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Dynamic Event-based Optical Identification and Communication

2023-03-14 21:39:04
Axel von Arnim, Jules Lecomte, Stanislaw Wozniak, Naima Elosegui Borras, Angeliki Pantazi


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.

Abstract (translated)

光学识别通常与空间或时间的视觉模式识别和定位相结合。根据技术,时间模式识别涉及通信频率、范围和准确的跟踪之间的权衡。我们提出了一种解决方案,使用发光 beacons,通过利用快速事件基于摄像头改善权衡,并使用基于突触神经元的计算稀疏神经形态学光学流进行跟踪。在一个资产监测使用场景中,我们演示了该系统在模拟无人机中的稳定性,对相对运动进行鲁棒性,并同时与多个移动 beacon 进行通信和跟踪。最后,在硬件实验室原型中,我们实现了高分辨率光学相机通信频率在 kHz 量级上。



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