SpaceYNet: A Novel Approach to Pose and Depth-Scene Regression Simultaneously

Detalhes bibliográficos
Autor(a) principal: Aragao, Dunfrey
Data de Publicação: 2020
Outros Autores: Nascimento, Tiago, Mondini, Adriano [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/IWSSIP48289.2020.9145427
http://hdl.handle.net/11449/221528
Resumo: One of the fundamental dilemmas of mobile robotics is the use of sensory information to locate an agent in geographic space. In this paper, we developed a global relocation system to predict the robot's position and avoid unforeseen actions from a monocular image, which we named SpaceYNet. We incorporated Inception layers to symmetric layers of down-sampling and up-sampling to solve depth-scene and 6-DoF estimation simultaneously. Also, we compared SpaceYNet to PoseNet - a state of the art in robot pose regression using CNN - in order to evaluate it. The comparison comprised one public dataset and one created in a broad indoor environment. SpaceYNet showed higher accuracy in global percentages when compared to PoseNet.
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spelling SpaceYNet: A Novel Approach to Pose and Depth-Scene Regression SimultaneouslyDatasetdepth-sceneposeregressionrobotOne of the fundamental dilemmas of mobile robotics is the use of sensory information to locate an agent in geographic space. In this paper, we developed a global relocation system to predict the robot's position and avoid unforeseen actions from a monocular image, which we named SpaceYNet. We incorporated Inception layers to symmetric layers of down-sampling and up-sampling to solve depth-scene and 6-DoF estimation simultaneously. Also, we compared SpaceYNet to PoseNet - a state of the art in robot pose regression using CNN - in order to evaluate it. The comparison comprised one public dataset and one created in a broad indoor environment. SpaceYNet showed higher accuracy in global percentages when compared to PoseNet.Universidade Federal da ParaíbaUniversidade Estadual Paulista 'Júlio de Mesquita Filho'Universidade Estadual Paulista 'Júlio de Mesquita Filho'Universidade Federal da Paraíba (UFPB)Universidade Estadual Paulista (UNESP)Aragao, DunfreyNascimento, TiagoMondini, Adriano [UNESP]2022-04-28T19:29:14Z2022-04-28T19:29:14Z2020-07-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject217-222http://dx.doi.org/10.1109/IWSSIP48289.2020.9145427International Conference on Systems, Signals, and Image Processing, v. 2020-July, p. 217-222.2157-87022157-8672http://hdl.handle.net/11449/22152810.1109/IWSSIP48289.2020.91454272-s2.0-85089136198Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Conference on Systems, Signals, and Image Processinginfo:eu-repo/semantics/openAccess2022-04-28T19:29:14Zoai:repositorio.unesp.br:11449/221528Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:49:22.481690Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv SpaceYNet: A Novel Approach to Pose and Depth-Scene Regression Simultaneously
title SpaceYNet: A Novel Approach to Pose and Depth-Scene Regression Simultaneously
spellingShingle SpaceYNet: A Novel Approach to Pose and Depth-Scene Regression Simultaneously
Aragao, Dunfrey
Dataset
depth-scene
pose
regression
robot
title_short SpaceYNet: A Novel Approach to Pose and Depth-Scene Regression Simultaneously
title_full SpaceYNet: A Novel Approach to Pose and Depth-Scene Regression Simultaneously
title_fullStr SpaceYNet: A Novel Approach to Pose and Depth-Scene Regression Simultaneously
title_full_unstemmed SpaceYNet: A Novel Approach to Pose and Depth-Scene Regression Simultaneously
title_sort SpaceYNet: A Novel Approach to Pose and Depth-Scene Regression Simultaneously
author Aragao, Dunfrey
author_facet Aragao, Dunfrey
Nascimento, Tiago
Mondini, Adriano [UNESP]
author_role author
author2 Nascimento, Tiago
Mondini, Adriano [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Federal da Paraíba (UFPB)
Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Aragao, Dunfrey
Nascimento, Tiago
Mondini, Adriano [UNESP]
dc.subject.por.fl_str_mv Dataset
depth-scene
pose
regression
robot
topic Dataset
depth-scene
pose
regression
robot
description One of the fundamental dilemmas of mobile robotics is the use of sensory information to locate an agent in geographic space. In this paper, we developed a global relocation system to predict the robot's position and avoid unforeseen actions from a monocular image, which we named SpaceYNet. We incorporated Inception layers to symmetric layers of down-sampling and up-sampling to solve depth-scene and 6-DoF estimation simultaneously. Also, we compared SpaceYNet to PoseNet - a state of the art in robot pose regression using CNN - in order to evaluate it. The comparison comprised one public dataset and one created in a broad indoor environment. SpaceYNet showed higher accuracy in global percentages when compared to PoseNet.
publishDate 2020
dc.date.none.fl_str_mv 2020-07-01
2022-04-28T19:29:14Z
2022-04-28T19:29:14Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/IWSSIP48289.2020.9145427
International Conference on Systems, Signals, and Image Processing, v. 2020-July, p. 217-222.
2157-8702
2157-8672
http://hdl.handle.net/11449/221528
10.1109/IWSSIP48289.2020.9145427
2-s2.0-85089136198
url http://dx.doi.org/10.1109/IWSSIP48289.2020.9145427
http://hdl.handle.net/11449/221528
identifier_str_mv International Conference on Systems, Signals, and Image Processing, v. 2020-July, p. 217-222.
2157-8702
2157-8672
10.1109/IWSSIP48289.2020.9145427
2-s2.0-85089136198
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv International Conference on Systems, Signals, and Image Processing
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 217-222
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808129254349602816

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