We study the problem of visual surface inspection of a bridge for defects using an Unmanned Aerial Vehicle (UAV). We do not assume that the geometric model of the bridge is known beforehand. Our planner, termed GATSBI, plans a path in a receding horizon fashion to inspect all points on the surface of the bridge. The input to GATSBI consists of a 3D occupancy map created online with LiDAR scans. Occupied voxels corresponding to the bridge in this map are semantically segmented and used to create a bridge-only occupancy map. Inspecting a bridge voxel requires the UAV to take images from a desired viewing angle and distance. We then create a Generalized Traveling Salesperson Problem (GTSP) instance to cluster candidate viewpoints for inspecting the bridge voxels and use an off-the-shelf GTSP solver to find the optimal path for the given instance. As the algorithm sees more parts of the environment over time, it replans the path to inspect novel parts of the bridge while avoiding obstacles. We evaluate the performance of our algorithm through high-fidelity simulations conducted in AirSim and real-world experiments. We compare the performance of GATSBI with a classical exploration algorithm. Our evaluation reveals that targeting the inspection to only the segmented bridge voxels and planning carefully using a GTSP solver leads to a more efficient and thorough inspection than the baseline algorithm.

翻译：本文研究了利用无人机（UAV）对桥梁表面缺陷进行视觉检测的问题。我们未预先假设桥梁的几何模型已知。我们提出的规划器GATSBI采用滚动时域方式规划路径，以检测桥梁表面的所有点位。GATSBI的输入包含通过激光雷达扫描在线创建的三维占据栅格地图。该地图中对应桥梁的占据体素经过语义分割，用于生成仅包含桥梁的占据地图。检测桥梁体素需要无人机从预设视角和距离拍摄图像。随后，我们构建广义旅行商问题（GTSP）实例，对检测桥梁体素的候选视点进行聚类，并采用现成的GTSP求解器计算给定实例的最优路径。随着算法对环境区域的持续观测，它会重新规划路径以检测桥梁的新区域，同时规避障碍物。我们通过AirSim高保真仿真和真实世界实验评估算法性能，并将GATSBI与经典探索算法进行对比。评估结果表明：通过针对分割后的桥梁体素进行定向检测，并利用GTSP求解器进行精细路径规划，相比基线算法能实现更高效、更全面的检测效果。