Scene data fusion: Real-time standoff volumetric gamma-ray imaging

An approach to gamma ray imaging has been developed that enables near real time volumetric (3D) imaging of unknown environments thus improving the utility of gamma ray imaging for source search and radiation mapping applications. The approach, herein dubbed scene data fusion (SEW), is based on integrating mobile radiation imagers with real-time tracking and scene reconstruction algorithms to enable a mobile mode of operation and 3D localization of gamma-ray sources. A 3D model of the scene, provided in real-time by a simultaneous localization and mapping (SLAM) algorithm, is incorporated into the image reconstruction reducing the reconstruction time and improving imaging performance. The SDF concept is demonstrated in this work with a Microsoft Kinect RGB-D sensor, a real-time SLAM solver, and a cart-based Compton imaging platform comprised of two 3D position-sensitive high purity germanium (HPGe) detectors. An iterative algorithm based on Compton kinematics is used to reconstruct the gamma-ray source distribution in all three spatial dimensions. SDF advances the real-world applicability of gamma-ray imaging for many search, mapping, and verification scenarios by improving the tractiblity of the gamma-ray image reconstruction and providing context for the 3D localization of gamma-ray sources within the environment in real-Lime. Published by Elsevier B.V.