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

Author

Listed:
  • Xiaoqin Liu
  • Xiang Li
  • Qi Shi
  • Chuanpei Xu
  • Yanmei Tang

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.

Suggested Citation

  • Xiaoqin Liu & Xiang Li & Qi Shi & Chuanpei Xu & Yanmei Tang, 2021. "UAV attitude estimation based on MARG and optical flow sensors using gated recurrent unit," International Journal of Distributed Sensor Networks, , vol. 17(4), pages 15501477211, April.
    • Handle: RePEc:sae:intdis:v:17:y:2021:i:4:p:15501477211009814
    DOI: 10.1177/15501477211009814
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    References listed on IDEAS

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    1. Shahrukh Ashraf & Priyanka Aggarwal & Praveen Damacharla & Hong Wang & Ahmad Y Javaid & Vijay Devabhaktuni, 2018. "A low-cost solution for unmanned aerial vehicle navigation in a global positioning system–denied environment," International Journal of Distributed Sensor Networks, , vol. 14(6), pages 15501477187, June.
    2. Kolanowski, Krzysztof & Świetlicka, Aleksandra & Kapela, Rafał & Pochmara, Janusz & Rybarczyk, Andrzej, 2018. "Multisensor data fusion using Elman neural networks," Applied Mathematics and Computation, Elsevier, vol. 319(C), pages 236-244.
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