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：光学识别通常依赖于空间或时间视觉模式识别与定位。时间模式识别在技术上需权衡通信频率、范围与精确跟踪之间的关系。我们提出一种利用发光信标的解决方案，通过快速事件相机以及基于脉冲神经元计算的稀疏神经形态光流实现跟踪，从而改善上述权衡。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对相对运动具有鲁棒性，并能够同时与多个移动信标进行通信及跟踪。最终，在硬件实验室原型中，我们实现了千赫兹量级的光学相机通信频率，达到当前最优水平。