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：我们研究使用无人机对桥梁进行视觉表面缺陷检测的问题，不预先假设桥梁几何模型已知。所提出的规划器GATSBI采用滚动时域方式规划路径，以检测桥梁表面所有点。GATSBI的输入为通过激光雷达扫描在线生成的三维占据地图，该地图中对应桥梁的占据体素经过语义分割后构建仅含桥梁的占据地图。检测桥梁体素要求无人机从期望视角和距离拍摄图像。我们构建广义旅行商问题实例对检测桥梁体素的候选视点进行聚类，并使用通用GTSP求解器为给定实例寻找最优路径。随着算法逐步观测更多环境信息，它会重新规划路径以检测桥梁未知区域并规避障碍。我们通过AirSim高保真仿真与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。结果表明，相较于基线算法，仅对分割后的桥梁体素进行针对性检测并运用GTSP求解器精心规划路径，可实现更高效、更彻底的检测。