EV-Catcher: High-Speed Object Catching Using Low-latency Event-based Neural Networks. (arXiv:2304.07200v1 [cs.RO])

Event-based sensors have recently drawn increasing interest in robotic
perception due to their lower latency, higher dynamic range, and lower
bandwidth requirements compared to standard CMOS-based imagers. These
properties make them ideal tools for real-time perception tasks in highly
dynamic environments. In this work, we demonstrate an application where event
cameras excel: accurately estimating the impact location of fast-moving
objects. We introduce a lightweight event representation called Binary Event
History Image (BEHI) to encode event data at low latency, as …

application arxiv binary cameras cmos data dynamic environments event excel history image impact inference latency location low moving networks neural networks objects perception real-time representation requirements sensors speed standard tools work