Bitte benutzen Sie diese Kennung, um auf die Ressource zu verweisen:
https://doi.org/10.21256/zhaw-22586| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Teat pose estimation via RGBD segmentation for automated milking |
| Autor/-in: | Borla, Nicolas Kuster, Fabian Langenegger, Jonas Ribera, Juan Honegger, Marcel Toffetti, Giovanni |
| et. al: | No |
| DOI: | 10.21256/zhaw-22586 |
| Angaben zur Konferenz: | Task-Informed Grasping: Agri-Food manipulation (TIG-III) Workshop at ICRA 2021, Xi’an, China, 30 May - 5 June 2021 |
| Erscheinungsdatum: | 20-Mai-2021 |
| Verlag / Hrsg. Institution: | ZHAW Zürcher Hochschule für Angewandte Wissenschaften |
| Sprache: | Englisch |
| Schlagwörter: | Computer science; Robotics |
| Fachgebiet (DDC): | 621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnik |
| Zusammenfassung: | We present initial results in the development of a novel robot using RGBD cameras, image segmentation, and a simple teat pose estimation algorithm for automated milking. We relate on the analysis of the accuracy of different commercial RGBD cameras in realistic conditions. Although preliminary, our initial implementation shows that 2D image segmentation combined with point cloud processing can achieve repeatable millimeter-scale precision in estimating (synthetic) teat tip positions and cup attachment approach. The solution is also applicable in a cloud robotics setup, with GPU-based segmentation executed on an edge device or cloud. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/22586 |
| Volltext Version: | Akzeptierte Version |
| Lizenz (gemäss Verlagsvertrag): | Keine Angabe |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Informatik (InIT) Institut für Mechatronische Systeme (IMS) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2021_Borla-etal_Teat-pose-estimation-RGBD-segmentation.pdf | Accepted Version | 2.14 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Borla, N., Kuster, F., Langenegger, J., Ribera, J., Honegger, M., & Toffetti, G. (2021, May 20). Teat pose estimation via RGBD segmentation for automated milking. Task-Informed Grasping: Agri-Food Manipulation (TIG-III) Workshop at ICRA 2021, Xi’an, China, 30 May - 5 June 2021. https://doi.org/10.21256/zhaw-22586
Borla, N. et al. (2021) ‘Teat pose estimation via RGBD segmentation for automated milking’, in Task-Informed Grasping: Agri-Food manipulation (TIG-III) Workshop at ICRA 2021, Xi’an, China, 30 May - 5 June 2021. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-22586.
N. Borla, F. Kuster, J. Langenegger, J. Ribera, M. Honegger, and G. Toffetti, “Teat pose estimation via RGBD segmentation for automated milking,” in Task-Informed Grasping: Agri-Food manipulation (TIG-III) Workshop at ICRA 2021, Xi’an, China, 30 May - 5 June 2021, May 2021. doi: 10.21256/zhaw-22586.
BORLA, Nicolas, Fabian KUSTER, Jonas LANGENEGGER, Juan RIBERA, Marcel HONEGGER und Giovanni TOFFETTI, 2021. Teat pose estimation via RGBD segmentation for automated milking. In: Task-Informed Grasping: Agri-Food manipulation (TIG-III) Workshop at ICRA 2021, Xi’an, China, 30 May - 5 June 2021. Conference paper. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. 20 Mai 2021
Borla, Nicolas, Fabian Kuster, Jonas Langenegger, Juan Ribera, Marcel Honegger, and Giovanni Toffetti. 2021. “Teat Pose Estimation via RGBD Segmentation for Automated Milking.” Conference paper. In Task-Informed Grasping: Agri-Food Manipulation (TIG-III) Workshop at ICRA 2021, Xi’an, China, 30 May - 5 June 2021. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-22586.
Borla, Nicolas, et al. “Teat Pose Estimation via RGBD Segmentation for Automated Milking.” Task-Informed Grasping: Agri-Food Manipulation (TIG-III) Workshop at ICRA 2021, Xi’an, China, 30 May - 5 June 2021, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2021, https://doi.org/10.21256/zhaw-22586.
Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt, soweit nicht anderweitig angezeigt.