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Research Papers

Hybrid Low-Cost Approach for Quadrotor Attitude Estimation

[+] Author and Article Information
Bara J. Emran, Muhannad Al-Omari, Mamoun F. Abdel-Hafez

Mechanical Engineering Department,
American University of Sharjah,
Sharjah 26666, UAE

Mohammad A. Jaradat

Mechanical Engineering Department,
American University of Sharjah,
Sharjah 26666, UAE
Department of Mechanical Engineering,
Jordan University of Science and Technology,
Irbid 22110, Jordan

Manuscript received December 31, 2013; final manuscript received September 7, 2014; published online February 11, 2015. Assoc. Editor: Dr. Corina Sandu.

J. Comput. Nonlinear Dynam 10(3), 031010 (May 01, 2015) (9 pages) Paper No: CND-13-1332; doi: 10.1115/1.4028524 History: Received December 31, 2013; Revised September 07, 2014; Online February 11, 2015

This paper presents a new approach to estimate the orientation of a quadrotor using single low-cost inertial measurement unit (IMU) sensor. The proposed hybrid solution uses two extended Kalman filters (EKF) along with a direction cosine matrix (DCM) algorithm. An EKF utilizes the dynamics of the quadrotor to filter the noise on the body-frame (B-frame) angular rates measured by the three-axis gyroscope sensor. Then, a DCM algorithm uses the filtered gyro signal along with the reading from a three-axis accelerometer and a three-axis magnetometer sensor to estimate the Euler angles. Finally, an additional EKF is presented to enhance the final estimates of the Euler angles. The performance of the proposed hybrid approach is tested and compared with other commonly used methods. Results are presented at the end of the paper to verify the performance of the proposed method. The results show an improvement in the estimated quadrotor's state. Monte Carlo tests are performed to ensure sustained high accuracy estimation.

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Figures

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Fig. 1

B-frame and E-frame

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Fig. 4

Rate EKF block diagram

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Fig. 5

Rate EKF results in filtering the gyroscope readings

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Fig. 6

Euler EKF block diagram

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Fig. 7

3D simulation environment

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Fig. 8

Pitch angle estimation

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Fig. 9

A zoomed view of pitch angle estimation

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Fig. 10

Roll angle estimation

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Fig. 11

A zoomed view of roll angle estimation

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Fig. 12

A zoomed view of heading angle estimation

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Fig. 13

Monte Carlo test for pitch angle

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Fig. 14

Monte Carlo test for roll angle

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Fig. 15

Monte Carlo test for heading angle

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