Vision guided robotics (VGR)

For reasons of flexibility and adaptability, there is a trend in the bin picking segment moving from rule-based or classical computer vision (CV) to computer vision based on deep learning. To facilitate robust and reliable performance in object identification and segmentation tasks leading to successful gripping of an object, the deep learning algorithms require large datasets that represent variations in objects, textures and environmental variables.
Capturing real data with 3D cameras/sensors in a lab before the algorithms are deployed in a manufacturing or logistics environment and also the training on a production site is a time, efforts and cost intensive process. This is significant especially in case of organic objects, object categories where each unit always has a unique look and shape or generic robotic vision applications, which need to be trained on hundred to thousands of different objects.