Please use this identifier to cite or link to this item: https://hdl.handle.net/11000/31574
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dc.contributor.authorSanto López, Antonio-
dc.contributor.authorGil, Arturo-
dc.contributor.authorValiente, David-
dc.contributor.authorBallesta, Mónica-
dc.contributor.authorPeidro, Adrian-
dc.contributor.otherDepartamentos de la UMH::Ingeniería de Sistemas y Automáticaes_ES
dc.date.accessioned2024-02-28T11:29:54Z-
dc.date.available2024-02-28T11:29:54Z-
dc.date.created2023-11-
dc.identifier.citationProceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - (Volume 1)es_ES
dc.identifier.isbn978-989-758-670-5-
dc.identifier.issn2184-2809-
dc.identifier.urihttps://hdl.handle.net/11000/31574-
dc.description.abstractThe correct assessment of the environment in terms of traversability is strictly necessary during the navigation task in autonomous mobile robots. In particular, navigating along unknown, natural and unstructured environments requires techniques to select which areas can be traversed by the robot. In order to increase the autonomy of the system’s decisions, this paper proposes a method for the evaluation of 3D point clouds obtained by a LiDAR sensor in order to obtain the transitable areas, both in road and natural environments. Specifically, a trained sparse encoder-decoder configuration with rotation invariant features is proposed to replicate the input data by associating to each point the learned traversability features. Experimental results show the robustness and effectiveness of the proposed method in outdoor environments, improving the results of other approaches.es_ES
dc.formatapplication/pdfes_ES
dc.format.extent15es_ES
dc.language.isoenges_ES
dc.publisherINSTICC - Institute for Systems and Technologies of Information, Control and Communicationes_ES
dc.relation.ispartofProceedings of the 20th International Conference on Informatics in Control, Automation and Robotics Volume 1es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAutonomous Mobile Robotses_ES
dc.subjectArtificial Intelligencees_ES
dc.subjectNeural Networkses_ES
dc.subjectSparse Convolutiones_ES
dc.subjectPoint Cloudses_ES
dc.subject.otherCDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnologíaes_ES
dc.titleComputing the Traversability of the Environment by Means of Sparse Convolutional 3D Neural Networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.5220/0000168300003543es_ES
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Artículos Ingeniería de Sistemas y Automática


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