dc.contributor.author | Dyrstad, Jonatan Sjølund | |
dc.contributor.author | Bakken, Marianne | |
dc.contributor.author | Grøtli, Esten Ingar | |
dc.contributor.author | Schulerud, Helene | |
dc.contributor.author | Mathiassen, John Reidar Bartle | |
dc.date.accessioned | 2019-03-29T21:03:56Z | |
dc.date.available | 2019-03-29T21:03:56Z | |
dc.date.created | 2019-03-25T10:14:41Z | |
dc.date.issued | 2019-12-15 | |
dc.identifier.citation | Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO). Kuala Lumpur, Malaysia, 12-15 Dec. 2018 | nb_NO |
dc.identifier.isbn | 978-1-7281-0377-8 | |
dc.identifier.uri | http://hdl.handle.net/11250/2592554 | |
dc.description.abstract | We 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.abstract | Bin Picking of Reflective Steel Parts Using a Dual-Resolution Convolutional Neural Network Trained in a Simulated Environment | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | IEEE | nb_NO |
dc.relation.ispartof | Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO) | |
dc.relation.uri | https://doi.org/10.1109/ROBIO.2018.8664766 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no | * |
dc.subject | Three-dimensional displays | nb_NO |
dc.subject | Robots | nb_NO |
dc.subject | Grippers | nb_NO |
dc.subject | Training | nb_NO |
dc.subject | Steel | nb_NO |
dc.subject | Convolutional neural networks | nb_NO |
dc.title | Bin Picking of Reflective Steel Parts Using a Dual-Resolution Convolutional Neural Network Trained in a Simulated Environment | nb_NO |
dc.type | Chapter | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | 530-537 | nb_NO |
dc.identifier.doi | 10.1109/ROBIO.2018.8664766 | |
dc.identifier.cristin | 1687425 | |
dc.relation.project | Norges forskningsråd: 262900 | nb_NO |
cristin.unitcode | 7401,90,26,0 | |
cristin.unitcode | 7401,90,33,0 | |
cristin.unitcode | 7566,2,0,0 | |
cristin.unitname | Mathematics and Cybernetics | |
cristin.unitname | Smart Sensor Systems | |
cristin.unitname | Sjømatteknologi | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |