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Research Papers

The Right Invariant Nonlinear Complementary Filter for Low Cost Attitude and Heading Estimation of Platforms

[+] Author and Article Information
Oscar De Silva

Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X5, Canada
e-mail: oscar.desilva@mun.ca

George K. I. Mann

Professor
Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X5, Canada
e-mail: gmann@mun.ca

Raymond G. Gosine

Professor
Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X5, Canada
e-mail: rgosine@mun.ca

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received September 17, 2016; final manuscript received July 8, 2017; published online September 8, 2017. Assoc. Editor: Suman Chakravorty.

J. Dyn. Sys., Meas., Control 140(1), 011011 (Sep 08, 2017) (10 pages) Paper No: DS-16-1447; doi: 10.1115/1.4037331 History: Received September 17, 2016; Revised July 08, 2017

This paper presents a novel filter with low computational demand to address the problem of orientation estimation of a robotic platform. This is conventionally addressed by extended Kalman filtering (EKF) of measurements from a sensor suit which mainly includes accelerometers, gyroscopes, and a digital compass. Low cost robotic platforms demand simpler and computationally more efficient methods to address this filtering problem. Hence, nonlinear observers with constant gains have emerged to assume this role. The nonlinear complementary filter (NCF) is a popular choice in this domain which does not require covariance matrix propagation and associated computational overhead in its filtering algorithm. However, the gain tuning procedure of the complementary filter is not optimal, where it is often hand picked by trial and error. This process is counter intuitive to system noise based tuning capability offered by a stochastic filter like the Kalman filter. This paper proposes the right invariant formulation of the complementary filter, which preserves Kalman like system noise based gain tuning capability for the filter. The resulting filter exhibits efficient operation in elementary embedded hardware, intuitive system noise based gain tuning capability and accurate attitude estimation. The performance of the filter is validated using numerical simulations and by experimentally implementing the filter on an ARDrone 2.0 micro aerial vehicle (MAV) platform.

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References

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Figures

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Fig. 1

The overview of this study and filters designed as part of this work

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Fig. 2

Gain matrix K(t) of (a) the RIEKF*, (b) the LIEKF*, and (c) the RINCF for a smooth trajectory

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Fig. 3

The simulated trajectory of the platform and the estimation accuracy provided by the RINCF for case 1

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Fig. 4

Performance of the RINCF for case 1 compared with main attitude estimators reported in this work

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Fig. 5

Gain matrix K(t) of the RIEKF* (top) and the RINCF2 (bottom), for a smooth trajectory with maximum angular velocity ωmax=π/3 (left), ωmax=2π/3 (middle), and ωmax=5π/3 (right)

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Fig. 6

Comparative performance of the RINCF for all cases

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Fig. 7

The experimental validation setup used in the study

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Fig. 8

(a) Attitude heading estimates of the EKF and (b) attitude heading estimates of the RINCF for experimental data

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Fig. 9

Attitude heading estimates of the filters for experimental data. RINCF performance was validated by implementing the filter on the MAV platform.

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