Simultaneous Localization and Mapping algorithms benchmark for Autonomous Driving

In the context of Autonomous Driving (AD), Simultaneous Localization and Mapping (SLAM) algorithms are of fundamental importance in enabling vehicles to navigate and interact with the surrounding environment, with no input from a human driver. These algorithms are used to construct a map of the surroundings while estimating, at the same time, the position and trajectory of the vehicle inside of it. This thesis, developed as a collaboration between Politecnico di Torino and Italdesign Giugiaro, aims to adapt some of the best open-source SLAM algorithm to an internal AD project of the company, the autonomous vehicle prototype called Techdemo. The focus of this work is to integrate the algorithms in the software of the Techdemo to generate a map of the surroundings and conduct benchmark testing finalised at selecting the best one for the application.