F$^3$Loc: Fusion and Filtering for Floorplan Localization
<|reference_start|>F$^3$Loc: Fusion and Filtering for Floorplan Localization: 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.<|reference_end|>
@article{chen2024f$^3$loc:,
    title={F$^3$Loc: Fusion and Filtering for Floorplan Localization},
    author={Changan Chen, Rui Wang, Christoph Vogel, Marc Pollefeys},
    journal={arXiv preprint arXiv:2403.03370},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03370},
    primaryClass={cs.CV cs.RO}
}