A Comparison Particle Filter for Searching a Radiation Source in Real and Simulated World

Abstract

In this paper, we are focusing on comparing solutions for localizing an unknown radiation source in both a Gazebo simulator and the real world. A proper simulation of the environment, sensors, and radiation source can significantly reduce the development time of robotic algorithms. We proposed a simple sampling importance resampling (SIR) particle filter. To verify its effectiveness and similarities, we first tested the algorithm's performance in the real world and then in the Gazebo simulator. In experiment, we used a 2-inch NaI(Tl) radiation detector and radiation source Cesium 137 with an activity of 330 Mbq. We compared the algorithm process using the evolution of information entropy, variance, and Kullback-Leibler divergence. The proposed metrics demonstrated the similarity between the simulator and the real world, providing valuable insights to improve and facilitate further development of radiation search and mapping algorithms.