Rapid Dynamic Obstacle Avoidance for UAVs Enhanced by DVS and Neuromorphic Computing

Abstract

Achieving rapid and accurate dynamic obstacle avoidance is crucial for enhancing the survivability of unmanned aerial vehicles (UAVs) in hazardous conditions. To accomplish dynamic obstacle avoidance, sensors with high temporal resolution and efficient processing models are required. Dynamic vision sensors (DVS) fulfill the sensing requirements, while spiking neural networks (SNNs) address the processing demands. In this paper, we develop an end-to-end obstacle avoidance algorithm for UAVs using only a single monocular DVS as the sensor and further enhance accuracy and speed through our proposed mechanisms. The algorithm consists of three components: ego-motion compensation, an SNN model for movement analysis, and a force filter inspired by spiking neurons. In movement analysis, we propose the temporal potential pooling (TPP) and incremental event (EI) mechanisms to accelerate our SNN model. The real-flight experiments confirm that our algorithm achieves approximately 90% accuracy with a processing latency as low as 4ms on a GPU, surpassing state-of-the-art methods. Ablation studies show that the proposed method maintains high accuracy in movement detection while significantly reducing computational time. Our method operates in real-time, achieves high accuracy, and is feasible across a wide range of environments. Our code is available at https://github.com/AmperiaWang/oanet_s1 for reproducibility.