Parameter configuration to maximize accuracy in point clouds acquired with lidar-based terrestrial mobile laser scanners in fruit tree orchards

The aim of this paper is to determine the optimal parameter configuration to minimize LiDAR point cloud errors and maximize accuracy for agronomic applications in field conditions in the framework of Precision Agriculture. The structure and geometry of trees are closely linked to vegetative paramete...

ver descrição completa

Detalhes bibliográficos
Autores: Lavaquiol Colell, Bernat, Escolà i Agustí, Alexandre, Arnó Satorra, Jaume, Grau, Joel, Ninot, Jérôme, Gómez, David, Llorens Calveras, Jordi
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2025
País:España
Recursos:Universitat de Lleida (UdL)
Repositório:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/469214
Acesso em linha:https://doi.org/10.1016/j.atech.2025.101573
https://hdl.handle.net/10459.1/469214
Access Level:Acceso aberto
Palavra-chave:Configuration of parameters
PPK and TC
SLAM
Point cloud error
LiDAR
id ES_4a2e9510e0f5d72df4e575f905a4a05e
oai_identifier_str oai:repositori.udl.cat:10459.1/469214
network_acronym_str ES
network_name_str España
repository_id_str
spelling Parameter configuration to maximize accuracy in point clouds acquired with lidar-based terrestrial mobile laser scanners in fruit tree orchardsLavaquiol Colell, BernatEscolà i Agustí, AlexandreArnó Satorra, JaumeGrau, JoelNinot, JérômeGómez, DavidLlorens Calveras, JordiConfiguration of parametersPPK and TCSLAMPoint cloud errorLiDARThe aim of this paper is to determine the optimal parameter configuration to minimize LiDAR point cloud errors and maximize accuracy for agronomic applications in field conditions in the framework of Precision Agriculture. The structure and geometry of trees are closely linked to vegetative parameters and productivity in fruit orchards. Therefore, achieving precise and accurate geometric characterization of tree canopies is fundamental for developing site-specific management strategies for resource optimization while reducing costs and environmental impacts associated with agricultural practices. Among the various sensing technologies available, LiDAR-based sensors have emerged as one of the preferred options for obtaining accurate geometric data in orchards. For this reason, the paper focuses on analyzing how different parameter configurations used during point cloud production contribute to reconstruction and location errors. Specifically, it examines the influence of type of SLAM algorithm (triple resolution or SLAM3 and feature or SLAMF), the GNSS constellations in use, orbit and clock products, trajectory correction (PPK or TC) and navigation constraint resolution, and the distance to the GNSS base station. Regarding the reconstruction error, since the maximum difference between the best configuration (featuring SLAM3 and PPK) and the worst (featuring SLAMF and TC) is just 0.006 m, all parameter configurations are capable of efficiently reconstructing point clouds. Location error analysis showed that GPS-based solutions consistently exceeded 0.1 m, while GNSS-based solutions - involving any combination of two or more constellations among GPS, GLONASS, Galileo, and BeiDou - remained below 0.07 m. The best configurations include SLAM3 paired with PPK, regardless of the navigation constraint resolution, achieving an error of 0.032 m in the most favorable case. Although PPK trajectories performed better in GNSS-based solutions, they were less effective than TC when only GPS-based solutions were available. Additionally, using PPK trajectories ensures better time efficiency, as they are a prerequisite for generating TC trajectories. SLAM3 outperformed SLAMF under the tested conditions. This study represents a significant advancement in the optimization of LiDAR-derived point clouds generation. By identifying and fine-tuning key parameter configurations, it effectively minimizes both reconstruction and location errors, while simultaneously enhancing the temporal efficiency of the point cloud production process.The present study is part of the Project PAgPROTECT (PID2021–126648OB-I00) funded by MICIU/AEI /10.13039/501100011033 and by FEDER/ERDF, EU.Elsevier2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.1016/j.atech.2025.101573https://hdl.handle.net/10459.1/469214reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)Inglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/PID2021-126648OB-I00Reproducció del document publicat a https://doi.org/10.1016/j.atech.2025.101573Smart Agricultural Technology, 2025, vol. 12, núm. 101573, p. 1-9cc-by (c) Lavaquiol et al., 2025Attribution 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:repositori.udl.cat:10459.1/4692142026-06-24T12:42:17Z
dc.title.none.fl_str_mv Parameter configuration to maximize accuracy in point clouds acquired with lidar-based terrestrial mobile laser scanners in fruit tree orchards
title Parameter configuration to maximize accuracy in point clouds acquired with lidar-based terrestrial mobile laser scanners in fruit tree orchards
