Indoor SLAM and Room Classification with Deep Learning at the edge

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. ???? For localization and mapping, several techniques are considered, from the classic extended Kalman filter to the more advanced graph-based SLAM. The most adapted ones are further developed, to retrieve a first, raw representation of the environment. The map is then processed with computer vision software in order to obtain a cleaner and clearer plot of the surroundings, and to setting it up for the recognition algorithm. ???? Finally, a convolutional neural network model is used, alongside to a series of frame images taken by the robot from the environment, to classify each room and provide predictions on the map. ???? The final algorithm is relatively efficient and lightweight, and opens up to a series of future implementations in the field of service robotics, in domestic environments and in the assistance to elderly and disabled users.