Optimization of laser scanner positioning networks for architectural surveys through the design of genetic algorithms

In recent decades, the use of terrestrial laser scanners has become the principal method for metric data collection in architecture. However, there are no systematic procedures in place to plan the data capture process. This means that the obtaining tasks of the clouds of points are based either on...

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Authors: Cabrera Revuelta. Elena, Chávez de Diego, María José, Barrera Vera, José Antonio, Fernández Rodríguez, Yago, Caballero Sánchez, Manuel
Format: article
Status:Versión aceptada para publicación
Publication Date:2021
Country:España
Institution:Universidad de Sevilla (US)
Repository:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/170973
Online Access:https://hdl.handle.net/11441/170973
https://doi.org/10.1016/j.measurement.2020.108898
Access Level:Open access
Keyword:Terrestrial laser scanner
Genetic algorithm
Optimization
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spelling Optimization of laser scanner positioning networks for architectural surveys through the design of genetic algorithmsCabrera Revuelta. ElenaChávez de Diego, María JoséBarrera Vera, José AntonioFernández Rodríguez, YagoCaballero Sánchez, ManuelTerrestrial laser scannerGenetic algorithmOptimizationIn recent decades, the use of terrestrial laser scanners has become the principal method for metric data collection in architecture. However, there are no systematic procedures in place to plan the data capture process. This means that the obtaining tasks of the clouds of points are based either on operator experience, or on the overlap register that grants a complete acquisition. In both cases, data redundancy represents a significant percentage, which forces subsequent filtration or point removal. This work describes the design and development of an automated methodology, based on genetic algorithms, for the selection of a set of positions from which to execute the data capture process. The algorithm designed herein is applied to a variety of cases, thereby attaining the best station-positioning network for data collection, which maximizes coverage and minimizes overlap between clouds of points.ElsevierMatemática Aplicada IIngeniería Gráfica2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/170973https://doi.org/10.1016/j.measurement.2020.108898reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésMeasurement, 174, 108898.https://www.sciencedirect.com/science/article/abs/pii/S026322412031383X?via%3Dihubinfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1709732026-06-17T12:51:07Z
dc.title.none.fl_str_mv Optimization of laser scanner positioning networks for architectural surveys through the design of genetic algorithms
title Optimization of laser scanner positioning networks for architectural surveys through the design of genetic algorithms
spellingShingle Optimization of laser scanner positioning networks for architectural surveys through the design of genetic algorithms
Cabrera Revuelta. Elena
Terrestrial laser scanner
Genetic algorithm
Optimization
title_short Optimization of laser scanner positioning networks for architectural surveys through the design of genetic algorithms
title_full Optimization of laser scanner positioning networks for architectural surveys through the design of genetic algorithms
title_fullStr Optimization of laser scanner positioning networks for architectural surveys through the design of genetic algorithms
title_full_unstemmed Optimization of laser scanner positioning networks for architectural surveys through the design of genetic algorithms
title_sort Optimization of laser scanner positioning networks for architectural surveys through the design of genetic algorithms
dc.creator.none.fl_str_mv Cabrera Revuelta. Elena
Chávez de Diego, María José
Barrera Vera, José Antonio
Fernández Rodríguez, Yago
Caballero Sánchez, Manuel
author Cabrera Revuelta. Elena
author_facet Cabrera Revuelta. Elena
Chávez de Diego, María José
Barrera Vera, José Antonio
Fernández Rodríguez, Yago
Caballero Sánchez, Manuel
author_role author
author2 Chávez de Diego, María José
Barrera Vera, José Antonio
Fernández Rodríguez, Yago
Caballero Sánchez, Manuel
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Matemática Aplicada I
Ingeniería Gráfica
dc.subject.none.fl_str_mv Terrestrial laser scanner
Genetic algorithm
Optimization
topic Terrestrial laser scanner
Genetic algorithm
Optimization
description In recent decades, the use of terrestrial laser scanners has become the principal method for metric data collection in architecture. However, there are no systematic procedures in place to plan the data capture process. This means that the obtaining tasks of the clouds of points are based either on operator experience, or on the overlap register that grants a complete acquisition. In both cases, data redundancy represents a significant percentage, which forces subsequent filtration or point removal. This work describes the design and development of an automated methodology, based on genetic algorithms, for the selection of a set of positions from which to execute the data capture process. The algorithm designed herein is applied to a variety of cases, thereby attaining the best station-positioning network for data collection, which maximizes coverage and minimizes overlap between clouds of points.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/170973
https://doi.org/10.1016/j.measurement.2020.108898
url https://hdl.handle.net/11441/170973
https://doi.org/10.1016/j.measurement.2020.108898
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Measurement, 174, 108898.
https://www.sciencedirect.com/science/article/abs/pii/S026322412031383X?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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