Speaker
Description
Understanding the distribution, isotopic composition, and activity of a radiological threat object or contamination area is crucial for responding to both short- and long-term nuclear threats. To address these needs, radiological Scene Data Fusion (SDF) has been developed at Lawrence Berkeley National Laboratory (LBNL) and the University of California, Berkeley (UCB) over the past decade to provide a mature technology for real-time, free-moving, 3D imaging and mapping of gamma and neutron radiation sources. SDF combines radiation measurements with contextual data such as 3D LiDAR maps or camera images in order to attribute radiation concentrations—not just dose rates—to locations, objects, or individuals within the scene. SDF has been deployed on platforms ranging from handheld systems to autonomous unmanned aerial vehicles (UAVs), in scenarios ranging from controlled field demonstrations to contamination mapping at DOE legacy sites, Chernobyl, and Fukushima. In this talk, we will present more recent SDF developments including autonomous source search capabilities (leveraging modern robotics), improved computer vision inputs, and new detector network analysis methods. We also contrast SDF with more traditional dose-rate mapping analyses, and conclude by discussing potential future SDF technology developments and applications.
