An extended analysis on tuning the parameters of Adaptive Monte Carlo Localization ROS package in an automated guided vehicle

With a growth tendency, the employment of the Adaptive Monte Carlo Localization (AMCL) Robot Operational System (ROS) package does not reflect a more in-depth discussion on its parameters’ tuning process. The authors usually do not describe it. This work aims to extend the analysis of the package’s parameters’ distinct influence in an automated guided vehicle (AGV) indoor localization. The experiments test parameters of the filter, the laser model, and the odometry model. Extending the previous analysis of seven parameters, the present research discusses another ten from the 22 configurable parameters of the package. An external visual vehicle pose tracking is used to compare the pose estimation from the localization package. Although the article does not propose the best parameter tuning, its results discuss how each tested parameter affects the localization. The paper’s contribution is discussing the parameters’ variation impact on the AGV localization using the covariance matrix results. It may help new researchers in the AMCL ROS package parameter tuning process. The results show minor changes in the default parameters which can improve the localization results, even modifying one parameter at a time.