[time]
2023-07-29T05:34:35+02:00

[track]
3

[team_name]
SZU

[team_institution]
Shenzhen University, College of Civil and Transportation Engineering

[logolink]


[team_members]


[reference_person]
Mengyuan Tang

[reference_email]
2210474152@email.szu.edu.cn

[description_short]
Our system proposes to use deep recurrent neural networks to learn the sensor data from smartphones. Based on sensor data and BLE beacons information, we train to recognize the basic behaviors of pedestrians inside buildings (detecting behaviors like going upstairs, in a lift, turning, etc.), and integrate the attitude results of pedestrians dead reckoning (PDR) methods to achieve trajectory positioning of pedestrians in multi-floor buildings.
Nowadays, Indoor space has been an important space for human activities and people spend more than 80% of their time in the indoor environment. However, as a common scenario in urban indoor spaces, multi-floor buildings face issues such as missing or attenuated GNSS signals. Therefore, it is one of the current research hotspots to realize the robust low-cost navigation and positioning in complex indoor environments.
Our system uses the PDR methods based on the Inertial Measurement Unit (IMU) to estimate the three-dimensional position, s

[description_long_link]
https://maifile.cn/est/a3296906014319/pdf

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true

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