Fast Sampling-based Next-Best-View Exploration Algorithm for a MAV

In this work, we present a new exploration algorithm for Micro Aerial Vehicles (MAVs). The planner uses a combination of Next-Best-View (NBV) sampling and frontier-based approaches to reduce the impact of finding unexplored areas in large scenarios. For each sampled point, the yaw angle is optimized to maximize the potential gain for mapping. The gain is expressed as a ratio between the exploration objective and the time it would take to reach the pose, thus, balancing the nearby exploration and global coverage. We reduce the gain computation bottleneck in the sampling strategy by managing a dual-map with different resolutions. The planner maintains a history graph, with nodes that indicate regions of interest for map expansion. We demonstrate the abilities of the proposed algorithm with simulations and real-world experiments. The results outperform state-of-the-art methods in exploration time and computational cost.