Tunnel construction using the drill-and-blast method requires the 3D measurement of the excavation front to evaluate underbreak locations. Considering the inspection and measurement task's safety, cost, and efficiency, deploying lightweight autonomous robots, such as unmanned aerial vehicles (UAV), becomes more necessary and popular. Most of the previous works use a prior map for inspection viewpoint determination and do not consider dynamic obstacles. To maximally increase the level of autonomy, this paper proposes a vision-based UAV inspection framework for dynamic tunnel environments without using a prior map. Our approach utilizes a hierarchical planning scheme, decomposing the inspection problem into different levels. The high-level decision maker first determines the task for the robot and generates the target point. Then, the mid-level path planner finds the waypoint path and optimizes the collision-free static trajectory. Finally, the static trajectory will be fed into the low-level local planner to avoid dynamic obstacles and navigate to the target point. Besides, our framework contains a novel dynamic map module that can simultaneously track dynamic obstacles and represent static obstacles based on an RGB-D camera. After inspection, the Structure-from-Motion (SfM) pipeline is applied to generate the 3D shape of the target. To our best knowledge, this is the first time autonomous inspection has been realized in unknown and dynamic tunnel environments. Our flight experiments in a real tunnel prove that our method can autonomously inspect the tunnel excavation front surface.

翻译：采用钻爆法进行隧道施工时，需对开挖掌子面进行三维测量以评估欠挖区域。考虑到检测与测量任务的安全性、经济性和效率，部署轻量级自主机器人（如无人机）的必要性与日俱增。现有研究多依赖先验地图确定检测视点，且未考虑动态障碍物。为最大限度提升自主性，本文提出一种无需先验地图的基于视觉的无人机动态隧道检测框架。该方法采用分层规划方案，将检测问题分解为不同层级：高层决策器首先确定机器人任务并生成目标点；中层路径规划器寻找航路点路径并优化无碰撞静态轨迹；最后将静态轨迹输入底层局部规划器以规避动态障碍物并导航至目标点。此外，本框架包含基于RGB-D相机的动态地图模块，可同时追踪动态障碍物与表征静态障碍物。检测完成后，采用运动恢复结构（SfM）流程生成目标三维形貌。据我们所知，这是首次在未知动态隧道环境中实现自主检测。在实际隧道的飞行实验证明，该方法可自主检测隧道开挖掌子面。