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The vital statistics of the last century highlight a sharp increment of theaverage age of the world population with a consequent growth of the number ofolder people. Service robotics applications have the potentiality to providesystems and tools to support the autonomous and self-sufficient older adults intheir houses in everyday life, thereby avoiding the task of monitoring themwith third parties. In this context, we propose a cost-effective modularsolution to detect and follow a person in an indoor, domestic environment. Weexploited the latest advancements in deep learning optimization techniques, andwe compared different neural network accelerators to provide a robust andflexible person-following system at the edge. Our proposed cost-effective andpower-efficient solution is fully-integrable with pre-existing navigationstacks and creates the foundations for the development of fully-autonomous andself-contained service robotics applications.

A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge