A Novel Probabilistic Approach to Identifying Similar Energy Regions in the SA-MCL: SA∗-MCL

This paper introduces a novel probabilistic approach to more orderly and optimally identify Similar Energy Regions (SERs) within the Self Adaptive Monte Carlo Localization (SA-MCL) algorithm. Unlike other MCL algorithms in which particles are distributed across the entire map at the initial time, SERs enable the assignment of the particles to pre-discretized grid cells on the map that possess energies closest to the robot's energy which is calculated based on measurements obtained from its surroundings. This method enhances the likelihood of the particles being assigned more intelligently, thereby improving the probability of accurately estimating the robot's location. With the proposed approach, energy calculation based on distance measurements from the real environment and the map has been reformulated by weighting according to the probability density function defined by the narrowness or width of the region where these measurements are obtained. Additionally, a new calculation method for the threshold values used in determining the SERs is introduced. The SA-MCL algorithm incorporating this new method is designated as the "SA∗-MCL"algorithm. To validate that the proposed method outperforms the previous approach, the real-world experiments are conducted in a pre-prepared platform, and the findings are presented in a comparative analysis.