The capability of autonomous exploration in complex, unknown environments is important in many robotic applications. While recent research on autonomous exploration have achieved much progress, there are still limitations, e.g., existing methods relying on greedy heuristics or optimal path planning are often hindered by repetitive paths and high computational demands. To address such limitations, we propose a novel exploration framework that utilizes the global topology information of observed environment to improve exploration efficiency while reducing computational overhead. Specifically, global information is utilized based on a skeletal topological graph representation of the environment geometry. We first propose an incremental skeleton extraction method based on wavefront propagation, based on which we then design an approach to generate a lightweight topological graph that can effectively capture the environment's structural characteristics. Building upon this, we introduce a finite state machine that leverages the topological structure to efficiently plan coverage paths, which can substantially mitigate the back-and-forth maneuvers (BFMs) problem. Experimental results demonstrate the superiority of our method in comparison with state-of-the-art methods. The source code will be made publicly available at: \url{https://github.com/Haochen-Niu/STGPlanner}.

翻译：在复杂未知环境中进行自主探索的能力在许多机器人应用中至关重要。尽管近期关于自主探索的研究已取得显著进展，但仍存在一些局限性，例如现有方法依赖贪婪启发式或最优路径规划，常受限于重复路径和高计算需求。为应对这些局限，我们提出了一种新颖的探索框架，该框架利用已观测环境的全局拓扑信息来提高探索效率，同时降低计算开销。具体而言，我们基于环境几何的骨架拓扑图表示来利用全局信息。我们首先提出了一种基于波前传播的增量式骨架提取方法，并在此基础上设计了一种生成轻量级拓扑图的方法，该图能有效捕捉环境的结构特征。基于此，我们引入了一个有限状态机，该状态机利用拓扑结构高效规划覆盖路径，从而能显著缓解来回机动问题。实验结果表明，与现有先进方法相比，我们的方法具有优越性。源代码将通过以下网址公开：\url{https://github.com/Haochen-Niu/STGPlanner}。