We present an autonomous exploration system for efficient coverage of unknown environments. First, a rapid environment preprocessing method is introduced to provide environmental information for subsequent exploration planning. Then, the whole exploration space is divided into multiple subregion cells, each with varying levels of detail. The subregion cells are capable of decomposition and updating online, effectively characterizing dynamic unknown regions with variable resolution. Finally, the hierarchical planning strategy treats subregions as basic planning units and computes an efficient global coverage path. Guided by the global path, the local path that sequentially visits the viewpoint set is refined to provide an executable path for the robot. This hierarchical planning from coarse to fine steps reduces the complexity of the planning scheme while improving exploration efficiency. The proposed method is compared with state-of-art methods in benchmark environments. Our approach demonstrates superior efficiency in completing exploration while using lower computational resources.

翻译：本文提出一种用于未知环境高效覆盖的自主探索系统。首先，引入快速环境预处理方法为后续探索规划提供环境信息。随后，将整个探索空间划分为多个具有不同细节层次的子区域单元。这些子区域单元支持在线分解与更新，能以可变分辨率有效表征动态未知区域。最后，分层规划策略将子区域作为基本规划单元，计算出高效的全局覆盖路径。在全局路径引导下，通过细化依次访问视点集的局部路径，为机器人提供可执行路径。这种从粗粒度到细粒度的分层规划策略在提升探索效率的同时降低了规划方案的复杂度。在基准环境中将所提方法与前沿方法进行比较，结果表明本方法在完成探索任务时具有更高的效率，且计算资源消耗更低。