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. In an asset monitoring use case, we demonstrate that the system, embedded in a simulated drone, is robust to relative movements and enables simultaneous communication with, and tracking of, multiple moving beacons. Finally, in a hardware lab prototype, we achieve state-of-the-art optical camera communication frequencies in the kHz magnitude.

翻译：光学识别通常通过空间或时间视觉模式识别与定位来实现。基于不同技术的时间模式识别需要在通信频率、作用距离与精准跟踪之间进行权衡。我们提出一种基于发光信标的解决方案，通过利用快速事件相机以及用于跟踪的、基于脉冲神经元计算的稀疏神经形态光流，显著改善了上述权衡关系。在资产监控应用场景中，我们证明了嵌入模拟无人机中的该系统能够抵抗相对运动，并实现与多个移动信标的同时通信与跟踪。最后，在硬件实验室原型中，我们实现了千赫兹量级的当前最优光学相机通信频率。