Pedestrian Dead Reckoning Using Pocket-Worn Smartphone

Hongyu Zhao,Jian Xu,Sen Qiu,Luyao Zhang,Ning Yang,Zhelong Wang

doi:10.1109/access.2019.2927053

Hongyu Zhao, Jian Xu + Show 4 more

Open Access

https://doi.org/10.1109/access.2019.2927053

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Journal: IEEE Access	Publication Date: Jan 1, 2019
Citations: 34	License type: CC BY 4.0

Affiliation: Dalian University of Technology

Abstract

Indoor pedestrian dead reckoning (PDR) extends the location-based service (LBS) to environments, where GPS or beacon signals are degraded or unavailable. A practical PDR system should consider the absence of any infrastructure or prior knowledge of the environment. This paper presents a PDR system based on a pocket-worn smartphone, which tracks a person's location through dead reckoning calculation by using the sensors embedded in smartphones. However, a smartphone-based PDR system faces various challenges, especially the heading drift due to gyroscope bias. In this paper, the gradient descent algorithm (GDA) is improved to reduce the heading drift, by fusing inertial data with only a fraction of magnetometer data that are accurate and usable. There is an 80% probability that the heading error is reduced to less than 4 degrees. Besides, a stride detection method is developed based on thigh angles, and then, a stride length estimation method is implemented in a complementary way. The experiments were conducted along with three types of reference paths, and the experimental results demonstrate that the average position errors along the three paths are 1.62%, 1.00%, and 0.92%, respectively. Despite the inherent sensor noise and complex human locomotion, the smartphone-based PDR system has great potential in pedestrian tracking.

Highlights

In the past decades, smartphones have gradually become indispensable electronic products in people’s daily life [1]
This paper aims to develop a novel pedestrian dead reckoning (PDR) algorithm for indoor applications, by using the inertial sensors and magnetometer embedded in a standard off-the-shelf smartphone
WORK This paper implements a self-contained PDR system based on a pocket-worn smartphone, by using the inertial sensor and magnetometer embedded in the smartphone

Summary

INTRODUCTION

Smartphones have gradually become indispensable electronic products in people’s daily life [1]. MEMS IMU-based localization is an infrastructure-free technique to track a person’s location in indoor environments. The conventional strapdown inertial navigation system (SINS) performs position update by numerical integration, which makes that any small error will accumulate over time This mechanism requires high-accuracy inertial sensors, whereas the smartphone’s built-in sensors cannot meet the accuracy requirement. There are many advantages of using smartphone for indoor localization: (1) as a ubiquitous portable electronic device, smartphone provides a promising platform for LBS; (2) it requires no external infrastructure and environmental information (e.g., radio fingerprint map); (3) it accords with people’s established habits due to its widespread use in outdoor localization applications. This paper aims to develop a novel PDR algorithm for indoor applications, by using the inertial sensors and magnetometer embedded in a standard off-the-shelf smartphone. The feasibility and validity of the smartphone-based PDR algorithm is verified through offline implementation with extensive experiments

SENSOR CALIBRATION AND INITIAL ALIGNMENT

B S q denotes the rotation between

ATTITUDE ESTIMATION

STRIDE LENGTH ESTIMATION

EXPERIMENT AND RESULT

Findings

CONCLUSION AND FUTURE WORK