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

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 are three simple yet effective system designs inspired by the mammalian visual system, namely, a chiasm-inspired event filtering, a lateral geniculate nucleus (LGN)-inspired event matching, and a dorsal stream-inspired obstacle tracking. We implement BioDrone on FPGA through software-hardware co-design and deploy it on an industrial drone. In comparative experiments against two state-of-the-art event-based systems, BioDrone consistently achieves an obstacle detection rate of $> $90%, and an obstacle tracking error of $<$5.8 cm across all flight modes with an end-to-end latency of $<$6.4 ms, outperforming both baselines by over 44%.