Skip to main content

Advertisement

Log in

ASC localization in noisy environment based on wireless sensor network

  • Original Research Paper
  • Published:
Intelligent Service Robotics Aims and scope Submit manuscript

Abstract

This study investigates indoor localization problem of robot or a customer at shopping mall environment. To improve the localization accuracy, a sensor fusion-based approach is employed, which combines data from ZigBee, odometry of active shopping cart (ASC), and QR marker. The proposed algorithm employs Gaussian probability estimation method and thus it is adaptive to localization problem even at noisy environment such as the shopping mall. To implement the localization service, an ASC which is equipped with motors for navigation, a laser sensor for tracking, and a tablet computer for human–computer interaction is designed. Through experimental work, we corroborate the feasibility of the proposed localization algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Want R, Hopper A, Falcao V, Gibbons J (1992) The active badge location system. ACM Trans Inf Syst 10:91–102

    Article  Google Scholar 

  2. Krumm J, Harris S, Harris B et al (2000) Multi-camera multi-person tracking for easy living. In: Proceedings of the 3rd IEEE workshop on visual surveillance, pp 3–10

  3. Bahl P, Padmanabhan VN (2000) RADAR: an in-building RF-based user location and tracking system. In: Proceedings of IEEE conference on Computer and Communications Societies, pp 775–784

  4. Raab FH, Blood EB, Steiner TO et al (1979) Magnetic position and orientation tracking system. IEEE Trans Aerosp Electron Syst 15(5):709–717

    Article  Google Scholar 

  5. Zhu M, Zhang K, Cartwright W et al (2009) Possibility studies of integrated INS/RFID positioning methods for personal positioning applications. In: Proceedings of IGNSS, pp 1–9

  6. Ni LM, Liu Y, Lau YC et al (2004) LANDMARC: indoor location sensing using active RFID. Wirel Netw 10(6):701–710

    Article  Google Scholar 

  7. Irfan N, Bolic M, Yagoub MCE et al (2010) Neural-based approach for localization of sensors in indoor environment. Telecommun Syst 44(1–2):149–158

    Article  Google Scholar 

  8. Helen M, Latvala J, Ikonen H et al (2001) Using calibration in RSSI-based location tracking system. In: Proceedings of the 5th world multiconference on circuits, systems, communications and computer

  9. Kim HH, Ha KN, Lee S et al (2009) Resident location recognition algorithm using a Bayesian classifier in the PIR sensor-based indoor location-aware system. Syst Man Cybern C Appl Rev IEEE Trans 39(2):240–245

    Article  MathSciNet  Google Scholar 

  10. Luo RC, Chen O (2013) Wireless and pyroelectric sensory fusion system for indoor human/robot localization and monitoring. Mechatron IEEE/ASME Trans 18(3):845–853

    Article  Google Scholar 

  11. Yu K, Montillet J, Rabbachin A et al (2006) UWB location and tracking for wireless embedded networks. Signal Process 86(9):2153–2171

    Article  MATH  Google Scholar 

  12. Zebra Technology (2008) http://www.wherenet.com/

  13. Ahn HS, Lee J, Yu W et al (2007) Indoor localization technique for intelligent robotic space. ITRI’s New Technol 22:48–57

    Google Scholar 

  14. Ahn HS, Yu W (2009) Environmental adaptive RSSI based indoor localization. Autom Sci Eng IEEE Trans 6(4):626–633

    Article  Google Scholar 

  15. Paul AS, Wan EA (2009) RSSI-based indoor localization and tracking using sigma-point Kalman smoothers. IEEE J Signal Process 3(5):860–873

    Google Scholar 

  16. Alhmiedat T, Samara G, Salem AOA (2013) An indoor fingerprinting localization approach for ZigBee wireless sensor networks. Eur J Sci Res 105(2):190–202

    Google Scholar 

  17. Kim D, Lee D, Myung H, Choi HT (2014) Artificial landmark-based underwater localization for AUVs using weighted template matching. Intell Serv Robot 7(3):175–184

    Article  Google Scholar 

  18. Alenyá G, Foix S, Torras C (2014) Using ToF and RGBD cameras for 3D robot perception and manipulation in human environments. Intell Serv Robot 7(4):211–220

    Article  Google Scholar 

  19. Glas DF, Miyashita T, Ishiguro H et al (2007) Laser tracking of human body motion using adaptive shape modeling. In: Proceedings of IEEE/RSJ international conference on robots and systems, pp 602–608

  20. Jung E-J, Lee JH, Yi B-J et al (2014) Development of a laser-range-based human tracking and control algorithm for a marathoner service robot. Mechatron IEEE/ASME Trans 19(6):1083–4435

    Google Scholar 

  21. Millington MJ (1985) Trolley for use with a wheelchair. U.S. Patent 4, 555, 124

  22. Montalvo SA (2007) Guide wheel assembly for carts. U.S. Patent 7, 198, 279

  23. Rappaport TS (1996) Wireless communications: principles and practice. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  24. Patwari N, Hero AO III, Perkins M et al (2003) Relative location estimation in wireless sensor networks. Signal Process IEEE Trans 51(8):2137–2148

    Article  Google Scholar 

  25. Hamedani GG, Tata MN (1975) On the determination of the bivariate normal distribution from distributions of linear combinations of the variables. Am Math Mon 82(9):913–915

    Article  MathSciNet  MATH  Google Scholar 

  26. Moutarlier P, Chatila R (1990) An experimental system for incremental environment modelling by an autonomous mobile robot. Experimental robotics I. Springer 139:327–346

  27. Gai S, Jung EJ, Yi BJ (2014) Localization algorithm based on ZigBee wireless sensor network with application to an active shopping cart. In: Proceedings of IEEE conference on robots and intelligent systems, pp 4571–4576

Download references

Conflict of interest

Byung-Ju Yi declares that they have no conflict of interest. Shengnan Gai declares that they have no conflict of interest. Se-Min Oh declares that they have no conflict of interest.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Byung-Ju Yi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gai, S., Oh, SM. & Yi, BJ. ASC localization in noisy environment based on wireless sensor network. Intel Serv Robotics 8, 201–213 (2015). https://doi.org/10.1007/s11370-015-0172-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-015-0172-3

Keywords

Navigation