Ronen Feldman, Martin Charles Golumbic
Ann. Math. Artif. Intell.
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.
Ronen Feldman, Martin Charles Golumbic
Ann. Math. Artif. Intell.
Zhikun Yuen, Paula Branco, et al.
DSAA 2023
Freddy Lécué, Jeff Z. Pan
IJCAI 2013
Seung Gu Kang, Jeff Weber, et al.
ACS Fall 2023