Autonomous exploration in complex and cluttered environments is essential for
various applications. However, there are many challenges due to the lack of
global heuristic information. Existing exploration methods suffer from the
repeated paths and considerable computational resource requirement in
large-scale environments. To address the above issues, this letter proposes an
efficient exploration planner that reduces repeated paths in complex
environments, hence it is called "Only Travelling Once Planner". OTO Planner
includes fast frontier updating, viewpoint evaluation and viewpoint refinement.
A selective frontier updating mechanism is designed, saving a large amount of
computational resources. In addition, a novel viewpoint evaluation system is
devised to reduce the repeated paths utilizing the enclosed sub-region
detection. Besides, a viewpoint refinement approach is raised to concentrate
the redundant viewpoints, leading to smoother paths. We conduct extensive
simulation and real-world experiments to validate the proposed method. Compared
to the state-of-the-art approach, the proposed method reduces the exploration
time and movement distance by 10%-20% and improves the speed of frontier
detection by 6-9 times.

提出了一种高效的探索规划器，用于减少复杂环境中的重复路径，从而降低探索时间和移动距离，并提高前沿检测的速度。