Abstract
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。
URL
https://arxiv.org/abs/2303.07169
