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arXiv · 2105.09843

Teat Pose Estimation via RGBD Segmentation for Automated Milking

Nicolas Borla·Fabian Kuster·Jonas Langenegger·Juan Ribera·Marcel Honegger·Giovanni Toffetti
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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.

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Nicolas Borla, Fabian Kuster, Jonas Langenegger, Juan Ribera, Marcel Honegger, Giovanni Toffetti. 2021-05-20. Teat Pose Estimation via RGBD Segmentation for Automated Milking. https://arxiv.org/abs/2105.09843

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