SLAM Algorithm for 2D Object Trajectory Tracking based on RFID Passive Tags

Tracking the physical location of nodes in a 2D environment is critical in many applications such as camera tracking in virtual studio, indoor mobile objects tracking. RFID technique poses an interesting solution to localizing the nodes because the passive RFID tags could store the position unit information according to unique tag ID. Based on tags pattern, algebraic approach could solve the 2D trajectory tracking problem. However, the tracking accuracy of this approach is highly related to the tags position distribution and position unit. It would be inaccurate for some erratic trajectory tracking. Thus, we would try to apply and evaluate the probabilistic approaches, such as SLAM (Simultaneous Localization and Mapping), into RFID tag based trajectory tracking. In this paper, we propose an RFID tag based SLAM algorithm for 2D trajectory tracking. Also a technique called Map adjustment is proposed to increase the efficiency of the algorithm. The simulation results show that the approach could improve the accuracy for some parts of trajectory tracking compared to RFID algebraic approach. The limitation and future work are given in the conclusion.

[1]  Andrew Calway,et al.  Real-Time Camera Tracking Using a Particle Filter , 2005, BMVC.

[2]  Wolfram Burgard,et al.  The Mobile Robot Rhino , 1995, SNN Symposium on Neural Networks.

[3]  Andy Hopper,et al.  A new location technique for the active office , 1997, IEEE Wirel. Commun..

[4]  Sebastian Thrun,et al.  FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges , 2003, IJCAI.

[5]  Yunhao Liu,et al.  LANDMARC: Indoor Location Sensing Using Active RFID , 2004, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[6]  Wolfram Burgard,et al.  Robust Monte Carlo localization for mobile robots , 2001, Artif. Intell..

[7]  Yunhao Liu,et al.  ANDMARC: Indoor Location Sensing Using Active RFID , 2003, PerCom.

[8]  M. O. Berger,et al.  Application of radio frequency identification devices to support navigation of autonomous mobile robots , 1997, 1997 IEEE 47th Vehicular Technology Conference. Technology in Motion.

[9]  Gaetano Borriello,et al.  Design and Calibration of the SpotON Ad-Hoc Location Sensing System , 2001 .

[10]  David W. Murray,et al.  Simultaneous Localization and Map-Building Using Active Vision , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Sebastian Thrun,et al.  FastSLAM: An Efficient Solution to the Simultaneous Localization And Mapping Problem with Unknown Data , 2004 .

[12]  Rodney A. Brooks,et al.  A robot that walks; emergent behaviors from a carefully evolved network , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[13]  Yoshihiko Kimuro,et al.  Self-localization of mobile robots with RFID system by using support vector machine , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[14]  Andrew J. Davison,et al.  Real-time simultaneous localisation and mapping with a single camera , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[15]  H. Aghajan,et al.  Utilizing RFID Signaling Scheme for Localization of Stationary Objects and Speed Estimation of Mobile Objects , 2007, 2007 IEEE International Conference on RFID.

[16]  JangMyung Lee,et al.  An Efficient Localization Algorithm for Mobile Robots based on RFID System , 2006, 2006 SICE-ICASE International Joint Conference.

[17]  M. Degroot,et al.  Probability and Statistics , 2021, Examining an Operational Approach to Teaching Probability.