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dc.contributor.authorDyrstad, Jonatan Sjølund
dc.contributor.authorBakken, Marianne
dc.contributor.authorGrøtli, Esten Ingar
dc.contributor.authorSchulerud, Helene
dc.contributor.authorMathiassen, John Reidar Bartle
dc.date.accessioned2019-04-08T07:11:15Z
dc.date.available2019-04-08T07:11:15Z
dc.date.created2019-03-25T10:14:41Z
dc.date.issued2019
dc.identifier.citationProceedings of the 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO). Kuala Lumpur, Malaysia, 12-15 Dec. 2018, 530,537nb_NO
dc.identifier.isbn978-1-7281-0377-8
dc.identifier.urihttp://hdl.handle.net/11250/2593595
dc.description.abstractWe consider the case of robotic bin picking of reflective steel parts, using a structured light 3D camera as a depth imaging device. In this paper, we present a new method for bin picking, based on a dual-resolution convolutional neural network trained entirely in a simulated environment. The dual-resolution network consists of a high resolution focus network to compute the grasp and a low resolution context network to avoid local collisions. The reflectivity of the steel parts result in depth images that have a lot of missing data. To take this into account, training of the neural net is done by domain randomization on a large set of synthetic depth images that simulate the missing data problems of the real depth images. We demonstrate both in simulation and in a real-world test that our method can perform bin picking of reflective steel parts.nb_NO
dc.description.abstractBin Picking of Reflective Steel Parts Using a Dual-Resolution Convolutional Neural Network Trained in a Simulated Environmentnb_NO
dc.language.isoengnb_NO
dc.relation.ispartofProceedings of the 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)
dc.relation.urihttps://doi.org/10.1109/ROBIO.2018.8664766
dc.titleBin Picking of Reflective Steel Parts Using a Dual-Resolution Convolutional Neural Network Trained in a Simulated Environmentnb_NO
dc.typeChapternb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber530-537nb_NO
dc.identifier.cristin1687425
dc.relation.projectNorges forskningsråd: 262900nb_NO
cristin.unitcode7401,90,26,0
cristin.unitcode7401,90,33,0
cristin.unitcode7566,2,0,0
cristin.unitnameMathematics and Cybernetics
cristin.unitnameSmart Sensor Systems
cristin.unitnameSjømatteknologi
cristin.ispublishedtrue
cristin.fulltextpostprint


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