Event-based Robotic Grasping Detection with Neuromorphic Vision Sensor and Event-Stream Dataset

Bin Li, Hu Cao, Zhongnan Qu, Yingbai Hu, Zhenke Wang, Zichen Liang

Robotic grasping plays an important role in the field of robotics. The current state-of-the-art robotic grasping detection systems are usually built on the conventional vision, such as RGB-D camera. Compared to traditional frame-based computer vision, neuromorphic vision is a small and young community of research. Currently, there are limited event-based datasets due to the troublesome annotation of the asynchronous event stream. Annotating large scale vision dataset often takes lots of computation resources, especially the troublesome data for video-level annotation. In this work, we consider the problem of detecting robotic grasps in a moving camera view of a scene containing objects. To obtain more agile robotic perception, a neuromorphic vision sensor (DAVIS) attaching to the robot gripper is introduced to explore the potential usage in grasping detection. We construct a robotic grasping dataset named \emph{Event-Stream Dataset} with 91 objects. For each object, there are $4020$ successive grasping annotations in different views with a time resolution of $1$ ms. A spatio-temporal mixed particle filter (SMP Filter) is proposed to track the led-based grasp rectangles which enables video-level annotation of a single grasp rectangle per object. As leds blink at high frequency, the \emph{Event-Stream} dataset is annotated in a high frequency of 1 kHz. Based on the \emph{Event-Stream} dataset, we develop a deep neural network for grasping detection which consider the angle learning problem as classification instead of regression. The method performs high detection accuracy on our \emph{Event-Stream} dataset with $93\%$ precision at object-wise level. This work provides a large-scale and well-annotated dataset, and promotes the neuromorphic vision applications in agile robot.

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