spellingShingle Parameter configuration to maximize accuracy in point clouds acquired with lidar-based terrestrial mobile laser scanners in fruit tree orchards
Lavaquiol Colell, Bernat
Configuration of parameters
PPK and TC
SLAM
Point cloud error
LiDAR
title_short Parameter configuration to maximize accuracy in point clouds acquired with lidar-based terrestrial mobile laser scanners in fruit tree orchards
title_full Parameter configuration to maximize accuracy in point clouds acquired with lidar-based terrestrial mobile laser scanners in fruit tree orchards
title_fullStr Parameter configuration to maximize accuracy in point clouds acquired with lidar-based terrestrial mobile laser scanners in fruit tree orchards
title_full_unstemmed Parameter configuration to maximize accuracy in point clouds acquired with lidar-based terrestrial mobile laser scanners in fruit tree orchards
title_sort Parameter configuration to maximize accuracy in point clouds acquired with lidar-based terrestrial mobile laser scanners in fruit tree orchards
dc.creator.none.fl_str_mv Lavaquiol Colell, Bernat
Escolà i Agustí, Alexandre
Arnó Satorra, Jaume
Grau, Joel
Ninot, Jérôme
Gómez, David
Llorens Calveras, Jordi
author Lavaquiol Colell, Bernat
author_facet Lavaquiol Colell, Bernat
Escolà i Agustí, Alexandre
Arnó Satorra, Jaume
Grau, Joel
Ninot, Jérôme
Gómez, David
Llorens Calveras, Jordi
author_role author
author2 Escolà i Agustí, Alexandre
Arnó Satorra, Jaume
Grau, Joel
Ninot, Jérôme
Gómez, David
Llorens Calveras, Jordi
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Configuration of parameters
PPK and TC
SLAM
Point cloud error
LiDAR
topic Configuration of parameters
PPK and TC
SLAM
Point cloud error
LiDAR
description The aim of this paper is to determine the optimal parameter configuration to minimize LiDAR point cloud errors and maximize accuracy for agronomic applications in field conditions in the framework of Precision Agriculture. The structure and geometry of trees are closely linked to vegetative parameters and productivity in fruit orchards. Therefore, achieving precise and accurate geometric characterization of tree canopies is fundamental for developing site-specific management strategies for resource optimization while reducing costs and environmental impacts associated with agricultural practices. Among the various sensing technologies available, LiDAR-based sensors have emerged as one of the preferred options for obtaining accurate geometric data in orchards. For this reason, the paper focuses on analyzing how different parameter configurations used during point cloud production contribute to reconstruction and location errors. Specifically, it examines the influence of type of SLAM algorithm (triple resolution or SLAM3 and feature or SLAMF), the GNSS constellations in use, orbit and clock products, trajectory correction (PPK or TC) and navigation constraint resolution, and the distance to the GNSS base station. Regarding the reconstruction error, since the maximum difference between the best configuration (featuring SLAM3 and PPK) and the worst (featuring SLAMF and TC) is just 0.006 m, all parameter configurations are capable of efficiently reconstructing point clouds. Location error analysis showed that GPS-based solutions consistently exceeded 0.1 m, while GNSS-based solutions - involving any combination of two or more constellations among GPS, GLONASS, Galileo, and BeiDou - remained below 0.07 m. The best configurations include SLAM3 paired with PPK, regardless of the navigation constraint resolution, achieving an error of 0.032 m in the most favorable case. Although PPK trajectories performed better in GNSS-based solutions, they were less effective than TC when only GPS-based solutions were available. Additionally, using PPK trajectories ensures better time efficiency, as they are a prerequisite for generating TC trajectories. SLAM3 outperformed SLAMF under the tested conditions. This study represents a significant advancement in the optimization of LiDAR-derived point clouds generation. By identifying and fine-tuning key parameter configurations, it effectively minimizes both reconstruction and location errors, while simultaneously enhancing the temporal efficiency of the point cloud production process.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.atech.2025.101573
https://hdl.handle.net/10459.1/469214
url https://doi.org/10.1016/j.atech.2025.101573
https://hdl.handle.net/10459.1/469214
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/PID2021-126648OB-I00
Reproducció del document publicat a https://doi.org/10.1016/j.atech.2025.101573
Smart Agricultural Technology, 2025, vol. 12, núm. 101573, p. 1-9
dc.rights.none.fl_str_mv cc-by (c) Lavaquiol et al., 2025
Attribution 4.0 International
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) Lavaquiol et al., 2025
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869407469823852544
score 15.81155

Registros relacionados