From 3D Point Cloud to Grasping by Using Deep Learning Techniques in Underwater Domain
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Show full item recordcomunitat-uji-handle:10234/158176
comunitat-uji-handle2:10234/71345
comunitat-uji-handle3:10234/174286
comunitat-uji-handle4:
TFG-TFMMetadata
Title
From 3D Point Cloud to Grasping by Using Deep Learning Techniques in Underwater DomainAuthor (s)
Tutor/Supervisor; University.Department
Sanz Valero, Pedro José; Simetti, Enrico; Peñalver Monfort, Antonio; Universitat Jaume I. Departament d'Enginyeria i Ciència dels ComputadorsDate
2017-09-15Publisher
Universitat Jaume IAbstract
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. [-]
Subject
Description
Treball de Final de Màster Universitari Erasmus Mundus en Robòtica Avançada. Curs acadèmic 2016-2017
Type
info:eu-repo/semantics/masterThesisRights
info:eu-repo/semantics/openAccess
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