Giovanni Santacroce
Obstacles Detection and Global Mapping Algorithm for an Autonomous Racecar.
Rel. Nicola Amati, Stefano Feraco. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2021
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Abstract
Nowadays, mapping is one of the most active research fields in robotics. In this thesis it is faced the global mapping issue related to the SLAM (Simultaneous Localization and Mapping) process of an autonomous vehicle which will participate to the FSD (Formula Student Driverless) competition. The mapping process together with the localization one allow to perform car navigation, which is the ability of getting the vehicle from place to place. Thus, according to Leonard and Durrant-Whyte H. “Dynamic map building for an autonomous mobile robot” paper, SLAM allows to answer to three fundamental questions for a mobile robot: “Where am I?”, “Where am I going?” and “How do I get there?”.
To do this, it was decided to adapt to our case the algorithm contained in the PerceptionAndSlam_KTHFSD1718 package of the KTH Royal Institute of Technology FSD team published on GitHub website
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