测模块和一个新颖的时间滤波模块。观测模块内部采用高效的基于射线的表示方法，由单视角和多视角子模块组成，用于从图像中预测水平深度，并通过融合两者结果以充分利用两种方法的优势。我们的方法可在传统消费级硬件上运行，并克服了同类方法通常要求图像竖直的常见限制。完整系统满足实时性要求，同时显著超越了当前最先进方法的性能。