DOI QR코드

DOI QR Code

Object Localization in Sensor Network using the Infrared Light based Sector and Inertial Measurement Unit Information

적외선기반 구역정보와 관성항법장치정보를 이용한 센서 네트워크 환경에서의 물체위치 추정

  • 이민영 (홍익대학교 기계시스템디자인공학과) ;
  • 이수용 (홍익대학교 기계시스템디자인공학과)
  • Received : 2010.09.10
  • Accepted : 2010.12.01
  • Published : 2010.12.01

Abstract

This paper presents the use of the inertial measurement unit information and the infrared sector information for getting the position of an object. Travel distance is usually calculated from the double integration of the accelerometer output with respect to time; however, the accumulated errors due to the drift are inevitable. The orientation change of the accelerometer also causes error because the gravity is added to the measured acceleration. Unless three axis orientations are completely identified, the accelerometer alone does not provide correct acceleration for estimating the travel distance. We propose a way of minimizing the error due to the change of the orientation. In order to reduce the accumulated error, the infrared sector information is fused with the inertial measurement unit information. Infrared sector information has highly deterministic characteristics, different from RFID. By putting several infrared emitters on the ceiling, the floor is divided into many different sectors and each sector is set to have a unique identification. Infrared light based sector information tells the sector the object is in, but the size of the uncertainty is too large if only the sector information is used. This paper presents an algorithm which combines both the inertial measurement unit information and the sector information so that the size of the uncertainty becomes smaller. It also introduces a framework which can be used with other types of the artificial landmarks. The characteristics of the developed infrared light based sector and the proposed algorithm are verified from the experiments.

Keywords

References

  1. C.-C. Hung and W.-C. Peng, “Clustering object moving patterns for prediction-based object tracking sensor networks,” Proc. of ACM Conference on Information and Knowledge Management (CIKM), pp. 1633-1636, 2009.
  2. T. King et al., “COMPASS: A probabilistic indoor positioning system based on 802.11 and digital compasses,” Proc. of the 1st international workshop on Wireless network testbeds, experimental evaluation & characterization (WiNTECH), pp. 34-40, Sep. 2006.
  3. H. Piontek, M. Seyffer, and J. kaiser, “Improving the accuracy of ultrasound-based localisation systems,” Persnal and Ubiquitious Computing, vol. 11, no. 6, pp. 439-449, 2007. https://doi.org/10.1007/s00779-006-0096-1
  4. C. Gui and P. Mohapatra, “Power conservation and quality of surveillance in target tracking sensor networks,” Proc. of the 10th annual international conference on Mobile computing and networking, pp. 129-143, 2004.
  5. L. Lazos and R. Poovendran, “SeRLoc: Robust localization for wireless sensor networks,” ACM Transactions on Sensor Networks, vol. 1, no. 1, pp. 73-100, Aug. 2005. https://doi.org/10.1145/1077391.1077395
  6. S. Lee and J. Song, “Use of coded infrared light as artificial landmarks for mobile robot localization,” Proc. of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1731-1736, Oct. 2007.
  7. 배정연, 이수용, 송재복, “IRID를 이용한 이동로봇의 위치추정,” Journal of Control, Automation, and Systems Engineering, vol. 13, no. 9, pp. 903-909, Sep. 2007. https://doi.org/10.5302/J.ICROS.2007.13.9.903
  8. S. Sukkarieh, E. M. Nebot, and H. Durrant-Whyte, “A high integrity IMU/GPS navigation loop for autonomous land vehicle applications,” IEEE Transactions on Robotics and Automation, vol. 15, no. 3, pp. 572-578, Jun. 1999. https://doi.org/10.1109/70.768189
  9. T. Upadhyay, S. Cotterill, and A. W. Deaton, “Autonomous GPS/INS navigation experiment for space transfer vehicle,” IEEE Transactions on Robotics and Automation, vol. 29, no. 3, pp. 772-285, Jul. 1993. https://doi.org/10.1109/7.220929
  10. W.-W. Kao, “Integration of GPS and dead-reckoning navigation systems,” Proc. Vehicle Navigation and Information Systems Conference, SAE, pp. 635-643, Oct. 1991.
  11. K. Kobayashi, F. Munekata, and K. Watanabe, “Accurate navigation via differential GPS and vehicle local sensors,” IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 9-16, Oct. 1994.
  12. A. Brown and Y. Lu, “Performance test results on an integrated GPS/MEMS inertial navigation,” Proc. of ION GNSS 2004, pp. 825-832, 2004.
  13. D. A. Grejner-Brzezinska, C. Toth, S. Moafipoor, and Y. Jwa, “Multi-sensor personal navigator supported by human motion dynamics model,” 3rd IAG / 12th FIG Symposium, May 2006.
  14. L. Ojeda and J. Borenstein, “Non-GPS navigation for security personnel and first responders,” Journal of Navigation, vol. 60, no. 3, pp. 391-407, Sep. 2007. https://doi.org/10.1017/S0373463307004286
  15. S. Beauregard, “A helmet-mounted pedestrian dead reckoning system,” 3rd International Forum on Applied Wearable Computing (IFAWC 2006), VDE Verlag, pp. 79-89, Mar. 2006.