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Autonomously searching for hazardous radiation sources requires the abilityof the aerial and ground systems to understand the scene they are scouting. Inthis paper, we present systems, algorithms, and experiments to performradiation search using unmanned aerial vehicles (UAV) and unmanned groundvehicles (UGV) by employing semantic scene segmentation. The aerial data isused to identify radiological points of interest, generate an orthophoto alongwith a digital elevation model (DEM) of the scene, and perform semanticsegmentation to assign a category (e.g. road, grass) to each pixel in theorthophoto. We perform semantic segmentation by training a model on a datasetof images we collected and annotated, using the model to perform inference onimages of the test area unseen to the model, and then refining the results withthe DEM to better reason about category predictions at each pixel. We then useall of these outputs to plan a path for a UGV carrying a LiDAR to map theenvironment and avoid obstacles not present during the flight, and a radiationdetector to collect more precise radiation measurements from the ground.Results of the analysis for each scenario tested favorably. We also note thatour approach is general and has the potential to work for a variety ofdifferent sensing tasks.

Radiation Search Operations using Scene Understanding with Autonomous UAV and UGV