Event-driven Vision and Control for UAVs on a Neuromorphic Chip

TL;DR

This paper demonstrates a neuromorphic chip implementing event-driven vision and control for UAVs, achieving ultra-fast, low-latency, and adaptable drone control using a spiking neural network on hardware.

Contribution

It introduces the first neuromorphic vision-based controller for high-speed UAVs, integrating event-based perception and on-chip learning for fast, scalable control.

Findings

Achieved faster control rates with lower latency on neuromorphic hardware.

Demonstrated online adaptation of the SNN controller.

Showed scalability for more complex visual tasks.

Abstract

Event-based vision sensors achieve up to three orders of magnitude better speed vs. power consumption trade off in high-speed control of UAVs compared to conventional image sensors. Event-based cameras produce a sparse stream of events that can be processed more efficiently and with a lower latency than images, enabling ultra-fast vision-driven control. Here, we explore how an event-based vision algorithm can be implemented as a spiking neuronal network on a neuromorphic chip and used in a drone controller. We show how seamless integration of event-based perception on chip leads to even faster control rates and lower latency. In addition, we demonstrate how online adaptation of the SNN controller can be realised using on-chip learning. Our spiking neuronal network on chip is the first example of a neuromorphic vision-based controller solving a high-speed UAV control task. The excellent scalability of processing in neuromorphic hardware opens the possibility to solve more challenging visual tasks in the future and integrate visual perception in fast control loops.