Citation

Ren M, Guo H, Shi J, Meng J. Micromachines (Basel) 2017; 8(11): e8110320.

Affiliation

Beijing Key Laboratory of Computational Intelligence and Intelligent Systems, Beijing 100124, China. S201761125@emails.bjut.edu.cn.

Copyright

(Copyright © 2017, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/mi8110320

PMID

30400511

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.

Language: en

Keywords

conditional random fields; indoor localization; inertial sensors; map matching; pedestrian navigation