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.

翻译：在复杂杂乱环境中进行自主探索对于各类应用至关重要。然而，由于缺乏全局启发式信息，该任务面临诸多挑战。现有探索方法在大规模环境中存在路径重复和计算资源需求大的问题。为解决上述问题，本文提出一种高效探索规划器，旨在减少复杂环境中的重复路径，故称为“一次性遍历规划器”。OTO规划器包含快速前沿更新、视点评估与视点优化三个模块。设计了一种选择性前沿更新机制，可节省大量计算资源。此外，开发了基于封闭子区域检测的新型视点评估系统以减少重复路径。同时提出视点优化方法以整合冗余视点，从而生成更平滑的路径。我们通过大量仿真与实物实验验证了所提方法。相比现有先进方法，本文方法将探索时间与移动距离降低了10%-20%，并将前沿检测速度提升了6-9倍。