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Improving Grasping Performance by Segmentation of Large Planar Surface

dc.contributor.authorLopes, Vasco
dc.contributor.authorAlexandre, Luís
dc.date.accessioned2020-01-09T12:13:42Z
dc.date.available2020-01-09T12:13:42Z
dc.date.issued2017-10
dc.description.abstractGrasping objects is a task that humans do without major concerns. This results from learning and observing other skilled humans doing such task and with previous information, unconsciously, we know how to pick up different types of objects. However, grasping novel objects in unknown positions for a robot is a complex task which encounters many problems, such as the performance rates that are not perfect and the time consumption. In this paper we present a method that complements the state-ofthe- art grasping by removing the largest planar surface of the image of the world before the grasp detector receives them. The proposed method improves the performance rate and is also capable of reducing the time consumption.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/8158
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.titleImproving Grasping Performance by Segmentation of Large Planar Surfacept_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.title23rd Portuguese Conference on Pattern Recognition, RECPAD 2017pt_PT
person.familyNameFERRINHO LOPES
person.familyNameAlexandre
person.givenNameVASCO
person.givenNameLuís
person.identifierbvYBcRkAAAAJ
person.identifier.ciencia-id2516-C038-0DC1
person.identifier.ciencia-id2014-0F06-A3E3
person.identifier.orcid0000-0002-5577-1094
person.identifier.orcid0000-0002-5133-5025
person.identifier.ridE-8770-2013
person.identifier.scopus-author-id8847713100
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationa8a77d1f-0219-4739-a346-1b1ae649a47d
relation.isAuthorOfPublication131ec6eb-b61a-4f27-953f-12e948a43a96
relation.isAuthorOfPublication.latestForDiscoverya8a77d1f-0219-4739-a346-1b1ae649a47d

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