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End-to-End Sequential Indoor Localization Using Smartphone Inertial Sensors and WiFi

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posted on 10.12.2021, 06:00 authored by Mukhamet Nurpeiissov, Askat KuzdeuovAskat Kuzdeuov, Aslan Assylkhanov,, Yerbolat KhassanovYerbolat Khassanov, Hüseyin Atakan VarolHüseyin Atakan Varol
This paper addresses sequential indoor localization using WiFi and Inertial Measurement Unit (IMU) modules commonly found in commercial off-the-shelf smartphones. Specifically, we developed an end-to-end neural network-based localization system integrating WiFi received signal strength indicator (RSSI) and IMU data without external data fusion models. The developed system leverages the advantages of WiFi and IMU modules to locate finer-level sequential positions of a user at 150 Hz sampling rate. Additionally, to demonstrate the efficacy of the proposed approach, we created the IMUWiFine dataset comprising IMU and WiFi RSSI readings sequentially collected at fine-level reference points. The dataset contains 120 trajectories covering an aggregate distance of over 14 kilometers. We conducted extensive experiments using deep learning models and achieved a mean error distance of 1.1 meters on an unseen evaluation set, which makes our approach suitable for many practical applications requiring meter-level accuracy. To enable experiment and result reproducibility, we made the developed localization system and IMUWiFine dataset publicly available in our GitHub repository.

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Email Address of Submitting Author

yerbolat.khassanov@nu.edu.kz

ORCID of Submitting Author

0000-0001-9422-6833

Submitting Author's Institution

ISSAI, Nazarbayev University

Submitting Author's Country

Kazakhstan