In autonomous driving systems and robotics, one of the main characteristics is the capability of the system to localize itself in the environment, to create a map and to optimize the navigation, which is the ability of getting the vehicle from place to place. The Simultaneous Localization and Mapping (SLAM) is the branch of the autonomous system responsible to solve this problem. Fusing the data coming from sensors, it can provide a local and a global map, beside to correctly localize and orient the vehicle in it. The aim of this thesis work is the deployment of a robust global mapping algorithm able to provide a precise and accurate map, of an unknown environment with cones, to the control system and allow a safe and optimal navigation.