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.

翻译：从环境中快速采样以获取可用前沿点，并及时将其纳入后续规划以减少碎片化区域，对于提高自主探索效率至关重要。我们提出HPHS，一种用于未知环境自主探索的快速有效方法。在本工作中，我们直接从激光雷达数据和机器人周围的局部地图中高效采样前沿点，同时利用分层规划策略为机器人提供全局视角。该分层规划框架将更新后的环境划分为多个子区域，并通过考虑全局路径的整体收益来安排其访问顺序。混合前沿采样方法与分层规划策略的结合降低了规划问题的复杂度，并缓解了探索过程中的区域残留问题。详细的仿真与真实世界实验证明了我们方法在多个方面的有效性和高效性。源代码将被公开以促进进一步研究。