We study the problem of visual surface inspection of infrastructure for defects using an Unmanned Aerial Vehicle (UAV). We do not assume that the geometric model of the infrastructure is known beforehand. Our planner, termed GATSBI, plans a path in a receding horizon fashion to inspect all points on the surface of the infrastructure. The input to GATSBI consists of a 3D occupancy map created online with 3D pointclouds. Occupied voxels corresponding to the infrastructure in this map are semantically segmented and used to create an infrastructure-only occupancy map. Inspecting an infrastructure 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 infrastructure 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 uninspected parts of the infrastructure 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 baseline inspection algorithm where the map is known a priori. Our evaluation reveals that targeting the inspection to only the segmented infrastructure voxels and planning carefully using a GTSP solver leads to a more efficient and thorough inspection than the baseline inspection algorithm.

翻译：本研究探讨了利用无人机（UAV）对基础设施表面进行视觉缺陷检测的问题。我们未预先假设基础设施的几何模型已知。我们提出的规划器GATSBI采用滚动时域规划方式，规划出一条覆盖基础设施表面所有检测点的路径。GATSBI的输入由在线生成的三维点云所构建的占据栅格地图构成。该地图中对应于基础设施的占据体素经过语义分割处理，用于创建仅包含基础设施的占据地图。检测基础设施体素需要无人机从预设的观测角度和距离采集图像。随后，我们构建广义旅行商问题（GTSP）实例，对检测基础设施体素的候选视点进行聚类，并采用现成的GTSP求解器计算该实例的最优路径。随着算法对环境感知范围的逐步扩展，它会重新规划路径以覆盖未检测的基础设施区域，同时规避障碍物。我们通过在AirSim中进行的高保真仿真和真实世界实验评估算法性能，并将GATSBI与已知先验地图的基线检测算法进行对比。评估结果表明：通过针对分割后的基础设施体素进行定向检测，并利用GTSP求解器进行精细路径规划，相较于基线算法能实现更高效、更全面的检测效果。