论文标题
一种具有成本效益的人追随者系统，用于辅助无人车，并在边缘深入学习
A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge
论文作者
论文摘要
上个世纪的重要统计数据突出了世界平均年龄的急剧增长，随之而来的是老年人数量的增长。服务机器人的应用程序具有提供系统和工具的潜力，以支持日常生活中房屋中自主和自给自足的老年人，从而避免与第三方监视他们的任务。在这种情况下，我们提出了一种具有成本效益的模块化解决方案，以检测并关注室内家庭环境中的一个人。我们利用了深度学习优化技术的最新进步，并比较了不同的神经网络加速器，以在边缘提供强大而灵活的人跟随系统。我们提出的具有成本效益和功率效率的解决方案是可以完全综合的，可与先前存在的导航堆栈完全融合，并为开发完全自主和独立的服务机器人技术应用创造基础。
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.
