Construction of Virtual Datasets for Bin Picking

Zhuang YANG, Jian-jun YI, Liang HE, Ya-jun ZHANG, Bo ZHOU


Bin Picking is a typical problem for identifying and sorting complex stacked objects, the difficulty of which lies in the identification of objects. This paper uses deep learning method to identify objects preliminarily. Because the deep learning method requires high-quality datasets, the traditional manual annotation method is too difficult to complete under the industrial production environment. Therefore, this paper proposes a method to build virtual datasets based on virtual simulation technology. It uses OpenGL technology and Bullet physics engine to build virtual scenes. Then through camera loading and scene drawing, the datasets including RGB images, segmentation annotation images and so on are generated. Experimental results show that the model trained on virtual datasets by deep learning can achieve good accuracy, so the feasibility and practical application value of the method are verified.


Bin Picking, Virtual Dataset, Virtual Simulation


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