Taming Event Cameras with Bio-Inspired Architecture and Algorithm: A Case for Drone Obstacle Avoidance

Abstract

Fast and accurate obstacle avoidance is crucial to drone safety. Yet existing on-board sensor modules such as frame cameras and radars are ill-suited for doing so due to their low temporal resolution or limited field of view. This paper presents BioDrone, a new design paradigm for drone obstacle avoidance using stereo event cameras. At the heart of BioDrone is two simple yet effective system design inspired by the mammalian visual system, namely, a chiasm-inspired signal processing pipeline for fast event filtering and obstacle detection, and a lateral geniculate nucleus (LGN)-inspired event matching algorithm for accurate obstacle localization. To make BioDrone a practical solution, we further take significant engineering efforts to deploy the software stack on FPGA through software and hardware co-design. The performance comparison with two state-of-the-art event-based obstacle avoidance systems shows BioDrone achieves a consistently high obstacle detection rate of 96.1%. The average localization error of BioDrone is 6.8cm with a 4.7ms latency, outperforming both baselines by over 40%.