Real-Time Grasping Strategies Using Event Camera
Xiaoqian Huang, Mohamad Halwani, Rajkumar Muthusamy, Abdulla Ayyad,, Dewald Swart, Lakmal Seneviratne, Dongming Gan, Yahya Zweiri

TL;DR
This paper introduces a novel event-based robotic grasping framework utilizing neuromorphic vision, enabling real-time, robust grasping of known and unknown objects in cluttered and low-light environments, outperforming traditional frame-based methods.
Contribution
It presents the first event-based grasping framework with model-based and model-free approaches, enhancing robustness and performance in challenging conditions.
Findings
Successful grasping of various objects in cluttered scenes
Effective operation in low-light environments
Model-free approach handles unknown objects without prior knowledge
Abstract
Robotic vision plays a key role for perceiving the environment in grasping applications. However, the conventional framed-based robotic vision, suffering from motion blur and low sampling rate, may not meet the automation needs of evolving industrial requirements. This paper, for the first time, proposes an event-based robotic grasping framework for multiple known and unknown objects in a cluttered scene. Compared with standard frame-based vision, neuromorphic vision has advantages of microsecond-level sampling rate and no motion blur. Building on that, the model-based and model-free approaches are developed for known and unknown objects' grasping respectively. For the model-based approach, event-based multi-view approach is used to localize the objects in the scene, and then point cloud processing allows for the clustering and registering of objects. Differently, the proposed…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Neuroscience and Neural Engineering · Ferroelectric and Negative Capacitance Devices
