3,417 research outputs found
Quaternion-Based Robust Attitude Estimation Using an Adaptive Unscented Kalman Filter
This paper presents the Quaternion-based Robust Adaptive Unscented Kalman Filter (QRAUKF) for attitude estimation. The proposed methodology modifies and extends the standard UKF equations to consistently accommodate the non-Euclidean algebra of unit quaternions and to add robustness to fast and slow variations in the measurement uncertainty. To deal with slow time-varying perturbations in the sensors, an adaptive strategy based on covariance matching that tunes the measurement covariance matrix online is used. Additionally, an outlier detector algorithm is adopted to identify abrupt changes in the UKF innovation, thus rejecting fast perturbations. Adaptation and outlier detection make the proposed algorithm robust to fast and slow perturbations such as external magnetic field interference and linear accelerations. Comparative experimental results that use an industrial manipulator robot as ground truth suggest that our method overcomes a trusted commercial solution and other widely used open source algorithms found in the literature
Motion-based remote control device for interaction with multimedia content
This dissertation describes the development and implementation of techniques to enhance
the accuracy of low-complexity lters, making them suitable for remote control devices
in consumer electronics. The evolution veri ed in the last years, on multimedia contents,
available for consumers in Smart TVs and set-top-boxes, is not raising the expected
interest from users, and one of the pointed reasons for this nding is the user interface.
Although most current pointing devices rely on relative rotation increments, absolute
orientation allows for a more intuitive use and interaction. This possibility is explored in
this work as well as the interaction with multimedia contents through gestures.
Classical accurate fusion algorithms are computationally intensive, therefore their implementation
in low-energy consumption devices is a challenging task. To tackle this
problem, a performance study was carried, comparing a relevant set of professional commercial
of-the-shelf units, with the developed low-complexity lters in state-of-the-art
Magnetic, Angular Rate, Gravity (MARG) sensors. Part of the performance evaluation
tests are carried out under harsh conditions to observe the algorithms response in a nontrivial
environment. The results demonstrate that the implementation of low-complexity
lters using low-cost sensors, can provide an acceptable accuracy in comparison with the
more complex units/ lters. These results pave the way for faster adoption of absolute
orientation-based pointing devices in interactive multimedia applications, which includes
hand-held, battery-operated devices
Human motion tracking based on complementary Kalman filter
Miniaturized Inertial Measurement Unit (IMU) has been widely used in many motion capturing applications. In order to overcome stability and noise problems of IMU, a lot of efforts have been made to develop appropriate data fusion method to obtain reliable orientation estimation from IMU data. This article presents a method which models the errors of orientation, gyroscope bias and magnetic disturbance, and compensate the errors of state variables with complementary Kalman filter in a body motion capture system. Experimental results have shown that the proposed method significantly reduces the accumulative orientation estimation errors
Gain-Scheduled Complementary Filter Design for a MEMS Based Attitude and Heading Reference System
This paper describes a robust and simple algorithm for an attitude and heading reference system (AHRS) based on low-cost MEMS inertial and magnetic sensors. The proposed approach relies on a gain-scheduled complementary filter, augmented by an acceleration-based switching architecture to yield robust performance, even when the vehicle is subject to strong accelerations. Experimental results are provided for a road captive test during which the vehicle dynamics are in high-acceleration mode and the performance of the proposed filter is evaluated against the output from a conventional linear complementary filter
Attitude Estimation and Control Using Linear-Like Complementary Filters: Theory and Experiment
This paper proposes new algorithms for attitude estimation and control based
on fused inertial vector measurements using linear complementary filters
principle. First, n-order direct and passive complementary filters combined
with TRIAD algorithm are proposed to give attitude estimation solutions. These
solutions which are efficient with respect to noise include the gyro bias
estimation. Thereafter, the same principle of data fusion is used to address
the problem of attitude tracking based on inertial vector measurements. Thus,
instead of using noisy raw measurements in the control law a new solution of
control that includes a linear-like complementary filter to deal with the noise
is proposed. The stability analysis of the tracking error dynamics based on
LaSalle's invariance theorem proved that almost all trajectories converge
asymptotically to the desired equilibrium. Experimental results, obtained with
DIY Quad equipped with the APM2.6 auto-pilot, show the effectiveness and the
performance of the proposed solutions.Comment: Submitted for Journal publication on March 09, 2015. Partial results
related to this work have been presented in IEEE-ROBIO-201
Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion
Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error)
Inertial measurement techniques for human joints' movement analysis
Abstract. Development and assessment of techniques that allow inertia measurement units consisting of an accelerometer and a gyroscope to be used for monitoring human joints' movements are presented. A new wavelet packet decomposition technique was developed that denoised the accelerometer signals. Investigations on the use of accelerometers to analyse legs’ movements are described
Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter
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
Human Motion Capture Algorithm Based on Inertial Sensors
On the basis of inertial navigation, we conducted a comprehensive analysis of the human body kinematics principle. From the direction of two characteristic parameters, namely, displacement and movement angle, we calculated the attitude of a node during the human motion capture process by combining complementary and Kalman filters. Then, we evaluated the performance of the proposed attitude strategy by selecting different platforms as the validation object. Results show that the proposed strategy for the real-time tracking of the human motion process has higher accuracy than the traditional strategy
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