  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.
