Simplicity or flexibility? Complementary Filter vs. EKF for orientation estimation on mobile devices

Nowicki, Michał; Wietrzykowski, Jan; Skrzypczyński, Piotr

doi:10.1109/cybconf.2015.7175926

Cited by 19 publications

(13 citation statements)

References 11 publications

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“…Common calibration procedures performed in laboratories allow for the determination and compensation of the scale factor and misalignment errors. This process exceeds the scope of this article; therefore, we assume that the compensation has already been performed (M Ω = I and ∆S Ω = 0) [22,41,62]. Based on Equation (5), the gyro-based attitude realization is given in quaternion form as…”

Section: Gyroscope Modelmentioning

confidence: 99%

“…As the sampling time in the ROS-based framework was relatively low (T s = 1ms), the adaptive strategy could be executed with bigger window size of L = 400; moreover, the length of the transform was L FFT = 2 9 and the threshold oscillation frequency and amplitude were f thr = 10 Hz and |Ω| thr = 0.26 rad/s, respectively. The process noises µ q k and ν k in Equation (16) were considered to be statistically independent [1,19,41]; therefore, diagonal matrices were applied for both the process and measurement noise covariances with the following characteristics,…”

Section: Tuning Of Filter Parametersmentioning

confidence: 99%

“…The proposed method was evaluated under dynamic conditions and compared with a threshold-based KF; however, significant improvement in the estimation accuracy was not highlighted. In [14,41], smartphone-based human body orientation estimation was addressed with the application of a qAEKF. The proposed adaptive strategy modified the noise covariance matrix based on the variance of input signal.…”

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confidence: 99%

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A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Odry

Kecskés

Šarčević

et al. 2020

Sensors

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This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy.The MEMS inertial measurement unit (IMU), composed of tri-axis MEMS accelerometer, gyroscope, and magnetometer sensors, also known as the measurement system of magnetic, angular rate, and gravity (MARG) sensor arrays, is the most commonly utilized device to track the real-time orientation of mobile platforms at present. The low-cost, low power consumption, and small size characteristics meet technological requirements, and therefore these devices have been widely utilized in embedded systems, where the filtering algorithm is executed by a microprocessor. As a result, an attitude and heading reference system (AHRS) has been formed, which provides the complete orientation measurement relative to the Earth's gravitational and magnetic fields (global reference system), where the attitude denotes the roll and pitch angles, whereas heading refers to the yaw Euler angle [19]. The role of the aforementioned filtering algorithm is to combine the individual features of each sensor and provide both properly smoothed and robust attitude results with regard to the global reference system, in either Euler angles or quaternions. The most common method applied in sensor fusion techniques synthesizes the short-term accuracy of gyroscope-based attitude realizations and the accelerometer and magnetometer provide rough, low-frequency attitude corrections. This technique cancels the accumulated error (drift), smooths the signals, and produces long-term stable outputs if the IMU is in stationary states. Significant decrease in estimation performance arises when...

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Section: Gyroscope Modelmentioning

confidence: 99%

Section: Tuning Of Filter Parametersmentioning

confidence: 99%

mentioning

confidence: 99%

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A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Odry

Kecskés

Šarčević

et al. 2020

Sensors

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show abstract

“…This is accomplished by calculating the variance and adjusting the covariance matrix to better account for the current state. For a detailed explanation and proof of the algorithm see Gośliński et al, 4 and Nowicki et al 6 The implementation used in testing relies only on the gyroscope and native Android orientation. In Gośliński et al they discuss using the Magnetometer and Accelerometer, however, in their implementation online 11 they do not use either.…”

Section: Orientation Algorithmsmentioning

confidence: 99%

Smart phone orientation estimation comparisons using three axis gimbal

Gaquin

Fields

2016

SPIE Proceedings

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Smartphones have put powerful sensor arrays in nearly everyone's pockets. Fusing the data from these sensors it is possible to estimate the phone's current orientation. In this study we utilize a 3 axis gimbal to compare the performance of multiple orientation estimation algorithms. Controlling the position of the gimbal allows us to compare the known device orientation to the estimated orientation. Using this same method we determine where each algorithm's faults lie, and where they begin to break down. Then repeating these movements we are able to compare each algorithm to each other.

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“…Gyroscope, accelerometer, and magnetometer measurements can be combined using complementary filters (Bachmann et al 2001 However, complementary filters fail to consider the stochastic error characteristics (e.g. white noise) associated with sensor measurements (Nowicki, Wietrzykowski, and Skrzypczyński 2015). In contrast, particle and Kalman filters combine measurements based on stochastic properties of sensor noise measurements (Sabatini 2011a).…”

Section: Sensor Fusionmentioning

confidence: 99%

The effects of movement speeds and magnetic disturbance on inertial measurement unit accuracy

Chen¹

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There are countless individuals who have supported me throughout my time as a graduate student. This journey would probably not have started if Prof. Geb Thomas did not take me into his research lab early in my sophomore year, a point in time where few other professors would see value in a student, especially one with less-than-stellar academic records. Throughout my time working with him as an undergraduate and master's student, he showed me the path less traveled, taught me how to 'think like an engineer', and gave me the skills to turn ideas into reality. I am also grateful for having him as my academic advisor and committee member throughout my doctoral work. This journey would not have gone smoothly if it wasn't for friends to keep everything in perspective. I would like to thank old friends that I have known as an undergraduate, who still make frequent pilgrimages back to visit, including Stephanie Swanson, Jose Ponce, Michael Carbone, and many others. These are the people that constantly remind me how truly special Iowa City is. I would also like to thank my undergraduate friends that have stayed here for their pursuit of advanced degrees, including

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Simplicity or flexibility? Complementary Filter vs. EKF for orientation estimation on mobile devices

Cited by 19 publications

References 11 publications

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Smart phone orientation estimation comparisons using three axis gimbal

The effects of movement speeds and magnetic disturbance on inertial measurement unit accuracy

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