F$^3$Loc: Fusion and Filtering for Floorplan Localization
2024
Online
report
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.
Comment: 10 pages, 11 figure, accepted to CVPR 2024
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F$^3$Loc: Fusion and Filtering for Floorplan Localization
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Autor/in / Beteiligte Person: | Chen, Changan ; Wang, Rui ; Vogel, Christoph ; Pollefeys, Marc |
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Veröffentlichung: | 2024 |
Medientyp: | report |
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