Abstract
Pedestrian dead reckoning (PDR), a sensor-based localization method using a smartphone, combines multi-sensor data from an inertial measurement unit (IMU) generated by the movement of pedestrians and calculates the amount of movement change from a previous location using fusion of sensor data. In this study, we propose a method to improve the efficiency of a deep learning (DL)-based PDR scheme to solve problems associated with the existing PDR method. The proposed DL-PDR scheme solves the movement change of smartphone users as a regression problem by combining IMU and global positioning system (GPS) data. In this paper, we (1) describe the existing PDR methods and problems, describe the proposed DL-PDR scheme and the data collection process of the input sensor data and output GPS used for deep learning, (2) correlate the collected I/O data and conduct preprocessing to make the data suitable for learning, (3) apply data refining and data augmentation methods to provide efficient learning and prevent overfitting, and (4) Verify the performance of the proposed scheme. The localization performance between the proposed scheme and existing methods is compared in various buildings where continuous localization is possible owing to connected indoor/outdoor spaces.
Highlights
Research on efficient and accurate localization regarding indoor/outdoor environments using communication technology and multi-sensors in smartphones is being actively conducted [1]–[4]
Various sensors exist in the mechanical systems (MEMS) of a smartphone, and the sensor group that measures the inertia generated by the movement of a user is called an inertial measurement unit (IMU), which consists of an accelerometer sensor [scale: m/sec2], a magnetometer sensor [scale: μT], and gyroscope sensor [scale: rad/sec]
To implement the proposed deep learning (DL)-pedestrian dead reckoning (PDR) and augmentation methods, a data collection application was built on the Android OS, and the entire experiment was conducted using a Samsung Galaxy S8 smartphone
Summary
Research on efficient and accurate localization regarding indoor/outdoor environments using communication technology and multi-sensors in smartphones is being actively conducted [1]–[4]. The proposed DL-PDR scheme uses sensor data that a smartphone user can collect while walking in an outdoor environment as input data and the amount of change in GPS location data between steps as label data for supervised learning. This information is used to predict the amount of change in the users’ movement based on the IMU sensor data by approaching the IMU-based PDR method as a regression problem in which deep learning applies an approximation function to solve a problem. Problems caused by sensor measurement methods and the surrounding environment are described
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