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arXiv · 2008.13474

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

Anna Boschi·Francesco Salvetti·Vittorio Mazzia·Marcello Chiaberge
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Abstract

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

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Anna Boschi, Francesco Salvetti, Vittorio Mazzia, Marcello Chiaberge. 2020-08-31. A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge. https://doi.org/10.3390/machines8030049

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