  This paper introduces DGBench, a fully reproducible open-source testing
system to enable benchmarking of dynamic grasping in environments with
unpredictable relative motion between robot and object. We use the proposed
benchmark to compare several visual perception arrangements. Traditional
perception systems developed for static grasping are unable to provide feedback
during the final phase of a grasp due to sensor minimum range, occlusion, and a
limited field of view. A multi-camera eye-in-hand perception system is
presented that has advantages over commonly used camera configurations. We
quantitatively evaluate the performance on a real robot with an image-based
visual servoing grasp controller and show a significantly improved success rate
on a dynamic grasping task.
