Robotic systems for retail have gained a lot of attention due to the labor-intensive nature of such business environments. Many tasks have the potential to be automated via intelligent robotic systems that have manipulation capabilities. For example, empty shelves can be replenished, stray products can be picked up or new items can be delivered. However, many challenges make the realization of this vision a challenge. In particular, robots are still too expensive and do not work out of the box. In this paper, we discuss a work-in-progress approach for enabling power-on-and-go robots in retail environments through a combination of active, physical sensors and passive, artificial sensors. In particular, we use low-cost hardware sensors in conjunction with machine learning techniques in order to generate high-quality environmental information. More specifically, we present a setup in which a standard monocular camera and Bluetooth low-energy yield a reliable robot system that can immediately be used after placing a couple of sensors in the environment. The camera information is used to synthesize accurate 3D point clouds, whereas the BLE data is used to integrate the data into a complex map of the environment. The combination of active and passive sensing enables high-quality sensing capabilities at a fraction of the costs traditionally associated with such tasks.

翻译：由于这类商业环境的劳动密集型性质,零售的机器人系统引起了人们的极大关注。许多任务都有可能通过具有操纵能力的智能机器人系统实现自动化。例如,空架子可以补充,可采集流体产品,或提供新的产品。然而,许多挑战使得实现这一愿景成为挑战。特别是,机器人仍然太昂贵,无法从盒子中发挥作用。在本文件中,我们讨论的是通过将主动式、物理传感器和被动式、人造传感器相结合,使零售环境中的电对流机器人能够实现自动化。特别是,我们使用低成本硬件传感器与机器学习技术相结合,以生成高质量的环境信息。更具体地说,我们提出了一个设置,使标准单色相机和蓝牙低能产生一个可靠的机器人系统,在环境安装了几套传感器后,即可使用该系统。相机信息用于合成准确的3D点云,而通用气象数据被用于将数据整合到复杂的环境地图中。将主动式和被动式遥感技术结合到一个与传统比例相联的图像能力,从而使得高品质的遥感能力得以在环境中实现。