  Rapid sampling from the environment to acquire available frontier points and
timely incorporating them into subsequent planning to reduce fragmented regions
are critical to improve the efficiency of autonomous exploration. We propose
HPHS, a fast and effective method for the autonomous exploration of unknown
environments. In this work, we efficiently sample frontier points directly from
the LiDAR data and the local map around the robot, while exploiting a
hierarchical planning strategy to provide the robot with a global perspective.
The hierarchical planning framework divides the updated environment into
multiple subregions and arranges the order of access to them by considering the
overall revenue of the global path. The combination of the hybrid frontier
sampling method and hierarchical planning strategy reduces the complexity of
the planning problem and mitigates the issue of region remnants during the
exploration process. Detailed simulation and real-world experiments demonstrate
the effectiveness and efficiency of our approach in various aspects. The source
code will be released to benefit the further research.
