Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : June 05, 2019 - June 08, 2019
Since foods have irregular shapes, their individual recognition is difficult. We have previously proposed a food detection method for piled pieces of fried chicken. However, the method fails to detect a piece under certain circumstances due to a clustering error when the method handles a 3-dimentional point cloud. For the clustering process, we newly propose to use a soft clustering method in which each point can belong to more than one clusters if needed. The ambiguity does not become a problem for manipulation tasks and it will enhances the robustness of the clustering process. In this paper, we measure the improvement of accuracy with the new method.
