From 3D Point Cloud to Grasping by Using Deep Learning Techniques in Underwater Domain
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Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/158176
comunitat-uji-handle2:10234/71345
comunitat-uji-handle3:10234/174286
comunitat-uji-handle4:
TFG-TFMMetadatos
Título
From 3D Point Cloud to Grasping by Using Deep Learning Techniques in Underwater DomainAutoría
Tutor/Supervisor; Universidad.Departamento
Sanz Valero, Pedro José; Simetti, Enrico; Peñalver Monfort, Antonio; Universitat Jaume I. Departament d'Enginyeria i Ciència dels ComputadorsFecha de publicación
2017-09-15Editor
Universitat Jaume IResumen
This work is based on the detection of a couple of points for optimal and robust grasping
in an underwater domain. The objective is to provide a base for the development of a
routine able to autonomously gather ... [+]
This work is based on the detection of a couple of points for optimal and robust grasping
in an underwater domain. The objective is to provide a base for the development of a
routine able to autonomously gather information from the surrounding environment in
order to provide a robust grasp for underwater intervention robot such as the G500. For
this purpose a neural network and a point cloud processing are considered: the former is
meant to be able to classify shapes out of a segmented point cloud, the latter have the
purpose to reconstruct those shapes out of the classified pat of the point cloud scene. The
neural network has been described in its details and the routine explained step by step.
Eventually results obtained on real scenes are provided. [-]
Palabras clave / Materias
Descripción
Treball de Final de Màster Universitari Erasmus Mundus en Robòtica Avançada. Curs acadèmic 2016-2017
Tipo de documento
info:eu-repo/semantics/masterThesisDerechos de acceso
info:eu-repo/semantics/openAccess
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