Sensor-Based Globally Asymptotically Stable Filters for Attitude Estimation: Analysis, Design, and Performance Evaluation

Batista, Pedro; Silvestre, Carlos; Oliveira, Paulo

doi:10.1109/tac.2012.2187142

Cited by 57 publications

(31 citation statements)

References 17 publications

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“…These topological obstructions can be avoided by allowing the attitude estimate to evolve on a larger state space; this strategy has recently been employed by Oliveira (2011a,b, 2012b) and by the authors (Grip, Saberi, & Johansen, 2011, by estimating a matrix with nine degrees of freedom that converges to a rotation matrix on SO(3). Batista et al (2012b) and Batista, Silvestre, and Oliveira (2012c) also considered the use of a separate observer to produce globally exponentially stable gyro bias estimates, although their approach requires stationary reference vectors. Attitude estimation using a single vector measurement has been considered by several authors (Batista et al, 2011b;Batista, Silvestre, & Oliveira, 2012a;Kinsey & Whitcomb, 2007;Lee, Leok, McClamroch, & Sanyal, 2007;Mahony, Hamel, Trumpf, & Lageman, 2009) and is possible under a persistency-of-excitation (pe) condition.…”

Section: Introductionmentioning

confidence: 99%

Globally exponentially stable attitude and gyro bias estimation with application to GNSS/INS integration

Grip

Fossen²,

Johansen³

et al. 2015

Automatica

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Section: Introductionmentioning

confidence: 99%

Globally exponentially stable attitude and gyro bias estimation with application to GNSS/INS integration

Grip

Fossen²,

Johansen³

et al. 2015

Automatica

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“…[5] were used successfully to estimate the attitude but they don't achieve global stability due to the topological obstruction [6]. A globally asymptotically stable complementary filters for attitude estimation were proposed in [7] by combining a vector-based filter with an optimal algorithm for attitude determination. In this method, the vector-based filter was designed as a Kalman filter using Linear Time Variant representation of the nonlinear kinematic equation.…”

Section: Introductionmentioning

confidence: 99%

“…It has the advantage to improve the quality of estimation by choosing the adequate filter. Generally, the structure based on complementary filter does not consider the sensors dynamics [5], [8], [7]. Indeed, sensors with low bandwidth provide measurement with phase delay which may lead to inaccurate estimation.…”

Section: Introductionmentioning

confidence: 99%

Sensors model based data fusion using complementary filters for attitude estimation and stabilization

Hadri¹,

Benziane²,

Seba³

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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This paper proposes simple and efficient algorithms for implementation of attitude estimation and control based on data fusion using complementary filters taking into account sensors dynamics. First of all, we propose a passive form of the filter by fusing the measured inertial vectors and the gyro measurements in order to reconstruct real inertial vectors which can be used with any algebraic algorithm (TRIAD, QUEST, etc.) that leads to globally asymptotic attitude estimation. Thereafter, the same principle of data fusion is used to address the problem of attitude stabilization. Then, instead of using direct raw measurements in control law we propose a new solution that leads to accurate estimation of inertial vectors by using complementary filters based on sensors dynamics. The stability analysis of the error dynamics based on Lyapunov method proved that almost all trajectories converge asymptotically to the desired equilibrium point. Simulation results show the effectiveness and the performance of the proposed solutions.

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“…[1][2][3][4][5][6][7][8]). The classic approach consists in the use of a Kalman filter [9] and their nonlinear counterparts, such as the Extended Kalman Filter (EKF) or the Unscented Kalman Filter (UKF) [10,11].…”

Section: Introductionmentioning

confidence: 99%

Single axis FOG aided attitude estimation algorithm for mobile robots

et al. 2015

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This paper is focused on an attitude estimation method for Autonomous Underwater Vehicles (AUVs).\ud Data acquired by a commercial Micro-Electro-Mechanical Systems (MEMS) Inertial Measurement Unit\ud (IMU), equipped with magnetometers, and a Fibre Optic Gyroscope (FOG) are fused to estimate the\ud attitude of the vehicle. One of the most used attitude estimation filter, a Nonlinear Complementary\ud Filter (NCF), is proposed as the basis of this work; then, some adaptations to the original formulation of\ud the filter are illustrated to better suit it to the field of underwater robotics. The proposed improvements\ud include the online tuning of the gains of the filter to cope with sensor disturbances and the employment\ud of the data acquired by a FOG. In addition, a fast procedure for the calibration of a magnetometer\ud is introduced to increase the reliability of its readings. The resulting filter is used to estimate the\ud attitude of an AUV; the performances of the proposed solution are tested and evaluated, in particular\ud when unpredictable magnetic disturbances are present, highlighting the improvements that the applied\ud changes allow to achieve in the specific field of application

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Sensor-Based Globally Asymptotically Stable Filters for Attitude Estimation: Analysis, Design, and Performance Evaluation

Cited by 57 publications

References 17 publications

Globally exponentially stable attitude and gyro bias estimation with application to GNSS/INS integration

Globally exponentially stable attitude and gyro bias estimation with application to GNSS/INS integration

Sensors model based data fusion using complementary filters for attitude estimation and stabilization

Single axis FOG aided attitude estimation algorithm for mobile robots

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