Incremental texture mapping for autonomous driving

Detalhes bibliográficos
Autor(a) principal: Miguel Riem Oliveira
Data de Publicação: 2016
Outros Autores: Santos,V, Sappa,AD, Dias,P, António Paulo Moreira
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/5048
http://dx.doi.org/10.1016/j.robot.2016.06.009
Resumo: Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures.
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spelling Incremental texture mapping for autonomous drivingAutonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures.2017-12-28T11:22:54Z2016-01-01T00:00:00Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/5048http://dx.doi.org/10.1016/j.robot.2016.06.009engMiguel Riem OliveiraSantos,VSappa,ADDias,PAntónio Paulo Moreirainfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:53Zoai:repositorio.inesctec.pt:123456789/5048Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:45.896184Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Incremental texture mapping for autonomous driving
title Incremental texture mapping for autonomous driving
spellingShingle Incremental texture mapping for autonomous driving
Miguel Riem Oliveira
title_short Incremental texture mapping for autonomous driving
title_full Incremental texture mapping for autonomous driving
title_fullStr Incremental texture mapping for autonomous driving
title_full_unstemmed Incremental texture mapping for autonomous driving
title_sort Incremental texture mapping for autonomous driving
author Miguel Riem Oliveira
author_facet Miguel Riem Oliveira
Santos,V
Sappa,AD
Dias,P
António Paulo Moreira
author_role author
author2 Santos,V
Sappa,AD
Dias,P
António Paulo Moreira
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Miguel Riem Oliveira
Santos,V
Sappa,AD
Dias,P
António Paulo Moreira
description Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures.
publishDate 2016
dc.date.none.fl_str_mv 2016-01-01T00:00:00Z
2016
2017-12-28T11:22:54Z
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dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/5048
http://dx.doi.org/10.1016/j.robot.2016.06.009
url http://repositorio.inesctec.pt/handle/123456789/5048
http://dx.doi.org/10.1016/j.robot.2016.06.009
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