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.

翻译：自主探索是机器人学中的一个关键方面，具有广泛的应用。现有方法大多贪婪地选择能最大化即时收益的目标，这种策略计算效率高，但不足以提升整体探索效率。近年来，一些先进方法通过生成全局覆盖路径显著提高了整体探索效率。然而，全局优化带来了高昂的计算开销，导致规划器更新频率低且规划运动不一致。本文提出一种新方法，支持无人机在大规模杂乱三维环境中快速探索。我们引入一种计算成本低的视点生成方法，利用新型无遮挡球体。此外，我们将贪婪策略与全局优化相结合，兼顾计算效率与探索效率。我们将该方法与现有先进方法进行基准测试，以展示其在探索效率和计算时间方面的优越性。我们开展了各种真实世界实验，证明了该方法在大规模杂乱环境中的优异性能。