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Visual-based Global Localization from Ceiling Images using Convolutional Neural Networks

Abstract : The problem of global localization consists in determining the position of a mobile robot inside its environment without any prior knowledge of its position. Existing approaches for indoor localization present drawbacks such as the need to prepare the environment, dependency on specific features of the environment, and high quality sensor and computing hardware requirements. We focus on ceiling-based localization that is usable in crowded areas and does not require expensive hardware. While the global goal of our research is to develop a complete robust global indoor localization framework for a wheeled mobile robot, in this paper we focus on one part of this framework-being able to determine a robot's pose (2-DoF position plus orientation) from a single ceiling image. We use convolutional neural networks to learn the correspondence between a single image of the ceiling of the room, and the mobile robot's pose. We conduct experiments in real-world indoor environments that are significantly larger than those used in state of the art learning-based 6-DoF pose estimation methods. In spite of the difference in environment size, our method yields comparable accuracy.
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Contributeur : Philip SCALES Connectez-vous pour contacter le contributeur
Soumis le : lundi 12 avril 2021 - 17:12:07
Dernière modification le : mercredi 6 juillet 2022 - 04:14:49
Archivage à long terme le : : mardi 13 juillet 2021 - 19:10:11


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Philip Scales, Mykhailo Rimel, Olivier Aycard. Visual-based Global Localization from Ceiling Images using Convolutional Neural Networks. 16th International Conference on Computer Vision Theory and Applications, Feb 2021, Online Streaming, France. pp.927-934, ⟨10.5220/0010248409270934⟩. ⟨hal-03196336⟩



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