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

• URL: http://arxiv.org/abs/2004.13652v2
• Date: Fri, 1 May 2020 16:59:14 GMT
• Title: Event-based Robotic Grasping Detection with Neuromorphic Vision Sensor and Event-Stream Dataset
• Authors: Bin Li, Hu Cao, Zhongnan Qu, Yingbai Hu, Zhenke Wang, and Zichen Liang
• Abstract summary: Neuromorphic vision is a small and young community of research. Compared to traditional frame-based computer vision, neuromorphic vision is a small and young community of research. We construct a robotic grasping dataset named Event-Stream dataset with 91 objects. As leds blink at high frequency, the Event-Stream dataset is annotated in a high frequency of 1 kHz. We develop a deep neural network for grasping detection which consider the angle learning problem as classification instead of regression.
• Score: 8.030163836902299
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 Event-Stream Dataset with 91 objects. 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 Event-Stream dataset is annotated in a high frequency of 1 kHz. Based on the 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 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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