  Autonomous exploration is a crucial aspect of robotics that has numerous
applications. Most of the existing methods greedily choose goals that maximize
immediate reward. This strategy is computationally efficient but insufficient
for overall exploration efficiency. In recent years, some state-of-the-art
methods are proposed, which generate a global coverage path and significantly
improve overall exploration efficiency. However, global optimization produces
high computational overhead, leading to low-frequency planner updates and
inconsistent planning motion. In this work, we propose a novel method to
support fast UAV exploration in large-scale and cluttered 3-D environments. We
introduce a computationally low-cost viewpoints generation method using novel
occlusion-free spheres. Additionally, we combine greedy strategy with global
optimization, which considers both computational and exploration efficiency. We
benchmark our method against state-of-the-art methods to showcase its
superiority in terms of exploration efficiency and computational time. We
conduct various real-world experiments to demonstrate the excellent performance
of our method in large-scale and cluttered environments.
