Event-Based cameras (also known as Dynamic Vision Sensors "DVS") have been used extensively in robotics during the last ten years and have proved the ability to solve many problems encountered in this domain. Their technology is very different from conventional cameras which requires rethinking the existing paradigms and reviewing all the classical image processing and computer vision algorithms. We show in this paper how Event-Based cameras are naturally adapted to estimate on the fly scene gradients and hence the visual flow. Our work starts with a complete study of existing event-based optical flow algorithms that are suitable to be integrated into real-time robotics applications. Then, we provide a data-set that includes different scenarios along with a set of visual flow ground-truth. Finally, we propose an evaluation of existing event-based visual flow algorithms using the proposed ground truth data-set.