Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-22586
Publication type: Conference paper
Type of review: Peer review (publication)
Title: Teat pose estimation via RGBD segmentation for automated milking
Authors: Borla, Nicolas
Kuster, Fabian
Langenegger, Jonas
Ribera, Juan
Honegger, Marcel
Toffetti, Giovanni
et. al: No
DOI: 10.21256/zhaw-22586
Conference details: Task-Informed Grasping: Agri-Food manipulation (TIG-III) Workshop at ICRA 2021, Xi’an, China, 30 May - 5 June 2021
Issue Date: 20-May-2021
Publisher / Ed. Institution: ZHAW Zürcher Hochschule für Angewandte Wissenschaften
Language: English
Subjects: Computer science; Robotics
Subject (DDC): 621.3: Electrical, communications, control engineering
Abstract: 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
Fulltext version: Accepted version
License (according to publishing contract): Not specified
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Institute of Mechatronic Systems (IMS)
Appears in collections:Publikationen School of Engineering

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