Exploration and collision-free navigation through an unknown environment is a
fundamental task for autonomous robots. In this paper, a novel exploration
strategy for Micro Aerial Vehicles (MAVs) is presented. The goal of the
exploration strategy is the reduction of map entropy regarding occupancy
probabilities, which is reflected in a utility function to be maximised. We
achieve fast and efficient exploration performance with tight integration
between our octree-based occupancy mapping approach, frontier extraction, and
motion planning-as a hybrid between frontier-based and sampling-based
exploration methods. The computationally expensive frontier clustering employed
in classic frontier-based exploration is avoided by exploiting the implicit
grouping of frontier voxels in the underlying octree map representation.
Candidate next-views are sampled from the map frontiers and are evaluated using
a utility function combining map entropy and travel time, where the former is
computed efficiently using sparse raycasting. These optimisations along with
the targeted exploration of frontier-based methods result in a fast and
computationally efficient exploration planner. The proposed method is evaluated
using both simulated and real-world experiments, demonstrating clear advantages
over state-of-the-art approaches.