UAV attitude estimation based on MARG and optical flow sensors using gated recurrent unit

Xiaoqin Liu, Xiang Li, Qi Shi, Chuanpei Xu and Yanmei Tang

International Journal of Distributed Sensor Networks, 2021, vol. 17, issue 4, 15501477211009814

Abstract: Three-dimensional attitude estimation for unmanned aerial vehicles is usually based on the combination of magnetometer, accelerometer, and gyroscope (MARG). But MARG sensor can be easily affected by various disturbances, for example, vibration, external magnetic interference, and gyro drift. Optical flow sensor has the ability to extract motion information from image sequence, and thus, it is potential to augment three-dimensional attitude estimation for unmanned aerial vehicles. But the major problem is that the optical flow can be caused by both translational and rotational movements, which are difficult to be distinguished from each other. To solve the above problems, this article uses a gated recurrent unit neural network to implement data fusion for MARG and optical flow sensors, so as to enhance the accuracy of three-dimensional attitude estimation for unmanned aerial vehicles. The proposed algorithm can effectively make use of the attitude information contained in the optical flow measurements and can also achieve multi-sensor fusion for attitude estimation without explicit mathematical model. Compared with the commonly used extended Kalman filter algorithm for attitude estimation, the proposed algorithm shows higher accuracy in the flight test of quad-rotor unmanned aerial vehicles.

Keywords: Unmanned aerial vehicle; attitude estimation; data fusion; optical flow sensor; magnetometer; accelerometer; gyroscope; gated recurrent unit (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:17:y:2021:i:4:p:15501477211009814

DOI: 10.1177/15501477211009814

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