Probabilistic Graph-based Real-Time Ground Segmentation for Urban Robotics
Empreu sempre aquest identificador per citar o enllaçar aquest ítem
http://hdl.handle.net/10045/142142
Registre complet
Camp Dublin Core | Valor | Idioma |
---|---|---|
dc.contributor | Automática, Robótica y Visión Artificial | es_ES |
dc.contributor.author | del Pino, Iván | - |
dc.contributor.author | Santamaria-Navarro, Angel | - |
dc.contributor.author | Garrell Zulueta, Anaís | - |
dc.contributor.author | Torres, Fernando | - |
dc.contributor.author | Andrade-Cetto, Juan | - |
dc.contributor.other | Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | es_ES |
dc.date.accessioned | 2024-04-15T09:26:55Z | - |
dc.date.available | 2024-04-15T09:26:55Z | - |
dc.date.issued | 2024-04-01 | - |
dc.identifier.citation | IEEE Transactions on Intelligent Vehicles. 2024. https://doi.org/10.1109/TIV.2024.3383599 | es_ES |
dc.identifier.issn | 2379-8858 (Print) | - |
dc.identifier.issn | 2379-8904 (Online) | - |
dc.identifier.uri | http://hdl.handle.net/10045/142142 | - |
dc.description.abstract | Terrain analysis is of paramount importance for the safe navigation of autonomous robots. In this study, we introduce GATA, a probabilistic real-time graph-based method for segmentation and traversability analysis of point clouds. In the method, we iteratively refine the parameters of a ground plane model and identify regions imaged by a LiDAR as traversable and non-traversable. The method excels in delivering rapid, high-precision obstacle detection, surpassing existing state-of-the-art methods. Furthermore, our method addresses the need to distinguish between surfaces with varying traversability, such as vegetation or unpaved roads, depending on the specific application. To achieve this, we integrate a shallow neural network, which operates on features extracted from the ground model. This enhancement not only boosts performance but also maintains real-time efficiency, without the need for GPUs. The method is rigorously evaluated using the SemanticKitti dataset and its practicality is showcased through real-world experiments with an urban last-mile delivery autonomous robot. The code is publicly available at https://gitlab.iri.upc.edu/idelpino/iri_ground_segmentation | es_ES |
dc.description.sponsorship | This work was partially supported by the EIT Urban Mobility project LOGISMILE (EIT-UM-2020-22140 and EIT-UM-2023-23374); the Spanish projects EBCON (PID2020-119244GB-I00, funded by MCIN/ AEI /10.13039/501100011033), AUDEL (TED2021-131759A-I00, funded by MCIN/ AEI /10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”) and LENA (PID2022-142039NA-I00, funded by MCIN/ AEI /10.13039/501100011033 and by “ERDF A way of making Europe”); by BotNet (23S06128-00, funded by the “Ajuntament de Barcelona” and “Fundació la Caixa”); by the Consolidated Research Group RAIG (2021 SGR 00510) of the Departament de Recerca i Universitats de la Generalitat de Catalunya; and by a Margarita Salas Fellowship to IDP (MARSALAS21-08) funded by the Spanish Ministry of Universities, the European Union-Next Generation, and the University of Alicante. | es_ES |
dc.language | eng | es_ES |
dc.publisher | IEEE | es_ES |
dc.rights | © 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission | es_ES |
dc.subject | Ground Segmentation | es_ES |
dc.subject | Terrain Analysis | es_ES |
dc.subject | Sequential Innovation | es_ES |
dc.subject | LiDAR | es_ES |
dc.title | Probabilistic Graph-based Real-Time Ground Segmentation for Urban Robotics | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.peerreviewed | si | es_ES |
dc.identifier.doi | 10.1109/TIV.2024.3383599 | - |
dc.relation.publisherversion | https://doi.org/10.1109/TIV.2024.3383599 | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119244GB-I00 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/TED2021-131759A-I00 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-142039NA-I00 | es_ES |
Apareix a la col·lecció: | INV - AUROVA - Artículos de Revistas |
Arxius per aquest ítem:
Arxiu | Descripció | Tamany | Format | |
---|---|---|---|---|
del-Pino_etal_2024_IEEE-TIV_accepted.pdf | Accepted Manuscript (acceso abierto) | 6,35 MB | Adobe PDF | Obrir Vista prèvia |
Tots els documents dipositats a RUA estan protegits per drets d'autors. Alguns drets reservats.