Laser scanner and drone photogrammetry: A statistical comparison between 3-dimensional models and its impacts on outdoor crime scene registration
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.forsciint.2021.111100 http://hdl.handle.net/11449/222952 |
Resumo: | This work evaluated the accuracy of 3D models generated by a DJI Mavic Pro drone with 3DF Zephyr software photogrammetry. The models were compared to models generated by a Trimble X7 laser scanner. The tests were performed in the outdoor area of a vehicle parking inbound to simulate the characteristics of a crime scene. Ground control points (GCPs) were distributed in ten positions within the surroundings. In manual flight, the drone performed nadiral photographs from one side to the other side and with an elliptical 45° center pointed. Three altitudes where tested: 10 m, 20 m and 40 m. The Trimble X7 laser scanner performed six scans and generated one set of point clouds. Drone photogrammetry returned eligible data for distances of 20 m and 40 m with errors of ~0.25 mm. To increase the overlay in the photogrammetry procedure, all photographs from distances of 10–40 m were processed, returning an error of ~0.53 mm. The results of the measured distances, which were manually picked from the GCPs, from the 3D-scanned model and photogrammetric 3D models were then statistically analyzed. The Trimble X7 laser scanner showed an average error of 3 cm, which was approximately equivalent to the results obtained with all images or when using a known scale value for the drone photographs, presenting no significant differences among the evaluated methods. |
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Repositório Institucional da UNESP |
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Laser scanner and drone photogrammetry: A statistical comparison between 3-dimensional models and its impacts on outdoor crime scene registrationCrime sceneDroneLaser scanningPhotogrammetryPoint cloudUAVThis work evaluated the accuracy of 3D models generated by a DJI Mavic Pro drone with 3DF Zephyr software photogrammetry. The models were compared to models generated by a Trimble X7 laser scanner. The tests were performed in the outdoor area of a vehicle parking inbound to simulate the characteristics of a crime scene. Ground control points (GCPs) were distributed in ten positions within the surroundings. In manual flight, the drone performed nadiral photographs from one side to the other side and with an elliptical 45° center pointed. Three altitudes where tested: 10 m, 20 m and 40 m. The Trimble X7 laser scanner performed six scans and generated one set of point clouds. Drone photogrammetry returned eligible data for distances of 20 m and 40 m with errors of ~0.25 mm. To increase the overlay in the photogrammetry procedure, all photographs from distances of 10–40 m were processed, returning an error of ~0.53 mm. The results of the measured distances, which were manually picked from the GCPs, from the 3D-scanned model and photogrammetric 3D models were then statistically analyzed. The Trimble X7 laser scanner showed an average error of 3 cm, which was approximately equivalent to the results obtained with all images or when using a known scale value for the drone photographs, presenting no significant differences among the evaluated methods.Superintendência da Polícia Técnico Científica do Estado de São Paulo SPTCUniversidade Estadual Paulista UNESP – Centro Nacional de Monitoramento de Desastres Naturais - CEMADENUniversidade Estadual Paulista UNESP – Centro Nacional de Monitoramento de Desastres Naturais - CEMADENSuperintendência da Polícia Técnico Científica do Estado de São Paulo SPTCUniversidade Estadual Paulista (UNESP)Cunha, Rafael RodriguesArrabal, Claude ThiagoDantas, Marcelo MourãoBassaneli, Hélio Rodrigues [UNESP]2022-04-28T19:47:45Z2022-04-28T19:47:45Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.forsciint.2021.111100Forensic Science International, v. 330.1872-62830379-0738http://hdl.handle.net/11449/22295210.1016/j.forsciint.2021.1111002-s2.0-85120315395Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengForensic Science Internationalinfo:eu-repo/semantics/openAccess2022-04-28T19:47:45Zoai:repositorio.unesp.br:11449/222952Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:10:29.656490Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Laser scanner and drone photogrammetry: A statistical comparison between 3-dimensional models and its impacts on outdoor crime scene registration |
title |
Laser scanner and drone photogrammetry: A statistical comparison between 3-dimensional models and its impacts on outdoor crime scene registration |
spellingShingle |
Laser scanner and drone photogrammetry: A statistical comparison between 3-dimensional models and its impacts on outdoor crime scene registration Cunha, Rafael Rodrigues Crime scene Drone Laser scanning Photogrammetry Point cloud UAV |
title_short |
Laser scanner and drone photogrammetry: A statistical comparison between 3-dimensional models and its impacts on outdoor crime scene registration |
title_full |
Laser scanner and drone photogrammetry: A statistical comparison between 3-dimensional models and its impacts on outdoor crime scene registration |
title_fullStr |
Laser scanner and drone photogrammetry: A statistical comparison between 3-dimensional models and its impacts on outdoor crime scene registration |
title_full_unstemmed |
Laser scanner and drone photogrammetry: A statistical comparison between 3-dimensional models and its impacts on outdoor crime scene registration |
title_sort |
Laser scanner and drone photogrammetry: A statistical comparison between 3-dimensional models and its impacts on outdoor crime scene registration |
author |
Cunha, Rafael Rodrigues |
author_facet |
Cunha, Rafael Rodrigues Arrabal, Claude Thiago Dantas, Marcelo Mourão Bassaneli, Hélio Rodrigues [UNESP] |
author_role |
author |
author2 |
Arrabal, Claude Thiago Dantas, Marcelo Mourão Bassaneli, Hélio Rodrigues [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Superintendência da Polícia Técnico Científica do Estado de São Paulo SPTC Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Cunha, Rafael Rodrigues Arrabal, Claude Thiago Dantas, Marcelo Mourão Bassaneli, Hélio Rodrigues [UNESP] |
dc.subject.por.fl_str_mv |
Crime scene Drone Laser scanning Photogrammetry Point cloud UAV |
topic |
Crime scene Drone Laser scanning Photogrammetry Point cloud UAV |
description |
This work evaluated the accuracy of 3D models generated by a DJI Mavic Pro drone with 3DF Zephyr software photogrammetry. The models were compared to models generated by a Trimble X7 laser scanner. The tests were performed in the outdoor area of a vehicle parking inbound to simulate the characteristics of a crime scene. Ground control points (GCPs) were distributed in ten positions within the surroundings. In manual flight, the drone performed nadiral photographs from one side to the other side and with an elliptical 45° center pointed. Three altitudes where tested: 10 m, 20 m and 40 m. The Trimble X7 laser scanner performed six scans and generated one set of point clouds. Drone photogrammetry returned eligible data for distances of 20 m and 40 m with errors of ~0.25 mm. To increase the overlay in the photogrammetry procedure, all photographs from distances of 10–40 m were processed, returning an error of ~0.53 mm. The results of the measured distances, which were manually picked from the GCPs, from the 3D-scanned model and photogrammetric 3D models were then statistically analyzed. The Trimble X7 laser scanner showed an average error of 3 cm, which was approximately equivalent to the results obtained with all images or when using a known scale value for the drone photographs, presenting no significant differences among the evaluated methods. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-04-28T19:47:45Z 2022-04-28T19:47:45Z 2022-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.forsciint.2021.111100 Forensic Science International, v. 330. 1872-6283 0379-0738 http://hdl.handle.net/11449/222952 10.1016/j.forsciint.2021.111100 2-s2.0-85120315395 |
url |
http://dx.doi.org/10.1016/j.forsciint.2021.111100 http://hdl.handle.net/11449/222952 |
identifier_str_mv |
Forensic Science International, v. 330. 1872-6283 0379-0738 10.1016/j.forsciint.2021.111100 2-s2.0-85120315395 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Forensic Science International |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128473596690432 |