GATSBI: An Online GTSP-Based Algorithm for Targeted Surface Bridge Inspection and Defect Detection

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.