This paper introduces a compound vision system that enables robots to localize people up to 15m away using a cheap camera. And, it proposes a robust navigation stack that combines Deep Reinforcement Learning (DRL) and a probabilistic localization method. To test the efficacy of these systems, we prototyped a low-cost mobile robot that we call Covy. Covy can be used for applications such as promoting social distancing during pandemics or estimating the density of a crowd. We evaluated Covy's performance through extensive sets of experiments both in simulated and realistic environments. Our results show that Covy's compound vision algorithm doubles the range of the used depth camera, and its hybrid navigation stack is more robust than a pure DRL-based one.

翻译：本文介绍一个复合视觉系统,使机器人能够使用廉价的相机在距离15米以内的人中定位。 并且, 它提出了一个强大的导航堆, 将深强化学习( DRL) 和一种概率定位法结合起来。 为了测试这些系统的功效, 我们制作了一个低成本移动机器人的原型, 我们称之为Covy。 Covy 可用于应用, 如在大流行病期间促进社会疏远或估计人群密度。 我们通过在模拟和现实环境中进行一系列广泛的实验, 评估了Covy的性能。 我们的结果表明, Covy 的复合视觉算法是使用过的深度摄像器的两倍, 其混合导航堆比纯粹的 DRL 的更坚固。