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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.

Deep Learning-Based PDR Scheme That Fuses Smartphone Sensors and GPS Location Changes