Exploring large-scale environments autonomously poses a significant challenge. As the size of environments increases, the computational cost becomes a hindrance to real-time operation. Additionally, while frontier-based exploration planning provides convenient access to environment frontiers, it suffers from slow global exploration speed. On the other hand, sampling-based methods can effectively explore individual regions but fail to cover the entire environment. To overcome these limitations, we present a hierarchical exploration approach that integrates frontier-based and sampling-based methods. It assesses the informational gain of sampling points by considering the quantity of frontiers in the vicinity, and effectively enhances exploration efficiency by utilizing a utility function that takes account of the direction of advancement for the purpose of selecting targets. To improve the search speed of global topological graph in large-scale environments, this paper introduces a method for constructing a sparse topological graph. It incrementally constructs a three-dimensional sparse topological graph by dynamically capturing the spatial structure of free space through uniform sampling. In various challenging simulated environments, the proposed approach demonstrates comparable exploration performance in comparison with the state-of-the-art approaches. Notably, in terms of computational efficiency, the single iteration time of our approach is less than one-tenth of that required by the recent advances in autonomous exploration.

自主探索大规模环境提出了重大挑战。随着环境规模的增加，计算成本成为实时操作的障碍。此外，虽然基于前沿的勘探规划可以方便地进入环境前沿，但其全球勘探速度缓慢。另一方面，基于采样的方法可以有效地探索各个区域，但无法覆盖整个环境。为了克服这些限制，我们提出了一种集成了基于前沿和基于采样的方法的分层探索方法。它通过考虑附近边界的数量来评估采样点的信息增益，并利用考虑前进方向的效用函数来选择目标，有效提高勘探效率。为了提高大规模环境下全局拓扑图的搜索速度，提出一种稀疏拓扑图的构造方法。它通过均匀采样动态捕获自由空间的空间结构，逐步构建三维稀疏拓扑图。在各种具有挑战性的模拟环境中，所提出的方法与最先进的方法相比表现出了可比的探索性能。值得注意的是，就计算效率而言，我们的方法的单次迭代时间不到自主探索最新进展所需的十分之一。