Ainetter, Stefan und Fraundorfer, Friedrich (2021) End-to-end trainable deep neural network for robotic grasp detection and semantic segmentation from RGB. In: 2021 IEEE International Conference on Robotics and Automation, ICRA 2021, Seiten 13452-13458. 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021-05-30 - 2021-06-05, Xi'an, China. doi: 10.1109/ICRA48506.2021.9561398. ISBN 978-172819077-8. ISSN 1050-4729.
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Offizielle URL: https://ieeexplore.ieee.org/document/9561398
Kurzfassung
In this work, we introduce a novel, end-to-end trainable CNN-based architecture to deliver high quality results for grasp detection suitable for a parallel-plate gripper, and semantic segmentation. Utilizing this, we propose a novel refinement module that takes advantage of previously calculated grasp detection and semantic segmentation and further increases grasp detection accuracy. Our proposed network delivers state-of-the-art accuracy on two popular grasp dataset, namely Cornell and Jacquard. As additional contribution, we provide a novel dataset extension for the OCID dataset, making it possible to evaluate grasp detection in highly challenging scenes. Using this dataset, we show that semantic segmentation can additionally be used to assign grasp candidates to object classes, which can be used to pick specific objects in the scene.
elib-URL des Eintrags: | https://elib.dlr.de/146134/ | ||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||
Titel: | End-to-end trainable deep neural network for robotic grasp detection and semantic segmentation from RGB | ||||||||||||
Autoren: |
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Datum: | 2021 | ||||||||||||
Erschienen in: | 2021 IEEE International Conference on Robotics and Automation, ICRA 2021 | ||||||||||||
Referierte Publikation: | Ja | ||||||||||||
Open Access: | Ja | ||||||||||||
Gold Open Access: | Nein | ||||||||||||
In SCOPUS: | Ja | ||||||||||||
In ISI Web of Science: | Ja | ||||||||||||
DOI: | 10.1109/ICRA48506.2021.9561398 | ||||||||||||
Seitenbereich: | Seiten 13452-13458 | ||||||||||||
ISSN: | 1050-4729 | ||||||||||||
ISBN: | 978-172819077-8 | ||||||||||||
Status: | veröffentlicht | ||||||||||||
Stichwörter: | Deep learning, Automation, Conferences, Semantics, Feature extraction, Convolutional neural networks, Grippers | ||||||||||||
Veranstaltungstitel: | 2021 IEEE International Conference on Robotics and Automation (ICRA) | ||||||||||||
Veranstaltungsort: | Xi'an, China | ||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||
Veranstaltungsbeginn: | 30 Mai 2021 | ||||||||||||
Veranstaltungsende: | 5 Juni 2021 | ||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Optische Fernerkundung, R - Künstliche Intelligenz | ||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > Photogrammetrie und Bildanalyse | ||||||||||||
Hinterlegt von: | Knickl, Sabine | ||||||||||||
Hinterlegt am: | 25 Nov 2021 11:15 | ||||||||||||
Letzte Änderung: | 24 Apr 2024 20:45 |
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