  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.
