Detection and depth estimation for domestic waste in outdoor environments by sensors fusion
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http://hdl.handle.net/10045/138769
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Camp Dublin Core | Valor | Idioma |
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dc.contributor | Automática, Robótica y Visión Artificial | es_ES |
dc.contributor.author | Páez Ubieta, Ignacio de Loyola | - |
dc.contributor.author | Velasco, Edison P. | - |
dc.contributor.author | Puente Méndez, Santiago T. | - |
dc.contributor.author | Candelas-Herías, Francisco A. | - |
dc.contributor.other | Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | es_ES |
dc.contributor.other | Universidad de Alicante. Instituto Universitario de Investigación Informática | es_ES |
dc.date.accessioned | 2023-11-27T12:39:23Z | - |
dc.date.available | 2023-11-27T12:39:23Z | - |
dc.date.issued | 2023-11-22 | - |
dc.identifier.citation | IFAC-PapersOnLine. 2023, 56(2): 9276-9281. https://doi.org/10.1016/j.ifacol.2023.10.211 | es_ES |
dc.identifier.issn | 2405-8963 | - |
dc.identifier.uri | http://hdl.handle.net/10045/138769 | - |
dc.description.abstract | In this work, we estimate the depth in which domestic waste are located in space from a mobile robot in outdoor scenarios. As we are doing this calculus on a broad range of space (0.3 - 6.0 m), we use RGB-D camera and LiDAR fusion. With this aim and range, we compare several methods such as average, nearest, median and center point, applied to those which are inside a reduced or non-reduced Bounding Box (BB). These BB are obtained from segmentation and detection methods which are representative of these techniques like Yolact, SOLO, You Only Look Once (YOLO)v5, YOLOv6 and YOLOv7. Results shown that, applying a detection method with the average technique and a reduction of BB of 40%, returns the same output as segmenting the object and applying the average method. Indeed, the detection method is faster and lighter in comparison with the segmentation one. The committed median error in the conducted experiments was 0.0298 ± 0.0544 m. | es_ES |
dc.description.sponsorship | Research work was funded by the Valencian Regional Government and FEDER through the PROMETEO/2021/075 project and the Spanish Government through the Formación del Personal Investigador [Research Staff Formation (FPI)] under Grant PRE2019-088069. The computer facilities were provided through the IDIFEFER/2020/003 project. | es_ES |
dc.language | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.rights | © 2023 The Authors. This is an open access article under the CC BY-NC-ND license. | es_ES |
dc.subject | Information and sensor fusion | es_ES |
dc.subject | Perception and sensing | es_ES |
dc.subject | Sensing | es_ES |
dc.subject | Autonomous mobile robots | es_ES |
dc.subject | Localization | es_ES |
dc.subject | Deep learning | es_ES |
dc.title | Detection and depth estimation for domestic waste in outdoor environments by sensors fusion | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.peerreviewed | si | es_ES |
dc.identifier.doi | 10.1016/j.ifacol.2023.10.211 | - |
dc.relation.publisherversion | https://doi.org/10.1016/j.ifacol.2023.10.211 | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
Apareix a la col·lecció: | INV - AUROVA - Comunicaciones a Congresos Internacionales |
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Paez-Ubieta_etal_2023_IFAC-PapersOnLine.pdf | 1,15 MB | Adobe PDF | Obrir Vista prèvia | |
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