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

Philip Scales, Mykhailo Rimel, Olivier Aycard

2021

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.

Paper Citation

in Harvard Style

Scales P., Rimel M. and Aycard O. (2021). Visual-based Global Localization from Ceiling Images using Convolutional Neural Networks. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP; ISBN 978-989-758-488-6, SciTePress, pages 927-934. DOI: 10.5220/0010248409270934

in Bibtex Style

@conference{visapp21,
author={Philip Scales and Mykhailo Rimel and Olivier Aycard},
title={Visual-based Global Localization from Ceiling Images using Convolutional Neural Networks},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP},
year={2021},
pages={927-934},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010248409270934},
isbn={978-989-758-488-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP
TI - Visual-based Global Localization from Ceiling Images using Convolutional Neural Networks
SN - 978-989-758-488-6
AU - Scales P.
AU - Rimel M.
AU - Aycard O.
PY - 2021
SP - 927
EP - 934
DO - 10.5220/0010248409270934
PB - SciTePress