TagRecon: Fine-Grained 3D Reconstruction of Multiple Tagged Packages via RFID Systems

Author:

Wang Ziang1ORCID,Duan Chunhui1ORCID,Xue Jiawei1ORCID,Li Fan1ORCID,Feng Qihua1ORCID,Zhu Yinan2ORCID,Zhou Ziyang1ORCID

Affiliation:

1. Beijing Institute of Technology, Beijing, China

2. The Hong Kong University of Science and Technology, Hong Kong, Hong Kong

Abstract

To meet the new requirements of Industry 4.0, the logistics field has introduced 3D reconstruction technology. Computer vision-based solutions face challenges like bad lighting conditions and line-of-sight constraints. Meanwhile, the widespread adoption of RFID tags in supply chains offers an opportunity to enhance current reconstruction methods. In this article, we propose TagRecon, a fine-grained multi-object 3D reconstruction scheme utilizing well-deployed RFIDs. Specifically, TagRecon transforms the task of reconstruction into a problem of estimating 3D bounding boxes for tagged packages. By placing dual anchor tags on each target package, TagRecon enables accurate inference of the package’s translation and rotation using RFID-based localization and orientation sensing. Our scheme introduces a novel method to estimate rotations and translations for tagged packages, utilizing the known geometric relationship of anchor tags. Besides, to achieve simultaneous reconstruction of multiple packages, we manage to match tags from various packages through the correlation between anchor tag pairs. As far as we know, this is the first RFID-based solution that can simultaneously realize 3D translation and rotation estimation of multiple objects to a fine granularity. Experiments validate TagRecon achieves a 28.0 cm translation error and 6.8°, 6.0°, and 7.5° rotation errors for roll, pitch, and yaw angles on average.

Funder

National Natural Science Foundation of China

Beijing Institute of Technology Research Fund Program for Young Scholars

Publisher

Association for Computing Machinery (ACM)

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