loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Juan José Cabrera ; Sergio Cebollada ; Luis Payá ; María Flores and Oscar Reinoso

Affiliation: Department of Systems Engineering and Automation, Miguel Hernández University, Elche, Spain

Keyword(s): Hierarchical Localization, Omnidirectional Imaging, Deep Learning, Bayesian Optimization.

Abstract: This paper reports and evaluates the training optimization process of a Convolutional Neural Network (CNN) with the aim of addressing the localization of a mobile robot. The proposed method addresses the localization problem by means of a hierarchical approach by using a visual sensor that provides omnidirectional images. In this sense, AlexNet is adapted and re-trained with a twofold purpose. First, the rough localization step consists of a room retrieval task. Second, the fine localization step within the retrieved room is carried out by means of a nearest neighbour search by comparing a holistic descriptor obtained from the CNN with the visual model of the retrieved room. The novelty of the present work lies in the use of a CNN and holistic descriptors obtained from raw omnidirectional images as captured by the vision system, with no panoramic conversion. In addition, this work proposes the use of a data augmentation technique and a Bayesian optimization to address the training pr ocess of the CNN. These approaches constitute an efficient and robust solution to the localization problem, as shown in the experimental section even in presence of substantial changes of the lighting conditions. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.145.50.71

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Cabrera, J.; Cebollada, S.; Payá, L.; Flores, M. and Reinoso, O. (2021). A Robust CNN Training Approach to Address Hierarchical Localization with Omnidirectional Images. In Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO; ISBN 978-989-758-522-7; ISSN 2184-2809, SciTePress, pages 301-310. DOI: 10.5220/0010574603010310

@conference{icinco21,
author={Juan José Cabrera. and Sergio Cebollada. and Luis Payá. and María Flores. and Oscar Reinoso.},
title={A Robust CNN Training Approach to Address Hierarchical Localization with Omnidirectional Images},
booktitle={Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO},
year={2021},
pages={301-310},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010574603010310},
isbn={978-989-758-522-7},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO
TI - A Robust CNN Training Approach to Address Hierarchical Localization with Omnidirectional Images
SN - 978-989-758-522-7
IS - 2184-2809
AU - Cabrera, J.
AU - Cebollada, S.
AU - Payá, L.
AU - Flores, M.
AU - Reinoso, O.
PY - 2021
SP - 301
EP - 310
DO - 10.5220/0010574603010310
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic