JOURNAL ARTICLE

Pedestrian navigation using inertial sensors and altitude error correction

Wen LiuYingjun ZhangXuefeng YangShengwei Xing

Year: 2015 Journal:   Sensor Review Vol: 35 (1)Pages: 68-75   Publisher: Emerald Publishing Limited

Abstract

Purpose – The aim of this article is to present a PIN (pedestrian inertial navigation) solution that incorporates altitude error correction, which eliminates the altitude error accurately without using external sensors. The main problem of PIN is the accumulation of positioning errors due to the drift caused by the noise in the sensors. Experiment results show that the altitude errors are significant when navigating in multilayer buildings, which always lead to localization to incorrect floors. Design/methodology/approach – The PIN proposed is implemented over an inertial navigation systems (INS) framework and a foot-mounted IMU. The altitude error correction idea is identifying the most probable floor of each horizontal walking motion. To recognize gait types, the walking motion is described with angular rate measured by IMU, and the dynamic time warping algorithm is used to cope with the different dimension samples due to the randomness of walking motion. After gait recognition, the altitude estimated with INS of each horizontal walking is checked for association with one of the existing in a database. Findings – Experiment results show that high accuracy altitude is achieved with altitude errors below 5 centimeters for upstairs and downstairs routes in a five floors building. Research limitations/implications – The main limitations of the study is the assumption that accuracy floor altitude information is available. Originality/value – Our PIN system eliminates altitude errors accurately and intelligently, which benefits from the new idea of combination of gait recognition and map-matching. In addition, only one IMU is used which is different from other approach that use external sensors.

Keywords:
Inertial measurement unit Inertial navigation system Gait Computer vision Computer science Artificial intelligence Altitude (triangle) Noise (video) Accelerometer Simulation Dynamic time warping Inertial frame of reference Mathematics Physics

Metrics

4
Cited By
0.50
FWCI (Field Weighted Citation Impact)
19
Refs
0.73
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Indoor and Outdoor Localization Technologies
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
Gait Recognition and Analysis
Physical Sciences →  Engineering →  Biomedical Engineering
Video Surveillance and Tracking Methods
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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