An Improved Adaptive Monte Carlo Localization (AMCL) for Automated Mobile Robot (AMR)

The adaptive Monte Carlo localization (AMCL) algorithm is commonly used for localization tasks for automated mobile robots (AMRs). However, when AMRs move to a feature-less environment, AMCL shows poor performance in localization. We propose an improved AMCL algorithm to improve the accuracy and robustness of the localization for AMR. We first matched the laser scanning points with the pre-built grid map according to the localization result generated from AMCL. Then, we designed a localization credibility estimation (LCE) to evaluate the localization performance, and the match with the higher LCE score was selected and injected into the particle swarm with an adaptive amount to optimize the next estimation process of AMCL. The particle swarm of AMCL gradually converged to the correct location through iterative optimization. Results demonstrate that the proposed AMCL algorithm is superior to the traditional AMCL algorithm and previous improved AMCL in terms of accuracy and robustness.