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Dynamic event-based optical identification and communication.

Axel von Arnim¹, Jules Lecomte¹, Naima Elosegui Borras^2,3

¹fortiss GmbH, Neuromorphic Computing, Munich, Germany.

Frontiers in Neurorobotics

|February 27, 2024

Summary

This summary is machine-generated.

This study introduces a novel optical identification system using event-based cameras and spiking neuromorphic optical flow for improved drone-based asset monitoring. It achieves simultaneous high-frequency communication and precise tracking of multiple beacons.

Keywords:

event-based sensing identification neuromorphic computing optical camera communication optical flow

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Area of Science:

Robotics and Computer Vision
Neuromorphic Engineering
Sensor Technology

Background:

Optical identification systems often face trade-offs between communication frequency, range, and tracking accuracy.
Existing temporal pattern recognition methods have limitations in balancing these parameters for applications like asset monitoring.
Drone-based systems require efficient and robust methods for simultaneous communication and localization.

Purpose of the Study:

To develop an improved optical identification system for drones that enhances the trade-off between communication and tracking.
To leverage event-based cameras and neuromorphic computing for faster and more accurate localization.
To enable simultaneous communication with and tracking of multiple moving targets in an asset monitoring scenario.

Main Methods:

Utilized fast event-based cameras for high-speed visual data acquisition.
Implemented sparse neuromorphic optical flow with spiking neurons for efficient and accurate beacon tracking.
Integrated the system into a simulated drone environment for asset monitoring simulations.
Developed a hardware lab prototype to validate performance in real-world conditions.

Main Results:

Demonstrated robustness to relative movements between the drone and beacons.
Enabled simultaneous communication and tracking of multiple moving beacons.
Achieved state-of-the-art frequency communication in the kHz range concurrently with beacon tracking for the first time.
Validated the system's effectiveness in a simulated asset monitoring use case.

Conclusions:

The proposed system significantly improves the trade-off between communication frequency, range, and tracking accuracy in optical identification.
Event-based cameras and neuromorphic optical flow offer a promising approach for advanced drone-based monitoring and communication.
This research marks a significant advancement in achieving simultaneous high-frequency communication and precise multi-target tracking in real-time applications.