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.

翻译：光学识别通常依赖于空间或时间上的视觉模式识别与定位。基于时间模式识别的技术需要在通信频率、通信范围与精确跟踪之间进行权衡。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的稀疏神经形态光流计算，改善了这一权衡。该系统嵌入于模拟无人机中，并在资产监控场景下进行了评估。它对相对运动具有鲁棒性，并能同时实现与多个移动信标的通信与跟踪。最后，在硬件实验室原型中，我们首次演示了信标跟踪与kHz级高频通信同时进行的性能。