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 well as a learning-based approach that allows real-time inference of a confidence-enabled control signal to the robot. To validate our approach, we present an experimental catching system in which we catch fast-flying ping-pong balls. We show that the system is capable of achieving a success rate of 81% in catching balls targeted at different locations, with a velocity of up to 13 m/s even on compute-constrained embedded platforms such as the Nvidia Jetson NX.

翻译：事件传感器因相较于标准CMOS成像器具有更低的延迟、更高的动态范围和更低的带宽需求，近年来在机器人感知领域日益受到关注。这些特性使其成为高度动态环境中实时感知任务的理想工具。本研究展示了一个事件相机擅长的应用场景：精确估计快速移动物体的撞击位置。我们提出一种名为二值事件历史图像（BEHI）的轻量级事件表示方法，用于以低延迟编码事件数据，并设计了一种基于学习的方法，可实时向机器人输出带置信度的控制信号。为验证所提方法，我们搭建了一套实验性捕捉系统，用于捕捉高速飞行的乒乓球。实验表明，该系统能够以81%的成功率捕捉不同目标位置的球体，即使在使用计算受限的嵌入式平台（如Nvidia Jetson NX）时，仍可处理速度高达13 m/s的球体。