| Autor: |
von Arnim, Axel, Lecomte, Jules, Borras, Naima Elosegui, Wozniak, Stanislaw, Pantazi, Angeliki |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Front. Neurorobot. 18:1290965 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.3389/fnbot.2024.1290965 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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