  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.
