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Authors: Frederico Luiz Martins de Sousa ; Mateus Coelho Silva and Ricardo Augusto Rabelo Oliveira

Affiliation: Computer Science Department, Federal University of Ouro Preto, Ouro Preto, 35400-000, Brazil

Keyword(s): Edge AI, Mobile Robotics, Deep Learning, Monocular Visual Odometry.

Abstract: Visual odometry is a relevant problem considering mobile robotics. While intelligent robots can provide mapping and location tasks with a multitude of sensors, it is interesting to evaluate the ability to create models using less information to create similar information. While traditional approaches consider computer vision aspects of proposing solutions, they lack the application of modern perspectives as edge computing and deep learning. This text assesses the problem of evaluating the usage of deep-learning-based visual odometry models in mobile robotics. We expect mobile robots to have embedded computers with limited computing technologies, so we approach this problem through the Edge AI perspective. Our results displayed an improvement of the model considering previous results. Also, we profile the performance of hardware candidates to perform this task in mobile edge devices.

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Paper citation in several formats:
Martins de Sousa, F. ; Silva, M. and Oliveira, R. (2022). Applying Edge AI towards Deep-learning-based Monocular Visual Odometry Model for Mobile Robotics. In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-569-2; ISSN 2184-4992, SciTePress, pages 561-568. DOI: 10.5220/0011071600003179

@conference{iceis22,
author={Frederico Luiz {Martins de Sousa} and Mateus Coelho Silva and Ricardo Augusto Rabelo Oliveira},
title={Applying Edge AI towards Deep-learning-based Monocular Visual Odometry Model for Mobile Robotics},
booktitle={Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2022},
pages={561-568},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011071600003179},
isbn={978-989-758-569-2},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Applying Edge AI towards Deep-learning-based Monocular Visual Odometry Model for Mobile Robotics
SN - 978-989-758-569-2
IS - 2184-4992
AU - Martins de Sousa, F.
AU - Silva, M.
AU - Oliveira, R.
PY - 2022
SP - 561
EP - 568
DO - 10.5220/0011071600003179
PB - SciTePress

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