Efficient Drone Exploration in Real Unknown Environments

Abstract

We propose an autonomous drone exploration system (ADES) with a lightweight and low-latency saliency prediction model to explore unknown environments. Recent studies have applied saliency prediction to drone exploration. However, these studies are not sufficiently mature. The ADES system proposes a smaller and faster saliency prediction model and adopts a novel drone exploration approach based on visual-inertial odometry (VIO) to solve the practical problems encountered during exploration, i.e., exploring salient objects without colliding with them and not repeatedly exploring salient objects. The system not only has a performance comparable to that of the state-of-the-art multiple-discontinuous-image saliency prediction network (TA-MSNet) but also enables drones to explore unknown environments more efficiently.