Teat Pose Estimation via RGBD Segmentation for Automated Milking
This addresses the problem of automating milking for dairy farmers, but it is incremental as it builds on existing RGBD and segmentation techniques.
The researchers tackled teat pose estimation for automated milking by developing a robot using RGBD cameras and segmentation, achieving millimeter-scale precision in synthetic teat tip position estimation.
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.