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.

本文提出了一种基于机器视觉和多层规划方案的无人机自主检测框架，用于评估爆破法隧道开挖表面的地理位置，能够在未知和动态的隧道环境中完成自主检测任务。