Creation of Hybrid Hierarchical Models by Using Omnidirectional Vision and Machine Learning Techniques

Over the past few years, the presence of mobile robots has significantly increased. Nowadays, they can be used for a wide range of applications and they can be found in diverse kinds of environments, such as industrial, household, educational and healthcare. Regarding mobile autonomous robots, these...

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Detalles Bibliográficos
Autor: Cebollada López, Sergio
Tipo de recurso: tesis doctoral
Fecha de publicación:2021
País:España
Institución:Universidad Miguel Hernández de Elche
Repositorio:REDIUMH. Depósito Digital de la UMH
OAI Identifier:oai:dspace.umh.es:11000/25519
Acceso en línea:http://dspace.umh.es/handle/11000/25519
Access Level:acceso abierto
Palabra clave:Robótica
Visión artificial
Inteligencia artificial
CDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnología
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spelling Creation of Hybrid Hierarchical Models by Using Omnidirectional Vision and Machine Learning TechniquesCebollada López, SergioRobóticaVisión artificialInteligencia artificialCDU::6 - Ciencias aplicadas::62 - Ingeniería. TecnologíaOver the past few years, the presence of mobile robots has significantly increased. Nowadays, they can be used for a wide range of applications and they can be found in diverse kinds of environments, such as industrial, household, educational and healthcare. Regarding mobile autonomous robots, these systems need a high degree of autonomy to develop their tasks. This means that they must be able to localize themselves and to navigate through environments, which are a priori unknown. Therefore, the robot will have to carry out the mapping task, which consists in obtaining information from the environment and creating a model. Once this task is done, the robot will be able to address the localization task, i.e., estimating its position within the environment with respect to a specific reference system. This thesis presents the analysis and design of mapping and localization methods in indoor environments. On the one hand, the thesis presents a work that focuses on solving these problems in underfloor voids with the aim of tackling a spray foam insulation task. On the other hand, a hierarchical localization framework is proposed and evaluated, considering severe visual effects that can influence the accuracy of the proposed method. The present thesis carries out a work, which focuses on solving the mapping and localization problems in voids between floor and foundations. Solving these tasks in such environments is especially challenging concerning visual information because the environment is dark and the terrain is uneven as stones, bricks fragments or sand are often present. Within these environments, the robot should be able to localize itself and apply insulation foam to the underside of the floor. Hence, the localization process is solved by estimating the position of the robot with respect to previously known position. This is done by using the alignment between point clouds (depth information). The robot is equipped with a 2D laser sensor, which permits building point clouds from several positions of the underfloor environment. This thesis describes several algorithms to obtain robustly the alignment between two positions. The proposed algorithms are tested with a set of point clouds captured with a laser scan under real working conditions. The results show that the localization problem can be solved and the accuracy obtained is enough to develop the insulation task.Universidad Miguel Hernández de ElcheReinoso García, ÓscarPayá Castelló, LuisDepartamentos de la UMH::Ingeniería de Sistemas y Automática2021202120212021info:eu-repo/semantics/doctoralThesisapplication/pdf390application/pdfhttp://dspace.umh.es/handle/11000/25519reponame:REDIUMH. Depósito Digital de la UMHinstname:Universidad Miguel Hernández de ElcheInglésinfo:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:dspace.umh.es:11000/255192026-05-27T13:36:21Z
dc.title.none.fl_str_mv Creation of Hybrid Hierarchical Models by Using Omnidirectional Vision and Machine Learning Techniques
title Creation of Hybrid Hierarchical Models by Using Omnidirectional Vision and Machine Learning Techniques
spellingShingle Creation of Hybrid Hierarchical Models by Using Omnidirectional Vision and Machine Learning Techniques
Cebollada López, Sergio
Robótica
Visión artificial
Inteligencia artificial
CDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnología
title_short Creation of Hybrid Hierarchical Models by Using Omnidirectional Vision and Machine Learning Techniques
title_full Creation of Hybrid Hierarchical Models by Using Omnidirectional Vision and Machine Learning Techniques
title_fullStr Creation of Hybrid Hierarchical Models by Using Omnidirectional Vision and Machine Learning Techniques
title_full_unstemmed Creation of Hybrid Hierarchical Models by Using Omnidirectional Vision and Machine Learning Techniques
title_sort Creation of Hybrid Hierarchical Models by Using Omnidirectional Vision and Machine Learning Techniques
dc.creator.none.fl_str_mv Cebollada López, Sergio
author Cebollada López, Sergio
author_facet Cebollada López, Sergio
author_role author
dc.contributor.none.fl_str_mv Reinoso García, Óscar
Payá Castelló, Luis
Departamentos de la UMH::Ingeniería de Sistemas y Automática
dc.subject.none.fl_str_mv Robótica
Visión artificial
Inteligencia artificial
CDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnología
topic Robótica
Visión artificial
Inteligencia artificial
CDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnología
description Over the past few years, the presence of mobile robots has significantly increased. Nowadays, they can be used for a wide range of applications and they can be found in diverse kinds of environments, such as industrial, household, educational and healthcare. Regarding mobile autonomous robots, these systems need a high degree of autonomy to develop their tasks. This means that they must be able to localize themselves and to navigate through environments, which are a priori unknown. Therefore, the robot will have to carry out the mapping task, which consists in obtaining information from the environment and creating a model. Once this task is done, the robot will be able to address the localization task, i.e., estimating its position within the environment with respect to a specific reference system. This thesis presents the analysis and design of mapping and localization methods in indoor environments. On the one hand, the thesis presents a work that focuses on solving these problems in underfloor voids with the aim of tackling a spray foam insulation task. On the other hand, a hierarchical localization framework is proposed and evaluated, considering severe visual effects that can influence the accuracy of the proposed method. The present thesis carries out a work, which focuses on solving the mapping and localization problems in voids between floor and foundations. Solving these tasks in such environments is especially challenging concerning visual information because the environment is dark and the terrain is uneven as stones, bricks fragments or sand are often present. Within these environments, the robot should be able to localize itself and apply insulation foam to the underside of the floor. Hence, the localization process is solved by estimating the position of the robot with respect to previously known position. This is done by using the alignment between point clouds (depth information). The robot is equipped with a 2D laser sensor, which permits building point clouds from several positions of the underfloor environment. This thesis describes several algorithms to obtain robustly the alignment between two positions. The proposed algorithms are tested with a set of point clouds captured with a laser scan under real working conditions. The results show that the localization problem can be solved and the accuracy obtained is enough to develop the insulation task.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
dc.identifier.none.fl_str_mv http://dspace.umh.es/handle/11000/25519
url http://dspace.umh.es/handle/11000/25519
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
390
application/pdf
dc.publisher.none.fl_str_mv Universidad Miguel Hernández de Elche
publisher.none.fl_str_mv Universidad Miguel Hernández de Elche
dc.source.none.fl_str_mv reponame:REDIUMH. Depósito Digital de la UMH
instname:Universidad Miguel Hernández de Elche
instname_str Universidad Miguel Hernández de Elche
reponame_str REDIUMH. Depósito Digital de la UMH
collection REDIUMH. Depósito Digital de la UMH
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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