Person tracking methodologies and algorithms in service robotic applications

The vital statistics of the last century highlight a sharply increasement of the average life of the world population with a consequent growth of the number of elderly people. This scenario has caused new social needs that the research in the service robotics field is trying to fulfill. Particularly, the idea of this thesis is born at the PIC4SeR (PoliTo interdepartmental centre for service robotics) with the purpose of creating complex service robotics applications to support the autonomous and self-sufficient old people into their house in everyday life, avoiding the task of monitoring them by third parties. This work represents the first steps of a broad project in which many other service tasks will be integrated. The main argument of this thesis is to develop algorithms and methodologies to detect, track and follow a person in an indoor environment using a small wheeled rover and low cost and available sensors to monitor the target person. Several techniques are explored showing the evolution of these methods along the years: from the classical Machine Learning algorithms to the Deep Neural Network ones. Since the main requirement to be respected is the necessity of real-time results, only few of the analysed algorithms are developed for this project scope and at the end are compared in order to find the best solution with optimal outcomes. The detection and localization are the base of the person tracking application, done by the robot on which it has been implemented a movement control algorithm and at last it has been introduced an obstacle avoidance algorithm to prevent collisions.