F$^3$Loc: フロアプラン自己位置推定のための融合とフィルタリング
F$^3$Loc: Fusion and Filtering for Floorplan Localization
フロアプラン上での自己位置推定を、単一・多視点の深度予測を融合した確率的モデルと時間フィルタリングで高精度・リアルタイムに実現した研究。
著者: Changan Chen, Rui Wang, Christoph Vogel, Marc Pollefeys
分類: cs.CV, cs.RO
原文アブストラクト
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.
