Publication:
Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry

dc.affiliation.dptoUC3M. Departamento de Ingeniería de Sistemas y Automáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Robótica (Robotics Lab)es
dc.contributor.authorMoreno Olivo, Francisco Miguel
dc.contributor.authorGuindel Gómez, Carlos
dc.contributor.authorArmingol Moreno, José María
dc.contributor.authorGarcía Fernández, Fernando
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2021-07-27T10:31:28Z
dc.date.available2021-07-27T10:31:28Z
dc.date.issued2020-08-02
dc.descriptionThis article belongs to the Special Issue Intelligent Transportation Systemsen
dc.description.abstractThis paper presents a study of how the performance of LiDAR odometry is affected by the preprocessing of the point cloud through the use of 3D semantic segmentation. The study analyzed the estimated trajectories when the semantic information is exploited to filter the original raw data. Different filtering configurations were tested: raw (original point cloud), dynamic (dynamic obstacles are removed from the point cloud), dynamic vehicles (vehicles are removed), far (distant points are removed), ground (the points belonging to the ground are removed) and structure (only structures and objects are kept in the point cloud). The experiments were performed using the KITTI and SemanticKITTI datasets, which feature different scenarios that allowed identifying the implications and relevance of each element of the environment in LiDAR odometry algorithms. The conclusions obtained from this work are of special relevance for improving the efficiency of LiDAR odometry algorithms in all kinds of scenarios.en
dc.description.sponsorshipResearch was supported by the Spanish Government through the CICYT projects (TRA2016-78886-C3-1-R and RTI2018-096036-B-C21) and the Comunidad de Madrid through SEGVAUTO-4.0-CM (P2018/EMT-4362) and PEAVAUTO-CM-UC3M.en
dc.format.extent12
dc.identifier.bibliographicCitationMoreno, F. M., Guindel, C., Armingol, J. M. & García, F. (2020). Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry. Applied Sciences, 10(16), 5657.en
dc.identifier.doihttps://doi.org/10.3390/app10165657
dc.identifier.issn2076-3417
dc.identifier.publicationfirstpage5657
dc.identifier.publicationissue16
dc.identifier.publicationtitleApplied Sciencesen
dc.identifier.publicationvolume10
dc.identifier.urihttps://hdl.handle.net/10016/33158
dc.identifier.uxxiAR/0000026670
dc.language.isoeng
dc.publisherMDPI
dc.relation.projectIDGobierno de España. TRA2016-78886-C3-1-Res
dc.relation.projectIDGobierno de España. RTI2018-096036-B-C21es
dc.relation.projectIDComunidad de Madrid. P2018/EMT-4362es
dc.rights© 2020 by the authors.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaRobótica e Informática Industriales
dc.subject.otherLocalizationen
dc.subject.otherIntelligent vehiclesen
dc.subject.otherLiDARen
dc.titleStudy of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometryen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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