[time]
2020-11-16T12:58:33+01:00

[team_name]
SZU Mellivora Capensis

[team_institution]
Shenzhen University 

[logolink]


[system_name]
SAM-AI Location System

[website]


[track]
6

[reference_person]
Xu Liu

[email]
xuliu_ksy@126.com

[description]
Our system proposes a novel method (named SAM-AI Location System) that integrated an improved deep recurrent neural network-based method with a traditional 3D inertial dead-reckoning method to achieve high-precision positioning using the sensor data obtained from smart phone. 
Nowadays, vehicle-based and smartphone-based positioning and is one of the current research hotspots to realize the robust low-cost vehicle location in the city road lacking of GNSS signal or signal attenuation. Vehicle positioning based on low-cost vehicle smart phone sensor plays an important role in vehicle integrated navigation. However, the common low-cost inertial sensors in smart phones are troubled by noise. The accumulated error will increase infinitely due to the measurement error of acceleration and the gyroscope itself.
The system transforms the coordinate system according to the attitude data, and integrates GNSS information when it detects GNSS signals. The system uses the dead reckoning inertial method based on IMU to estimate the three-dimensional position, speed and direction of the vehicle. At the same time, we use the deep recurrent neural networks to learn the data of accelerometer, gyroscope and magnetometer to train model and predict the highly accurate vehicle track. The inertial-based track obtained by the integrated method through federated filter. Moreover, this system adds GPS information where it detects a GPS signal. GPS signal provide starting information for our inertial-based algorithms. It is worth mentioning that we consider the transformation of coordinate system and the installation angle of smart phone in the process of deducing trajectory.The positioning results of the competition show that the SAM-AI Location System proposed by us can solve the positioning requirements in the complex indoor and outdoor environment.

[references]