F$^3$Loc: Fusion and Filtering for Floorplan Localization

• URL: http://arxiv.org/abs/2403.03370v1
• Date: Tue, 5 Mar 2024 23:32:26 GMT
• Title: F$^3$Loc: Fusion and Filtering for Floorplan Localization
• Authors: Changan Chen, Rui Wang, Christoph Vogel, Marc Pollefeys
• Abstract summary: We propose an efficient data-driven solution to self-localization within a floorplan. Our method does not require retraining per map and location or demand a large database of images of the area of interest.
• Score: 63.28504055661646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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