Optical identification is often done with spatial or temporal visual pattern recognition and localization. Temporal pattern recognition, depending on the technology, involves a trade-off between communication frequency, range, and accurate tracking. We propose a solution with light-emitting beacons that improves this trade-off by exploiting fast event-based cameras and, for tracking, sparse neuromorphic optical flow computed with spiking neurons. The system is embedded in a simulated drone and evaluated in an asset monitoring use case. It is robust to relative movements and enables simultaneous communication with, and tracking of, multiple moving beacons. Finally, in a hardware lab prototype, we demonstrate for the first time beacon tracking performed simultaneously with state-of-the-art frequency communication in the kHz range.

中文翻译：

---

基于动态事件的光学识别和通信

光学识别通常通过空间或时间视觉模式识别和定位来完成。根据技术的不同，时间模式识别涉及通信频率、范围和准确跟踪之间的权衡。我们提出了一种带有发光信标的解决方案，通过利用基于事件的快速相机以及用于跟踪的用尖峰神经元计算的稀疏神经形态光流来改善这种权衡。该系统嵌入模拟无人机中，并在资产监控用例中进行评估。它对相对运动具有鲁棒性，并且能够与多个移动信标同时通信和跟踪。最后，在硬件实验室原型中，我们首次演示了信标跟踪与 kHz 范围内最先进的频率通信同时执行。