Andrea Eirale
Indoor SLAM and Room Classification with Deep Learning at the edge.
Rel. Marcello Chiaberge. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2020
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Abstract
In recent years, the development of technology has led to the emergence of increasingly complex and accurate localization and mapping algorithms. In the field of robotics, this has allowed the progressive integration alongside other programs with the most varied functions, from home care to space exploration, designed to provide a service, to support and improve people's living conditions. With this goal in mind, the PIC4SeR (PoliTo interdepartmental centre for service robotics) has developed the idea of integrating a SLAM algorithm, for simultaneous self localization and mapping, with a convolutional neural network for room recognition. ???? The main goal of this thesis project is the development of an algorithm able to lead an unmanned ground vehicle in an unknown, closed domestic environment, mapping it and classifying each room encountered in the process.
Low cost sensors and free, open-source software are used to achieve the final result
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