Radiation Source Localization Considering Shielding Effect of Structures Using 3D Object Recognition.
2024 IEEE/SICE International Symposium on System Integration (SII)(2024)
Abstract
In Fukushima Daiichi, post-accident conditions have led to the accumulation of radioactive fuel debris. Cleaning up this debris necessitates human intervention, but workers face a severe risk of radiation exposure due to the work's location within the reactor building and potential contaminant exposure. To establish a safe working environment, efforts focus on identifying and removing these radiation sources. However, challenges arise from shielding effects caused by structures like concrete walls. This study proposes a novel approach to identify the positions of these radiation sources. Specifically, it employs 3D object recognition to identify objects that could potentially shield radiation. By correcting the measured radiation dose that has been reduced due to the shielding effect of identified structures, the study utilizes a sequential image reconstruction method known as the Maximum Likelihood Expectation Maximization (ML-EM) technique to estimate the positions of radiation sources. It's worth noting that the ML-EM technique focuses on detailed estimation within localized areas. To estimate these localized areas with a high probability of radiation source presence, an Extended Kalman Filter is utilized.
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Key words
Effect Of Structure,Radiation Source,Shielding Effect,3D Object Recognition,Local Area,Probability Of Presence,Source Position,Detailed Estimates,Concrete Walls,Risk Of Radiation Exposure,Computation Time,Measurement Data,Wall Thickness,Point Cloud,Nuclear Reactors,Correct Value,Estimation Process,Nuclear Power Plant,Position Estimation,Attenuation Coefficient,Simultaneous Localization And Mapping,Extended Kalman Filter,Robot Operating System,Point Cloud Data,Forward Projection,Projection Angle,Back Projection,Image Coordinates,Target Environment,Linear Attenuation Coefficient
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