2014
DOI: 10.3182/20140824-6-za-1003.01173
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Experimental Comparison of Sensor Fusion Algorithms for Attitude Estimation

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Cited by 56 publications
(46 citation statements)
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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%
“…[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%
“…The task of analyzing the parameters of those three algorithms can be found in [2]. The EKF algorithm produces marginally better results regarding the attitude estimation accuracy but it requires higher execution times.…”
Section: Problem Formulationmentioning
confidence: 99%
“…In this section, we will analyze the performance of three pose estimation fusion algorithms of IMU which include CF filter (proposed by Mahony), 10 Gradient Descent algorithm (proposed by Madgwick), 11 and EKF (proposed by Cavallo). 12 Mahony algorithm treats the pose estimation problem as a deterministic observation problem. The authors thought the definition of a Direct CF and a Passive CF arrive to a reformulation of the CF, named Explicit CF, in terms of direct vectorial measurements, such as gravitational or magnetic field directions obtained from an IMU.…”
Section: Imu Pose Estimationmentioning
confidence: 99%
“…And then we analyze three pose estimation fusion algorithms of IMU. By comparing with those methods which named CF algorithm, 10 gradient decrease algorithm, 11 and standard EKF fusion algorithm, 12 finally we take the standard EKF as the sensor fusion algorithm due to its good estimation result. Based on this, in this article, a new IMU-based iterative pose compensation algorithm is proposed based on the accuracy pose measurement of IMU.…”
Section: Introductionmentioning
confidence: 99%
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