Dynamic Event-based Optical Identification and Communication
This addresses asset monitoring for drones by enabling simultaneous communication and tracking, though it appears incremental as it builds on existing event-based and neuromorphic methods.
The paper tackled the trade-off between communication frequency, range, and accurate tracking in optical identification by proposing a system using light-emitting beacons, event-based cameras, and neuromorphic optical flow. The result demonstrated robust tracking of multiple moving beacons with simultaneous kHz-range communication in a hardware prototype.
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.