Vehicle Localization Using 3D Building Models and Point Cloud Matching

Detecting buildings in the surroundings of an urban vehicle and matching them to building models available on map services is an emerging trend in robotics localization for urban vehicles. In this paper, we present a novel technique, which improves a previous work by detecting building facade, their...

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Autores: Ballardini, Augusto Luis|||0000-0001-6688-5081, Fontana, Simone, Cattaneo, Daniele, Matteucci, Matteo, Sorrenti, Domenico Giorgio
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/63593
Acceso en línea:http://hdl.handle.net/10017/63593
https://dx.doi.org/10.3390/s21165356
Access Level:acceso abierto
Palabra clave:Urban vehicle localization
Point cloud processing
Autonomous vehicle
Robot perception
Robótica e Informática Industrial
Robotics
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spelling Vehicle Localization Using 3D Building Models and Point Cloud MatchingBallardini, Augusto Luis|||0000-0001-6688-5081Fontana, SimoneCattaneo, DanieleMatteucci, MatteoSorrenti, Domenico GiorgioUrban vehicle localizationPoint cloud processingAutonomous vehicleRobot perceptionRobótica e Informática IndustrialRoboticsDetecting buildings in the surroundings of an urban vehicle and matching them to building models available on map services is an emerging trend in robotics localization for urban vehicles. In this paper, we present a novel technique, which improves a previous work by detecting building facade, their positions, and finding the correspondences with their 3D models, available in OpenStreetMap. The proposed technique uses segmented point clouds produced using stereo images, processed by a convolutional neural network. The point clouds of the facades are then matched against a reference point cloud, produced extruding the buildings' outlines, which are available on OpenStreetMap (OSM). In order to produce a lane-level localization of the vehicle, the resulting information is then fed into our probabilistic framework, called Road Layout Estimation (RLE). We prove the effectiveness of this proposal, testing it on sequences from the well-known KITTI dataset and comparing the results concerning a basic RLE version without the proposed pipeline.European UnionMarie Skłodowska Curie20212021-08-09journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/63593https://dx.doi.org/10.3390/s21165356reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)InglésengEuropean Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 754382 GETopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/635932026-06-18T11:13:07Z
dc.title.none.fl_str_mv Vehicle Localization Using 3D Building Models and Point Cloud Matching
title Vehicle Localization Using 3D Building Models and Point Cloud Matching
spellingShingle Vehicle Localization Using 3D Building Models and Point Cloud Matching
Ballardini, Augusto Luis|||0000-0001-6688-5081
Urban vehicle localization
Point cloud processing
Autonomous vehicle
Robot perception
Robótica e Informática Industrial
Robotics
title_short Vehicle Localization Using 3D Building Models and Point Cloud Matching
title_full Vehicle Localization Using 3D Building Models and Point Cloud Matching
title_fullStr Vehicle Localization Using 3D Building Models and Point Cloud Matching
title_full_unstemmed Vehicle Localization Using 3D Building Models and Point Cloud Matching
title_sort Vehicle Localization Using 3D Building Models and Point Cloud Matching
dc.creator.none.fl_str_mv Ballardini, Augusto Luis|||0000-0001-6688-5081
Fontana, Simone
Cattaneo, Daniele
Matteucci, Matteo
Sorrenti, Domenico Giorgio
author Ballardini, Augusto Luis|||0000-0001-6688-5081
author_facet Ballardini, Augusto Luis|||0000-0001-6688-5081
Fontana, Simone
Cattaneo, Daniele
Matteucci, Matteo
Sorrenti, Domenico Giorgio
author_role author
author2 Fontana, Simone
Cattaneo, Daniele
Matteucci, Matteo
Sorrenti, Domenico Giorgio
author2_role author
author
author
author
dc.subject.none.fl_str_mv Urban vehicle localization
Point cloud processing
Autonomous vehicle
Robot perception
Robótica e Informática Industrial
Robotics
topic Urban vehicle localization
Point cloud processing
Autonomous vehicle
Robot perception
Robótica e Informática Industrial
Robotics
description Detecting buildings in the surroundings of an urban vehicle and matching them to building models available on map services is an emerging trend in robotics localization for urban vehicles. In this paper, we present a novel technique, which improves a previous work by detecting building facade, their positions, and finding the correspondences with their 3D models, available in OpenStreetMap. The proposed technique uses segmented point clouds produced using stereo images, processed by a convolutional neural network. The point clouds of the facades are then matched against a reference point cloud, produced extruding the buildings' outlines, which are available on OpenStreetMap (OSM). In order to produce a lane-level localization of the vehicle, the resulting information is then fed into our probabilistic framework, called Road Layout Estimation (RLE). We prove the effectiveness of this proposal, testing it on sequences from the well-known KITTI dataset and comparing the results concerning a basic RLE version without the proposed pipeline.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-08-09
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/63593
https://dx.doi.org/10.3390/s21165356
url http://hdl.handle.net/10017/63593
https://dx.doi.org/10.3390/s21165356
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 754382 GET
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/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 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
repository.name.fl_str_mv
repository.mail.fl_str_mv
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