A survey on ground segmentation methods for automotive LiDAR sensors
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/81696 |
Resumo: | In the near future, autonomous vehicles with full self-driving features will populate our public roads. However, fully autonomous cars will require robust perception systems to safely navigate the environment, which includes cameras, RADAR devices, and Light Detection and Ranging (LiDAR) sensors. LiDAR is currently a key sensor for the future of autonomous driving since it can read the vehicle’s vicinity and provide a real-time 3D visualization of the surroundings through a point cloud representation. These features can assist the autonomous vehicle in several tasks, such as object identification and obstacle avoidance, accurate speed and distance measurements, road navigation, and more. However, it is crucial to detect the ground plane and road limits to safely navigate the environment, which requires extracting information from the point cloud to accurately detect common road boundaries. This article presents a survey of existing methods used to detect and extract ground points from LiDAR point clouds. It summarizes the already extensive literature and proposes a comprehensive taxonomy to help understand the current ground segmentation methods that can be used in automotive LiDAR sensors. |
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A survey on ground segmentation methods for automotive LiDAR sensorsAutonomous drivingLiDARPerception systemGround segmentationSurveyEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaScience & TechnologyIndústria, inovação e infraestruturasIn the near future, autonomous vehicles with full self-driving features will populate our public roads. However, fully autonomous cars will require robust perception systems to safely navigate the environment, which includes cameras, RADAR devices, and Light Detection and Ranging (LiDAR) sensors. LiDAR is currently a key sensor for the future of autonomous driving since it can read the vehicle’s vicinity and provide a real-time 3D visualization of the surroundings through a point cloud representation. These features can assist the autonomous vehicle in several tasks, such as object identification and obstacle avoidance, accurate speed and distance measurements, road navigation, and more. However, it is crucial to detect the ground plane and road limits to safely navigate the environment, which requires extracting information from the point cloud to accurately detect common road boundaries. This article presents a survey of existing methods used to detect and extract ground points from LiDAR point clouds. It summarizes the already extensive literature and proposes a comprehensive taxonomy to help understand the current ground segmentation methods that can be used in automotive LiDAR sensors.This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope UIDB/00319/2020 and Grant 2021.06782.BD.MDPIUniversidade do MinhoGomes, Tiago Manuel RibeiroMatias, DiogoCampos, AndréCunha, LuísRoriz, Ricardo2023-01-052023-01-05T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/81696engGomes, T.; Matias, D.; Campos, A.; Cunha, L.; Roriz, R. A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors. Sensors 2023, 23, 601. https://doi.org/10.3390/s230206011424-82201424-822010.3390/s2302060136679414https://www.mdpi.com/1424-8220/23/2/601info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:50:24Zoai:repositorium.sdum.uminho.pt:1822/81696Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:49:07.272241Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A survey on ground segmentation methods for automotive LiDAR sensors |
title |
A survey on ground segmentation methods for automotive LiDAR sensors |
spellingShingle |
A survey on ground segmentation methods for automotive LiDAR sensors Gomes, Tiago Manuel Ribeiro Autonomous driving LiDAR Perception system Ground segmentation Survey Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática Science & Technology Indústria, inovação e infraestruturas |
title_short |
A survey on ground segmentation methods for automotive LiDAR sensors |
title_full |
A survey on ground segmentation methods for automotive LiDAR sensors |
title_fullStr |
A survey on ground segmentation methods for automotive LiDAR sensors |
title_full_unstemmed |
A survey on ground segmentation methods for automotive LiDAR sensors |
title_sort |
A survey on ground segmentation methods for automotive LiDAR sensors |
author |
Gomes, Tiago Manuel Ribeiro |
author_facet |
Gomes, Tiago Manuel Ribeiro Matias, Diogo Campos, André Cunha, Luís Roriz, Ricardo |
author_role |
author |
author2 |
Matias, Diogo Campos, André Cunha, Luís Roriz, Ricardo |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Gomes, Tiago Manuel Ribeiro Matias, Diogo Campos, André Cunha, Luís Roriz, Ricardo |
dc.subject.por.fl_str_mv |
Autonomous driving LiDAR Perception system Ground segmentation Survey Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática Science & Technology Indústria, inovação e infraestruturas |
topic |
Autonomous driving LiDAR Perception system Ground segmentation Survey Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática Science & Technology Indústria, inovação e infraestruturas |
description |
In the near future, autonomous vehicles with full self-driving features will populate our public roads. However, fully autonomous cars will require robust perception systems to safely navigate the environment, which includes cameras, RADAR devices, and Light Detection and Ranging (LiDAR) sensors. LiDAR is currently a key sensor for the future of autonomous driving since it can read the vehicle’s vicinity and provide a real-time 3D visualization of the surroundings through a point cloud representation. These features can assist the autonomous vehicle in several tasks, such as object identification and obstacle avoidance, accurate speed and distance measurements, road navigation, and more. However, it is crucial to detect the ground plane and road limits to safely navigate the environment, which requires extracting information from the point cloud to accurately detect common road boundaries. This article presents a survey of existing methods used to detect and extract ground points from LiDAR point clouds. It summarizes the already extensive literature and proposes a comprehensive taxonomy to help understand the current ground segmentation methods that can be used in automotive LiDAR sensors. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-01-05 2023-01-05T00:00:00Z |
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 |
https://hdl.handle.net/1822/81696 |
url |
https://hdl.handle.net/1822/81696 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Gomes, T.; Matias, D.; Campos, A.; Cunha, L.; Roriz, R. A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors. Sensors 2023, 23, 601. https://doi.org/10.3390/s23020601 1424-8220 1424-8220 10.3390/s23020601 36679414 https://www.mdpi.com/1424-8220/23/2/601 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799133071577972736 |