| Resumo: | In the context of Industry 4.0, warehouses are transforming and new solutions are allowing more warehouse tasks to be executed in autonomous ways. The use of robots is a great advantage and a great addition to the autonomy of such structures since they can help with the automatic cyclic counting of the inventory, the localization of hard-to-find items, and the access to narrow or high storage areas. In this context, the usage of an Unmanned Aerial Vehicle (UAV) becomes a requirement in this type of operations. Nevertheless, using this type of robots inside a warehouse comes with great engineering challenges such as indoor autonomous localization and navigation, collision avoidance, and automated UAV fleet management. Therefore, this thesis addresses the development of a localization framework based in Graph-SLAM that is capable of calculating the real-time position and orientation of a commercial UAV in warehouse scenarios.
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