Bounded memory probabilistic mapping of out-of-structure objects in fruit crops environments

Autores
Gimenez Romero, Javier Alejandro; Tosetti Sanz, Santiago Ramon; Salinas, Lucio Rafael; Carelli Albarracin, Ricardo Oscar
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Spatial awareness and memory are key factors for a robot to evolve in semi-structured and dynamic environments as those found in agriculture, and particularly in fruit crops where the trees are regularly distributed. This paper proposes a probabilistic method for mapping out-of-structure objects (weeds, workers, machines, fallen branches, etc.) using a Kernel density estimator. The methodology has theoretical and practical advantages over the well-known occupancy grid map estimator such as optimization of storage resources, online update, high resolution, and straightforward adaptability to dynamic environments. An example application would be a control scheme through which a robot is able to perform cautious navigation in areas with high probability of finding obstacles. Simulations and experiments show that large extensions can be online mapped with few data and high spatial resolution.
Fil: Gimenez Romero, Javier Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Tosetti Sanz, Santiago Ramon. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Salinas, Lucio Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Materia
DYNAMIC OBJECT
KERNEL ESTIMATORS
PRECISION AGRICULTURE
PROBABILISTIC MAPPING
RECURSIVE SUBSAMPLING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/89230
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Bounded memory probabilistic mapping of out-of-structure objects in fruit crops environmentsGimenez Romero, Javier AlejandroTosetti Sanz, Santiago RamonSalinas, Lucio RafaelCarelli Albarracin, Ricardo OscarDYNAMIC OBJECTKERNEL ESTIMATORSPRECISION AGRICULTUREPROBABILISTIC MAPPINGRECURSIVE SUBSAMPLINGhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Spatial awareness and memory are key factors for a robot to evolve in semi-structured and dynamic environments as those found in agriculture, and particularly in fruit crops where the trees are regularly distributed. This paper proposes a probabilistic method for mapping out-of-structure objects (weeds, workers, machines, fallen branches, etc.) using a Kernel density estimator. The methodology has theoretical and practical advantages over the well-known occupancy grid map estimator such as optimization of storage resources, online update, high resolution, and straightforward adaptability to dynamic environments. An example application would be a control scheme through which a robot is able to perform cautious navigation in areas with high probability of finding obstacles. Simulations and experiments show that large extensions can be online mapped with few data and high spatial resolution.Fil: Gimenez Romero, Javier Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Tosetti Sanz, Santiago Ramon. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Salinas, Lucio Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaElsevier2018-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/89230Gimenez Romero, Javier Alejandro; Tosetti Sanz, Santiago Ramon; Salinas, Lucio Rafael; Carelli Albarracin, Ricardo Oscar; Bounded memory probabilistic mapping of out-of-structure objects in fruit crops environments; Elsevier; Computers and Eletronics in Agriculture; 151; 8-2018; 11-200168-1699CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0168169917313662info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compag.2018.05.018info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:38:03Zoai:ri.conicet.gov.ar:11336/89230instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:38:03.529CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Bounded memory probabilistic mapping of out-of-structure objects in fruit crops environments
title Bounded memory probabilistic mapping of out-of-structure objects in fruit crops environments
spellingShingle Bounded memory probabilistic mapping of out-of-structure objects in fruit crops environments
Gimenez Romero, Javier Alejandro
DYNAMIC OBJECT
KERNEL ESTIMATORS
PRECISION AGRICULTURE
PROBABILISTIC MAPPING
RECURSIVE SUBSAMPLING
title_short Bounded memory probabilistic mapping of out-of-structure objects in fruit crops environments
title_full Bounded memory probabilistic mapping of out-of-structure objects in fruit crops environments
title_fullStr Bounded memory probabilistic mapping of out-of-structure objects in fruit crops environments
title_full_unstemmed Bounded memory probabilistic mapping of out-of-structure objects in fruit crops environments
title_sort Bounded memory probabilistic mapping of out-of-structure objects in fruit crops environments
dc.creator.none.fl_str_mv Gimenez Romero, Javier Alejandro
Tosetti Sanz, Santiago Ramon
Salinas, Lucio Rafael
Carelli Albarracin, Ricardo Oscar
author Gimenez Romero, Javier Alejandro
author_facet Gimenez Romero, Javier Alejandro
Tosetti Sanz, Santiago Ramon
Salinas, Lucio Rafael
Carelli Albarracin, Ricardo Oscar
author_role author
author2 Tosetti Sanz, Santiago Ramon
Salinas, Lucio Rafael
Carelli Albarracin, Ricardo Oscar
author2_role author
author
author
dc.subject.none.fl_str_mv DYNAMIC OBJECT
KERNEL ESTIMATORS
PRECISION AGRICULTURE
PROBABILISTIC MAPPING
RECURSIVE SUBSAMPLING
topic DYNAMIC OBJECT
KERNEL ESTIMATORS
PRECISION AGRICULTURE
PROBABILISTIC MAPPING
RECURSIVE SUBSAMPLING
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Spatial awareness and memory are key factors for a robot to evolve in semi-structured and dynamic environments as those found in agriculture, and particularly in fruit crops where the trees are regularly distributed. This paper proposes a probabilistic method for mapping out-of-structure objects (weeds, workers, machines, fallen branches, etc.) using a Kernel density estimator. The methodology has theoretical and practical advantages over the well-known occupancy grid map estimator such as optimization of storage resources, online update, high resolution, and straightforward adaptability to dynamic environments. An example application would be a control scheme through which a robot is able to perform cautious navigation in areas with high probability of finding obstacles. Simulations and experiments show that large extensions can be online mapped with few data and high spatial resolution.
Fil: Gimenez Romero, Javier Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Tosetti Sanz, Santiago Ramon. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Salinas, Lucio Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
description Spatial awareness and memory are key factors for a robot to evolve in semi-structured and dynamic environments as those found in agriculture, and particularly in fruit crops where the trees are regularly distributed. This paper proposes a probabilistic method for mapping out-of-structure objects (weeds, workers, machines, fallen branches, etc.) using a Kernel density estimator. The methodology has theoretical and practical advantages over the well-known occupancy grid map estimator such as optimization of storage resources, online update, high resolution, and straightforward adaptability to dynamic environments. An example application would be a control scheme through which a robot is able to perform cautious navigation in areas with high probability of finding obstacles. Simulations and experiments show that large extensions can be online mapped with few data and high spatial resolution.
publishDate 2018
dc.date.none.fl_str_mv 2018-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/89230
Gimenez Romero, Javier Alejandro; Tosetti Sanz, Santiago Ramon; Salinas, Lucio Rafael; Carelli Albarracin, Ricardo Oscar; Bounded memory probabilistic mapping of out-of-structure objects in fruit crops environments; Elsevier; Computers and Eletronics in Agriculture; 151; 8-2018; 11-20
0168-1699
CONICET Digital
CONICET
url http://hdl.handle.net/11336/89230
identifier_str_mv Gimenez Romero, Javier Alejandro; Tosetti Sanz, Santiago Ramon; Salinas, Lucio Rafael; Carelli Albarracin, Ricardo Oscar; Bounded memory probabilistic mapping of out-of-structure objects in fruit crops environments; Elsevier; Computers and Eletronics in Agriculture; 151; 8-2018; 11-20
0168-1699
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0168169917313662
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compag.2018.05.018
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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