3D Radiation Mapping Using Gaussian Process Regression with Intensity Projection

Abstract

This article presents a novel approach for generating a three-dimensional radiation map using data collected by mobile robots, aimed at monitoring radiation distribution in environments such as nuclear power plants. The proposed approach leverages Gaussian process regression with a novel adaptation of the inverse square law as a kernel function, which accurately reflects the physical characteristics of radiation, enabling precise mapping from uncertain data. Additionally, a method is proposed for constructing a comprehensive radiation map in 3D environments by estimating the radiation source and project the radiation data from sparse data. The effectiveness of the methodology is validated through simulations and experiments. Utilizing Octomap, a 3D spatial mapping tool, the study not only successfully visualizes radiation distribution in complex settings with multiple sources but also quantitatively demonstrates the enhanced accuracy of our approach compared to other existing methods. This research offers contributions in managing radiation risks, providing essential insights into the field.