  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.
