Next Article in Journal
Investigation of Production Limits in Manufacturing Microstructured Surfaces Using Micro Coining
Previous Article in Journal
Acousto-Plasmonic Sensing Assisted by Nonlinear Optical Interactions in Bimetallic Au-Pt Nanoparticles
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Micromachines 2017, 8(11), 320; doi:10.3390/mi8110320

Indoor Pedestrian Navigation Based on Conditional Random Field Algorithm

1,2,3,* , 1,2,3
,
1,2,3
and
1,2,3
1
College of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2
Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China
3
Beijing Key Laboratory of Computational Intelligence and Intelligent Systems, Beijing 100124, China
*
Author to whom correspondence should be addressed.
Received: 22 August 2017 / Revised: 19 October 2017 / Accepted: 25 October 2017 / Published: 30 October 2017
View Full-Text   |   Download PDF [1541 KB, uploaded 30 October 2017]   |  

Abstract

Foot-mounted micro-electromechanical systems (MEMS) inertial sensors based on pedestrian navigation can be used for indoor localization. We previously developed a novel zero-velocity detection algorithm based on the variation in speed over a gait cycle, which can be used to correct positional errors. However, the accumulation of heading errors cannot be corrected and thus, the system suffers from considerable drift over time. In this paper, we propose a map-matching technique based on conditional random fields (CRFs). Observations are chosen as positions from the inertial navigation system (INS), with the length between two consecutive observations being the same. This is different from elsewhere in the literature where observations are chosen based on step length. Thus, only four states are used for each observation and only one feature function is employed based on the heading of the two positions. All these techniques can reduce the complexity of the algorithm. Finally, a feedback structure is employed in a sliding window to increase the accuracy of the algorithm. Experiments were conducted in two sites with a total of over 450 m in travelled distance and the results show that the algorithm can efficiently improve the long-term accuracy. View Full-Text
Keywords: indoor localization; pedestrian navigation; map matching; inertial sensors; conditional random fields indoor localization; pedestrian navigation; map matching; inertial sensors; conditional random fields
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Ren, M.; Guo, H.; Shi, J.; Meng, J. Indoor Pedestrian Navigation Based on Conditional Random Field Algorithm. Micromachines 2017, 8, 320.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Micromachines EISSN 2072-666X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top