Low-latency heading feedback control with neuromorphic vision sensors using efficient approximated incremental inference

Abstract

Asynchronous neuromorphic vision sensors have unique properties that make them ideal for high speed control applications. We consider a one dimensional simplification of a more general six dimensional trajectory tracking problem for mobile platforms, and present a computationally efficient method for feedback control that takes advantage of the asynchronous, event-based nature of these sensors to provide very high bandwidth and low latency feedback. This is an important step toward application of these incredible sensors to mobile robotic systems and useful in its own right. Through experimental tests we compare sensors and show that neuromorphic vision sensors can provide good closed loop performance in terms of computation, data rate, frequency and latency, and tracking error.