LAEA: A 2D LiDAR-Assisted UAV Exploration Algorithm for Unknown Environments

Abstract

In UAV autonomous exploration, large frontier clusters are commonly associated with high information gain and are visited first. In contrast, small and isolated frontier clusters with fewer frontiers are associated with smaller information gain and are thus explored with low priority. However, these small and isolated frontier clusters are often in close proximity to UAVs and surrounded by explored areas, which could result in back-and-forth flights that lower exploration efficiency. This paper proposes LAEA, a LiDAR-assisted and depth camera-dominated UAV exploration algorithm that aims to improve UAV autonomous exploration efficiency. A hybrid map is obtained that characterizes rich environmental profile information in real time, enabling us to detect small and isolated frontier clusters that can lead to repeated visits to explored areas. An environmental information gain optimization strategy is incorporated such that frontier clusters with larger unexplored areas behind them, as well as small and isolated frontier clusters close to the UAV, are assigned higher weights to prioritize their visit order. An optimized flight trajectory is generated to cover unexplored frontier clusters in the immediate vicinity of the UAV while flying to the next target. A comprehensive comparison between the proposed algorithm and state-of-the-art algorithms was conducted via a simulation study, which showed that our algorithm exhibits superior exploration efficiency in various environments. Experiments were also carried out to verify the feasibility of the proposed approach in real-world scenarios.