Annotating RFID-attached objects in images

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 thesis, 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 pixelwise 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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Sie, Emerson

Contributors dc:contributor

• Vasisht, Deepak

• Rfid
• Object Detection

Statement dc:rights

• Copyright 2024 Emerson Sie

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/124570

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F124570