  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.
