A Deep Learning-Enhanced Stereo Matching Method and Its Application to Bin Picking Problems Involving Tiny Cubic Workpieces

Abstract

This paper proposes a stereo matching method enhanced by object detection and instance segmentation results obtained through the use of a deep convolutional neural network. Then, this method is applied to generate a picking plan to solve bin picking problems, that is, to automatically pick up objects with random poses in a stack using a robotic arm. The system configuration and bin picking process flow are suggested using the proposed method, and it is applied to bin picking problems, especially those involving tiny cubic workpieces. The picking plan is generated by applying the Harris corner detection algorithm to the point cloud in the generated three-dimensional map. In the experiments, two kinds of stacks consisting of cubic workpieces with an edge length of 10 mm or 5 mm are tested for bin picking. In the first bin picking problem, all workpieces are successfully picked up, whereas in the second, the depths of the workpieces are obtained, but the instance segmentation process is not completed. In future work, not only cubic workpieces but also other arbitrarily shaped workpieces should be recognized in various types of bin picking problems.