  Autonomously searching for hazardous radiation sources requires the ability
of the aerial and ground systems to understand the scene they are scouting. In
this paper, we present systems, algorithms, and experiments to perform
radiation search using unmanned aerial vehicles (UAV) and unmanned ground
vehicles (UGV) by employing semantic scene segmentation. The aerial data is
used to identify radiological points of interest, generate an orthophoto along
with a digital elevation model (DEM) of the scene, and perform semantic
segmentation to assign a category (e.g. road, grass) to each pixel in the
orthophoto. We perform semantic segmentation by training a model on a dataset
of images we collected and annotated, using the model to perform inference on
images of the test area unseen to the model, and then refining the results with
the DEM to better reason about category predictions at each pixel. We then use
all of these outputs to plan a path for a UGV carrying a LiDAR to map the
environment and avoid obstacles not present during the flight, and a radiation
detector to collect more precise radiation measurements from the ground.
Results of the analysis for each scenario tested favorably. We also note that
our approach is general and has the potential to work for a variety of
different sensing tasks.
