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

TL;DR

This paper presents a low-cost, power-efficient person-following system for assistive robots in indoor environments, leveraging deep learning at the edge to support autonomous care for older adults.

Contribution

It introduces a modular, cost-effective solution utilizing optimized deep learning models and neural accelerators for robust person detection and following at the edge.

Findings

Achieved high accuracy in person detection within indoor settings

Compared and selected optimal neural network accelerators for edge deployment

Demonstrated system integration with existing navigation stacks

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.