Closing the Reality Gap with Unsupervised Sim-to-Real Image Translation for Semantic Segmentation in Robot Soccer

Deep learning approaches have become the standard solution to many problems in computer vision and robotics, but obtaining proper and sufficient training data is often a problem, as human labor is often error prone, time consuming and expensive. Solutions based on simulation have become more popular in recent years, but the gap between simulation and reality is still a major issue. In this paper, we introduce a novel model for augmenting synthetic image data through unsupervised image-to-image translation by applying the style of real world images to simulated images with open source frameworks. This model intends to generate the training data as a separate step and not as part of the training. The generated dataset is combined with conventional augmentation methods and is then applied to a neural network capable of running in real-time on autonomous soccer robots. Our evaluation shows a significant improvement compared to networks trained on simulated images without this kind of augmentation.