Complementary Observer for Body Segments Motion Capturing by Inertial and Magnetic Sensors

Fourati, Hassen; Manamanni, Noureddine; Afilal, Lissan; Handrich, Yves

doi:10.1109/tmech.2012.2225151

Cited by 128 publications

(80 citation statements)

References 35 publications

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“…The raw data is processed here with the proposed filter and representative complementary ones including the LMA-Complementary Observer (LMA-CO) by Fourati et al [17] and Wahba's Complementary Filter (WCF) by Marantos et al [19] The factor of LMA in LMA-CO is set to 0.01 according to related literature while its complementary gain is set to 0.01 denoting the motion's change rate. The parameters of WCF follow the original author's determination that are given by 푤 푎 = 0.9, 푤 푚 = 0.8, 푐 1 = 0.7, 푐 2 = 0.3, 푐 3푎 = 8500.0, 푐 3푚 = 5500.0.…”

Section: Experiments 2: Comparisons With Representative Complementarymentioning

confidence: 99%

“…This commitment was revisited by Madgwick et al Journal of Sensors in 2011 showing that the gradient descent algorithm (GDA) could be another possible solution [6]. Besides, optimization methods like improved Gauss-Newton algorithm (IGNA, [16]) and Levenberg-Marquadt algorithm (LMA, [17]) are also applied for faster and more robust solutions. The above methods use optimization algorithms to compute attitude quaternion from the eCompass system.…”

Section: Introductionmentioning

confidence: 99%

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Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter

Guo

Wang

et al. 2017

Journal of Sensors

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Orientation estimation from magnetic, angular rate, and gravity (MARG) sensor array is a key problem in mechatronic-related applications. This paper proposes a new method in which a quaternion-based Kalman filter scheme is designed. The quaternion kinematic equation is employed as the process model. With our previous contributions, we establish the measurement model of attitude quaternion from accelerometer and magnetometer, which is later proved to be the fastest (computationally) one among representative attitude determination algorithms of such sensor combination. Variance analysis is later given enabling the optimal updating of the proposed filter. The algorithm is implemented on real-world hardware where experiments are carried out to reveal the advantages of the proposed method with respect to conventional ones. The proposed approach is also validated on an unmanned aerial vehicle during a real flight. Results show that the proposed one is faster than any other Kalman-based ones and even faster than some complementary ones while the attitude estimation accuracy is maintained.

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Section: Experiments 2: Comparisons With Representative Complementarymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter

Guo

Wang

et al. 2017

Journal of Sensors

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“…It can significantly improve the attitude estimation results combined with gyroscopes [15][16][17]. Apart from Davenport's method, it is also noticeable that the singular value decomposition (SVD) could be efficient.…”

Section: Introductionmentioning

confidence: 99%

Optimal Attitude Determination from Vector Sensors Using Fast Analytical Singular Value Decomposition

Liu

Gong

et al. 2018

Journal of Sensors

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A novel algorithm is proposed in this paper to solve the optimal attitude determination formulation from vector observation pairs, that is, the Wahba problem. We propose here a fast analytic singular value decomposition (SVD) approach to obtain the optimal attitude matrix. The derivations and mandatory proofs are presented to clarify the theory and support its feasibility. Through simulation experiments, the proposed algorithm is validated. The results show that it maintains the same attitude determination accuracy and robustness with conventional methodologies but significantly reduces the computation time.

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“…A typical inertial/magnetic sensor unit contains a triaxial accelerometer, a triaxial gyroscope, and a triaxial magnetometer, and these sensors are usually assembled together on a printed circuit board to form an inertial/magnetic measurement node. Thus far, extensive research has been performed on how to accurately determine attitude information from micro inertial/magnetic sensor measurements [5] [6]. Some researchers even moved beyond this and proposed to Manuscript received May 21, 2014; revised September 17, 2014; accepted for publication October 31, 2014.…”

Section: Introductionmentioning

confidence: 99%

Two-Step Calibration Methods for Miniature Inertial and Magnetic Sensor Units

Zhang

2014

IEEE Trans. Ind. Electron.

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Abstract-Low-cost inertial/magnetic sensor units have been extensively used to determine sensor attitude information for a wide variety of applications, ranging from virtual reality, underwater vehicles, handheld navigation systems, to bio-motion analysis and biomedical applications. In order to achieve precise attitude reconstruction, appropriate sensor calibration procedures must be performed in advance to process sensor readings properly. In this paper, we are aiming to calibrate different error parameters, such as sensor sensitivity/scale factor error, offset/bias error, non-orthogonality error, mounting error, and also the soft iron and hard iron errors for magnetometer. Instead of estimating all these parameters individually, these errors are combined together as the combined bias and transformation matrix. Two-step approaches are proposed to determine the combined bias and transformation matrix separately. For the accelerometer and magnetometer, the combined bias is determined by finding an optimal ellipsoid that can best fit the sensor readings, and the transformation matrix is then derived through a two-step iterative algorithm by exploring the intrinsic relationship among sensor readings. For the gyroscope, the combined bias can be easily determined by placing the sensor node stationary. For the transformation matrix estimation, the intrinsic relationship among gyroscope readings is also again, and an unscented Kalman filter is employed to determine such matrix. The calibration methods are then applied to our sensor nodes, and the good performance of the orientation estimation has illustrated the effectiveness of the proposed sensor calibration methods.

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Complementary Observer for Body Segments Motion Capturing by Inertial and Magnetic Sensors

Cited by 128 publications

References 35 publications

Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter

Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter

Optimal Attitude Determination from Vector Sensors Using Fast Analytical Singular Value Decomposition

Two-Step Calibration Methods for Miniature Inertial and Magnetic Sensor Units

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