Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Map-Matching Algorithm for MEMS-Based Pedestrian Dead Reckoning System in the Mobile Device

모바일 장치용 MEMS 기반 보행항법시스템을 위한 맵매칭 알고리즘

  • 신승혁 (서울대학교 기계항공공학부) ;
  • 김현욱 (서울대학교 기계항공공학부) ;
  • 박찬국 (서울대학교 기계항공공학부/자동화시스템공동연구소(ASRI)) ;
  • 최상언 (삼성전자)
  • Published : 2008.11.01
Download PDF
⟨ Previous Next ⟩

Abstract

We introduce a MEMS-based pedestrian dead reckoning (PDR) system. A walking navigation algorithm for pedestrians is presented and map-matching algorithm for the navigation system based on dead reckoning (DR) is proposed. The PDR is equipped on the human body and provides the position information of pedestrians. And this is able to be used in ubiquitous sensor network (USN), U-hearth monitoring system, virtual reality (VR) and etc. The PDR detects a step using a novel technique and simultaneously estimates step length. Also an azimuth of the pedestrian is calculated using a fluxgate which is the one of magnetometers. Map-matching algorithm can be formulated to integrate the positioning data with the digital road network data. Map-matching algorithm not only enables the physical location to be identified from navigation system but also improves the positioning accuracy. However most of map-matching algorithms which are developed previously are for the car navigation system (CNS). Therefore they are not appropriate to implement to pedestrian navigation system based on DR system. In this paper, we propose walking navigation system and map-matching algorithm for PDR.

Keywords

References

  1. R. Jirawimut, P. Ptasinski, V. Garaj, F. Cecelja and W. Balachandran, 'A Method for Dead Reckoning Parameter Correction in Pedestrian Navigation System,' Proc. of the 18th IEEE Instrumentation and Measurement Technology Conf., Budapest, Hungary, May 2001
  2. Q. Ladetto, 'On foot navigation: continuous step calibration using both complementary recursive prediction and adaptive Kalman filtering,' Proc. of ION-GPS 2000, pp.1735-1740., Salt Lake, Utah, USA, Sep. 2000
  3. S. H. Shin, C. G. Park, H. S. Hong and J. M. Lee, 'MEMSbased Personal Navigator Equipped on the User's Body,' Proc. ION GNSS 2005, Long Beach, CA, Sep. 2005
  4. S. H. Shin, C. G. Park, J. W. Kim, H. S. Hong and J. M. Lee, 'Adaptive Step Length Estimation Algorithm Using Low-Cost MEMS Inertial Sensors,' Sensors Applications Symposium, 2007. IEEE, Feb. 2007, San Diego, USA
  5. K. Aminian, Ph. Robert, E. Jequier and Y. Schutz, 'Level, Downhill and Uphill Walking Identification using Neural Networks,' Electronics Letters 19th vol.29, no.17, Aug. 1993
  6. S. Y. Cho and C. G. Park, 'Tilt compensation algorithm for a 2- axis magnetic compass,' IEE Electronic Letters, vol.39, Issue 22, Oct. 2003
  7. S. Y. Cho, 'Design of a Pedestrian Navigation System and the Error Compensation Using RHKF Filter' ph.D. dissertation, Kwangwoon Univ., South Korea, Feb. 2004
  8. C. E. White, D. Bernstein and A.L. Kornhauser, 'Some Map Matching Algorithms for Personal Navigation Assistants,' Transportation Research: Part C8, pp. 91-108, 2000 https://doi.org/10.1016/S0968-090X(00)00026-7
  9. W. Y. Ochieng, M. A. Quddus and R. B. Noland, 'Integrated positioning algorithms for transport telematics applications,' 2004 ION Annual Conference, CA, USA, 692-705, Sep. 2004
  10. Quddus, M.A., Ochieng, W.Y., Noland, R.B., 'A High Accuracy Fuzzy Logic Based Map Matching Algorithm for Road Transport,' Journal of Intelligent Transportation Systems, vol. 10, no. 3, pp. 103-115, 2006 https://doi.org/10.1080/15472450600793560
  11. H. Leppakoskik, J. Kapi, J. Syrjarinne and J. Takala, 'Error Analysis of Step Length Estimation Pedestrian Dead Reckoning,' Proc. ION GPS, Portland, OR, pp. 1136-1142. Sep. 2002

Cited by

  1. A study on indoor navigation system using localization based on wireless communication vol.37, pp.1, 2013, https://doi.org/10.5916/jkosme.2013.37.1.114
  2. Biaxial Accelerometer-based Magnetic Compass Module Calibration and Analysis of Azimuth Computational Errors Caused by Accelerometer Errors vol.20, pp.2, 2014, https://doi.org/10.5302/J.ICROS.2014.13.9008
  3. Map assisted PDR/Wi-Fi fusion for indoor positioning using smartphone vol.15, pp.2, 2017, https://doi.org/10.1007/s12555-015-0342-2