Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection

Autores
Auat Cheein, F.; Steiner, G.; Perez Paina, G.; Carelli Albarracin, Ricardo Oscar
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Precision agricultural maps are required for agricultural machinery navigation, path planning and plantation supervision. In this work we present a Simultaneous Localization and Mapping (SLAM) algorithm solved by an Extended Information Filter (EIF) for agricultural environments (olive groves). The SLAM algorithm is implemented on an unmanned non-holonomic car-like mobile robot. The map of the environment is based on the detection of olive stems from the plantation. The olive stems are acquired by means of both: a range sensor laser and a monocular vision system. A support vector machine (SVM) is implemented on the vision system to detect olive stems on the images acquired from the environment. Also, the SLAM algorithm has an optimization criterion associated with it. This optimization criterion is based on the correction of the SLAM system state vector using only the most meaningful stems - from an estimation convergence perspective - extracted from the environment information without compromising the estimation consistency. The optimization criterion, its demonstration and experimental results within real agricultural environments showing the performance of our proposal are also included in this work.
Fil: Auat Cheein, F.. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Steiner, G.. Universidad Tecnológica Nacional; Argentina
Fil: Perez Paina, G.. Universidad Tecnológica Nacional; Argentina
Fil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
AGRICULTURAL MAPPING
MOBILE ROBOT
SLAM
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:11336/189712
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/189712
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detectionAuat Cheein, F.Steiner, G.Perez Paina, G.Carelli Albarracin, Ricardo OscarAGRICULTURAL MAPPINGMOBILE ROBOTSLAMhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Precision agricultural maps are required for agricultural machinery navigation, path planning and plantation supervision. In this work we present a Simultaneous Localization and Mapping (SLAM) algorithm solved by an Extended Information Filter (EIF) for agricultural environments (olive groves). The SLAM algorithm is implemented on an unmanned non-holonomic car-like mobile robot. The map of the environment is based on the detection of olive stems from the plantation. The olive stems are acquired by means of both: a range sensor laser and a monocular vision system. A support vector machine (SVM) is implemented on the vision system to detect olive stems on the images acquired from the environment. Also, the SLAM algorithm has an optimization criterion associated with it. This optimization criterion is based on the correction of the SLAM system state vector using only the most meaningful stems - from an estimation convergence perspective - extracted from the environment information without compromising the estimation consistency. The optimization criterion, its demonstration and experimental results within real agricultural environments showing the performance of our proposal are also included in this work.Fil: Auat Cheein, F.. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Steiner, G.. Universidad Tecnológica Nacional; ArgentinaFil: Perez Paina, G.. Universidad Tecnológica Nacional; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier2011-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/189712Auat Cheein, F.; Steiner, G.; Perez Paina, G.; Carelli Albarracin, Ricardo Oscar; Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection; Elsevier; Computers and Eletronics in Agriculture; 78; 2; 9-2011; 195-2070168-1699CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0168169911001542info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compag.2011.07.007info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:17:43Zoai:ri.conicet.gov.ar:11336/189712instacron: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 10:17:43.775CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection
title Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection
spellingShingle Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection
Auat Cheein, F.
AGRICULTURAL MAPPING
MOBILE ROBOT
SLAM
title_short Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection
title_full Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection
title_fullStr Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection
title_full_unstemmed Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection
title_sort Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection
dc.creator.none.fl_str_mv Auat Cheein, F.
Steiner, G.
Perez Paina, G.
Carelli Albarracin, Ricardo Oscar
author Auat Cheein, F.
author_facet Auat Cheein, F.
Steiner, G.
Perez Paina, G.
Carelli Albarracin, Ricardo Oscar
author_role author
author2 Steiner, G.
Perez Paina, G.
Carelli Albarracin, Ricardo Oscar
author2_role author
author
author
dc.subject.none.fl_str_mv AGRICULTURAL MAPPING
MOBILE ROBOT
SLAM
topic AGRICULTURAL MAPPING
MOBILE ROBOT
SLAM
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Precision agricultural maps are required for agricultural machinery navigation, path planning and plantation supervision. In this work we present a Simultaneous Localization and Mapping (SLAM) algorithm solved by an Extended Information Filter (EIF) for agricultural environments (olive groves). The SLAM algorithm is implemented on an unmanned non-holonomic car-like mobile robot. The map of the environment is based on the detection of olive stems from the plantation. The olive stems are acquired by means of both: a range sensor laser and a monocular vision system. A support vector machine (SVM) is implemented on the vision system to detect olive stems on the images acquired from the environment. Also, the SLAM algorithm has an optimization criterion associated with it. This optimization criterion is based on the correction of the SLAM system state vector using only the most meaningful stems - from an estimation convergence perspective - extracted from the environment information without compromising the estimation consistency. The optimization criterion, its demonstration and experimental results within real agricultural environments showing the performance of our proposal are also included in this work.
Fil: Auat Cheein, F.. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Steiner, G.. Universidad Tecnológica Nacional; Argentina
Fil: Perez Paina, G.. Universidad Tecnológica Nacional; Argentina
Fil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Precision agricultural maps are required for agricultural machinery navigation, path planning and plantation supervision. In this work we present a Simultaneous Localization and Mapping (SLAM) algorithm solved by an Extended Information Filter (EIF) for agricultural environments (olive groves). The SLAM algorithm is implemented on an unmanned non-holonomic car-like mobile robot. The map of the environment is based on the detection of olive stems from the plantation. The olive stems are acquired by means of both: a range sensor laser and a monocular vision system. A support vector machine (SVM) is implemented on the vision system to detect olive stems on the images acquired from the environment. Also, the SLAM algorithm has an optimization criterion associated with it. This optimization criterion is based on the correction of the SLAM system state vector using only the most meaningful stems - from an estimation convergence perspective - extracted from the environment information without compromising the estimation consistency. The optimization criterion, its demonstration and experimental results within real agricultural environments showing the performance of our proposal are also included in this work.
publishDate 2011
dc.date.none.fl_str_mv 2011-09
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/11336/189712
Auat Cheein, F.; Steiner, G.; Perez Paina, G.; Carelli Albarracin, Ricardo Oscar; Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection; Elsevier; Computers and Eletronics in Agriculture; 78; 2; 9-2011; 195-207
0168-1699
CONICET Digital
CONICET
url http://hdl.handle.net/11336/189712
identifier_str_mv Auat Cheein, F.; Steiner, G.; Perez Paina, G.; Carelli Albarracin, Ricardo Oscar; Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection; Elsevier; Computers and Eletronics in Agriculture; 78; 2; 9-2011; 195-207
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/abs/pii/S0168169911001542
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compag.2011.07.007
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
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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