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：光学识别通常通过空间或时间视觉模式识别与定位实现。基于时间模式识别的方法，根据技术差异，需要在通信频率、作用距离与精确跟踪之间进行权衡。我们提出一种采用发光信标的解决方案，通过利用快速事件相机进行跟踪，并采用脉冲神经元计算的稀疏神经形态光流，改善了上述权衡关系。该系统嵌入在仿真无人机中，并在资产监控用例中进行了评估。该方案对相对运动具有鲁棒性，能够同时对多个移动信标进行通信与跟踪。最后，在硬件实验室原型中，我们首次演示了在千赫兹频段内同时实现信标跟踪与最先进频率通信的性能验证。