  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.

Robotics