HPC Solutions for ALS Point Cloud Processing in Pathfinding and Powerline Detection and Characterization

This thesis addresses the processing of LiDAR point clouds using high-performance computing techniques. By employing efficient data structures and the shared-memory parallelization paradigm, two methods have been implemented for point cloud analysis. First, a path planning algorithm is used to find...

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Detalles Bibliográficos
Autor: Yermo, Miguel
Tipo de recurso: tesis doctoral
Fecha de publicación:2024
País:España
Institución:Universidad de Santiago de Compostela (USC)
Repositorio:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
Idioma:inglés
OAI Identifier:oai:minerva.usc.gal:10347/34947
Acceso en línea:http://hdl.handle.net/10347/34947
Access Level:acceso abierto
Palabra clave:330406 Arquitectura de ordenadores
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spelling HPC Solutions for ALS Point Cloud Processing in Pathfinding and Powerline Detection and CharacterizationYermo, Miguel330406 Arquitectura de ordenadoresThis thesis addresses the processing of LiDAR point clouds using high-performance computing techniques. By employing efficient data structures and the shared-memory parallelization paradigm, two methods have been implemented for point cloud analysis. First, a path planning algorithm is used to find the route between any two points within an airborne LiDAR point cloud, considering terrain features such as trafficability, slope, roughness, presence of vegetation, and roads. It is guaranteed that the found route is optimal in terms of cost. Second, the problem of detecting and characterizing powerlines in general-purpose airborne LiDAR point clouds has been tackled. The method can detect multiple powerlines in a given scene with a precision of 97.2%, and it can model the conductors with a mean error of 0.14 meters.Fernández Rivera, FranciscoFernández Pena, Anselmo TomásUniversidade de Santiago de Compostela. Escola de Doutoramento Internacional (EDIUS)20242024-01-0120242024-01-01doctoral thesishttp://purl.org/coar/resource_type/c_db06info:eu-repo/semantics/doctoralThesisapplication/pdfhttp://hdl.handle.net/10347/34947reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostelainstname:Universidad de Santiago de Compostela (USC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:minerva.usc.gal:10347/349472026-06-15T12:47:27Z
dc.title.none.fl_str_mv HPC Solutions for ALS Point Cloud Processing in Pathfinding and Powerline Detection and Characterization
title HPC Solutions for ALS Point Cloud Processing in Pathfinding and Powerline Detection and Characterization
spellingShingle HPC Solutions for ALS Point Cloud Processing in Pathfinding and Powerline Detection and Characterization
Yermo, Miguel
330406 Arquitectura de ordenadores
title_short HPC Solutions for ALS Point Cloud Processing in Pathfinding and Powerline Detection and Characterization
title_full HPC Solutions for ALS Point Cloud Processing in Pathfinding and Powerline Detection and Characterization
title_fullStr HPC Solutions for ALS Point Cloud Processing in Pathfinding and Powerline Detection and Characterization
title_full_unstemmed HPC Solutions for ALS Point Cloud Processing in Pathfinding and Powerline Detection and Characterization
title_sort HPC Solutions for ALS Point Cloud Processing in Pathfinding and Powerline Detection and Characterization
dc.creator.none.fl_str_mv Yermo, Miguel
author Yermo, Miguel
author_facet Yermo, Miguel
author_role author
dc.contributor.none.fl_str_mv Fernández Rivera, Francisco
Fernández Pena, Anselmo Tomás
Universidade de Santiago de Compostela. Escola de Doutoramento Internacional (EDIUS)
dc.subject.none.fl_str_mv 330406 Arquitectura de ordenadores
topic 330406 Arquitectura de ordenadores
description This thesis addresses the processing of LiDAR point clouds using high-performance computing techniques. By employing efficient data structures and the shared-memory parallelization paradigm, two methods have been implemented for point cloud analysis. First, a path planning algorithm is used to find the route between any two points within an airborne LiDAR point cloud, considering terrain features such as trafficability, slope, roughness, presence of vegetation, and roads. It is guaranteed that the found route is optimal in terms of cost. Second, the problem of detecting and characterizing powerlines in general-purpose airborne LiDAR point clouds has been tackled. The method can detect multiple powerlines in a given scene with a precision of 97.2%, and it can model the conductors with a mean error of 0.14 meters.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-01-01
2024
2024-01-01
dc.type.none.fl_str_mv doctoral thesis
http://purl.org/coar/resource_type/c_db06
dc.type.openaire.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10347/34947
url http://hdl.handle.net/10347/34947
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
instname:Universidad de Santiago de Compostela (USC)
instname_str Universidad de Santiago de Compostela (USC)
reponame_str Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
collection Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
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