Por favor, use este identificador para citar o enlazar este ítem: http://bibdigital.epn.edu.ec/handle/15000/23900
Registro completo de metadatos
Campo DCValorLengua/Idioma
dc.contributor.authorMorales Escobar, Luis Alberto-
dc.contributor.authorNavas Flores, Diego Francisco-
dc.contributor.authorVargas Suasnavas, Jonathan Ramiro-
dc.date.accessioned2023-05-09T17:56:40Z-
dc.date.available2023-05-09T17:56:40Z-
dc.date.issued2017-11-
dc.identifier.citationVargas, J., Morales, L. y Navas, D. (2017). The nearest object localization through 3D lidar reconstruction using an embedded system. Memorias, XXVII Jornadas en Ingeniería Eléctrica y Electrónica, 27(4), 32-38.es_ES
dc.identifier.isbn978-9978-383-49-0-
dc.identifier.urihttp://bibdigital.epn.edu.ec/handle/15000/23900-
dc.description.abstractAbstract: This work presents a system capable of reconstructing a three-dimensional environment and from its information it finds where the nearest object is located. The 3D information, which was acquired through a LiDAR (Light Detection and Ranging) sensor, was processed as a point cloud using the python binding to the Point Cloud Library. The algorithms used in this work include PassThrough filter, statistical outlier removal and RANSAC. Finally, a kd-tree algorithm was used to find the closest points to the system and in this way, it is possible to find the nearest object. The developed system has as main devices a Hokuyo laser sensor and a Raspberry Pi 3. It was tested in indoor environments. The results show that the system can effectively locate the nearest object.es_ES
dc.language.isoenges_ES
dc.publisherQuito : EPN, 2017.es_ES
dc.rightsopenAccesses_ES
dc.subjectPOINT CLOUDes_ES
dc.subjectLASERes_ES
dc.subjectRASPBERRY PI 3es_ES
dc.subject3D RECONSTRUCTIONes_ES
dc.titleThe nearest object localization through 3D lidar reconstruction using an embedded system.es_ES
dc.typeArticlees_ES
Aparece en las colecciones:2017 Memorias de las XXVII Jornadas en Ingeniería Eléctrica y Electrónica (2017 J - FIEE)

Ficheros en este ítem:
Fichero Descripción TamañoFormato 
2017AJIEE-4.pdf5,36 MBAdobe PDFVisualizar/Abrir


Los ítems de DSpace están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.