Cut-paste image generation for instance segmentation for robotic picking of industrial parts

Abstract

AbstractVision-based robotic picking enables automation of commissioning and sortation of disordered parts. To locate parts for grasping, state-of-the-art approaches rely on convolutional neural networks for instance segmentation in 2D images. However, this requires sufficiently large training datasets, which are expensive to capture and annotate. Therefore, training with synthetic data is promising as the data can be generated automatically. We present an approach for the cut-paste method to create synthetic images for industrial use cases. With this approach, an end-user first prepares the image generation with just a smartphone and about 20 minutes of manual effort. Then, a versatile dataset with instance segmentation labels is generated automatically. In addition, a procedure for grasp pose computation is applied to enable robotic picking based on instance segmentation. For evaluation, training data is generated for a wide range of rigid parts and deformable linear objects. Testing with real-world data and practical experiments demonstrates the effectiveness of the proposed cut-paste method for industrial applications.