Simulation to Reality Semantic Segmentation for Humanoid Soccer Robots

Deep learning methods like semantic segmentation have gained popularity in computer vision, but challenges remain, particularly in the lack of relevant datasets, such as for humanoid soccer robots. Manually annotating these datasets for segmentation is time-consuming and error-prone. To overcome this, we utilized realistic simulation to quickly generate large datasets and corresponding masks. This paper focuses on applying the u-net architecture on low-end hardware, using a synthetic dataset for training. To evaluate the model, a real annotated dataset was created. Although our Sim-to-Real approach produced a dataset close to reality, the results were unsatisfactory. To address this, transfer learning was employed to fine-tune the network and achieve better accuracy in a real environment.