  Wireless tags are increasingly used to track and identify common items of
interest such as retail goods, food, medicine, clothing, books, documents,
keys, equipment, and more. At the same time, there is a need for labelled
visual data featuring such items for the purpose of training object detection
and recognition models for robots operating in homes, warehouses, stores,
libraries, pharmacies, and so on. In this paper, we ask: can we leverage the
tracking and identification capabilities of such tags as a basis for a
large-scale automatic image annotation system for robotic perception tasks? We
present RF-Annotate, a pipeline for autonomous pixel-wise image annotation
which enables robots to collect labelled visual data of objects of interest as
they encounter them within their environment. Our pipeline uses unmodified
commodity RFID readers and RGB-D cameras, and exploits arbitrary small-scale
motions afforded by mobile robotic platforms to spatially map RFIDs to
corresponding objects in the scene. Our only assumption is that the objects of
interest within the environment are pre-tagged with inexpensive battery-free
RFIDs costing 3-15 cents each. We demonstrate the efficacy of our pipeline on
several RGB-D sequences of tabletop scenes featuring common objects in a
variety of indoor environments.
