A Quantitative Study of Tuning ROS Adaptive Monte Carlo Localization Parameters and their Effect on an AGV Localization One key aspect of the use of Automated Guided Vehicles in an industrial environment is its localization effectiveness. Among the existing techniques, the use of a laser scanner stands out. Besides, the Adaptive Monte Carlo Localization algorithm has become a reference in academic research. Despite many works use the AMCL package, they do not fully discuss the effect of the parameters change on the algorithm response and its tuning. This work aims to examine the distinct influence of each tested parameter in AGV localization. We performed the experiments in the same environment, and the AGV ran the same path to enable comparison against the parameters variation. For the 7 parameters tested, the results show the relationship between the package parameters and the localization response behavior. Although the article does not aim to propose the best parameter tuning, the results show the direction to follow in values adjusting.

Objectbased differential Localization of Mobile Robots using sparse 2D Lidar Data The highly accurate pose estimation of mobile robots with respect to a known target object is a key technology for autonomous industrial manufacturing processes. Current approaches generally assume that the environment is static and locate the robot in relation to predefined positions. This paper presents an implementation and validation of how to localize a mobile robot in relation to the coordinate system of the target object  a prerequisite for any kind of manipulation or interaction. This allows the robot to be localized to arbitrarily positioned objects in the environment. For experimental validation, a highprecision external tracking system is used as ground truth. In this way, objects of different shapes are evaluated from different viewpoints. We achieve a pose estimation accuracy of less than 1 cm in a real world scenario.