3-Dimensional Reconstruction on Tagged Packages via RFID Systems

Nowadays, 3D reconstruction has been introduced in monitoring the package placement in logistic industry-related applications. Existing 3D econstruction methods are mainly based on computer vision or sensor-based approaches, which are limited by the line-of-sight or battery life constraint. In this paper, we propose RF-3DScan to perform 3D reconstruction on tagged packages via passive RFID, by attaching multiple reference tags onto the surface of the packages. The basic idea is that by moving the antenna along straight lines within a constrained 2-dimensional space, the antenna obtains the RF-signals of the reference tags attached on the packages. By extracting the phase differences to build the angle profile for each tag, RF-3DScan can compare the angle profiles of the different reference tags and derive their relative positions, then further determine the package orientation and stacking for 3D reconstruction. We implement RF- 3DScan and evaluate its performance in real settings. The experiment results show that the average identification accuracy of the bottom face is about 92.5%, and the average estimation error of the rotation angle is about 4.08º.

[1]  Michael Bosse,et al.  Watertight surface reconstruction of caves from 3D laser data , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Michael Weber,et al.  Find my stuff: supporting physical objects search with relative positioning , 2013, UbiComp.

[3]  Shigeng Zhang,et al.  Flexible and Time-Efficient Tag Scanning with Handheld Readers , 2016, IEEE Transactions on Mobile Computing.

[4]  Yunhao Liu,et al.  STPP: Spatial-Temporal Phase Profiling-Based Method for Relative RFID Tag Localization , 2017, IEEE/ACM Transactions on Networking.

[5]  Lei Yang,et al.  Season: Shelving interference and joint identification in large-scale RFID systems , 2011, 2011 Proceedings IEEE INFOCOM.

[6]  Jie Wu,et al.  Tell me what i see: recognize RFID tagged objects in augmented reality systems , 2016, UbiComp.

[7]  Ross A. Knepper,et al.  RF-compass: robot object manipulation using RFIDs , 2013, MobiCom.

[8]  Xinyu Zhang,et al.  Gyro in the air: tracking 3D orientation of batteryless internet-of-things , 2016, MobiCom.

[9]  Andrew W. Fitzgibbon,et al.  KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera , 2011, UIST.

[10]  Simon J. Julier,et al.  Structured Prediction of Unobserved Voxels from a Single Depth Image , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Lei Yang,et al.  See Through Walls with COTS RFID System! , 2015, MobiCom.

[12]  Jue Wang,et al.  Dude, where's my card?: RFID positioning that works with multipath and non-line of sight , 2013, SIGCOMM.

[13]  Lina Yao,et al.  TagTrack: device-free localization and tracking using passive RFID tags , 2014, MobiQuitous.

[14]  Jue Wang,et al.  RF-IDraw: virtual touch screen in the air using RF signals , 2015, SIGCOMM 2015.

[15]  Marc Pollefeys,et al.  Live Metric 3D Reconstruction on Mobile Phones , 2013, 2013 IEEE International Conference on Computer Vision.

[16]  Lei Yang,et al.  Beyond one-dollar mouse: A battery-free device for 3D human-computer interaction via RFID tags , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[17]  Lijun Chen,et al.  Fast RFID grouping protocols , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[18]  Shigeng Zhang,et al.  Unknown Tag Identification in Large RFID Systems: An Efficient and Complete Solution , 2015, IEEE Transactions on Parallel and Distributed Systems.

[19]  Lei Yang,et al.  Tagoram: real-time tracking of mobile RFID tags to high precision using COTS devices , 2014, MobiCom.