Attitude and gyro bias estimation for a flying UAV

Metni, Najib; Pflimlin, Jean Michel; Hamel, Tarek; Souères, Philippe

doi:10.1109/iros.2005.1544997

Cited by 45 publications

(30 citation statements)

References 9 publications

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“…where  is the cut-off frequency and  is damping ratio which is fixed to 0.707 to provide a good transient response [8,14].…”

Section: Adaptive Complementary Kalman Filtermentioning

confidence: 99%

“…where ω is the cut-off frequency and ζ is damping ratio which is fixed to 0.707 to provide a good transient response [8,14]. The complementary filter using fixed cut-off frequency is difficult to satisfy acceptable performance in dynamic condition because attitude error caused by vision data disturbance is added to the filter.…”

Section: Fuzzy Logic For Adaptive Complementary Filtermentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Complementary Filter For Gyroscope/Vision Integrated Attitude Estimation

Park¹,

Kang²,

Hwang³

et al. 2016

International Journal of Aeronautical and Space Sciences

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An attitude estimation algorithm which integrates gyroscope and vision measurements using an adaptive complementary filter is proposed in this paper. In order to make the filter more tolerant to vision measurement fault and more robust to system dynamics, fuzzy interpolator is applied. For recognizing the dynamic condition of the system and vision measurement fault, the cut-off frequency of the complementary filter is determined adaptively by using the fuzzy logic with designed membership functions. The performance of the proposed algorithm is evaluated by experiments and it is confirmed that proposed algorithm works well in the static or dynamic condition.

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“…where  is the cut-off frequency and  is damping ratio which is fixed to 0.707 to provide a good transient response [8,14].…”

Section: Adaptive Complementary Kalman Filtermentioning

confidence: 99%

Section: Fuzzy Logic For Adaptive Complementary Filtermentioning

confidence: 99%

An Adaptive Complementary Filter For Gyroscope/Vision Integrated Attitude Estimation

Park¹,

Kang²,

Hwang³

et al. 2016

International Journal of Aeronautical and Space Sciences

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“…where ω b is estimated from the IMU data [26], and Q is a symmetric positive definite matrix depending on the size of C S and on η. In details, by denoting with (α e , α a ) the spherical coordinates of ν, where α e is the elevation angle and α a is the azimuth angle, a rotation matrix with ν in the z-axis assuming no rotation around ν to the inertial frame I can be defined as…”

Section: Image Point Kinematicsmentioning

confidence: 99%

“…By multiplying both side of (22) for R P and substituting (26), the control problem can be rewritten in a partitioned form in P as follows…”

Section: B Hovering Controlmentioning

confidence: 99%

Wall inspection control of a VTOL unmanned aerial vehicle based on a stereo optical flow

Lippiello

Siciliano

2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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An autonomous wall inspection control based on a stereo optical flow, suitable for unmanned aerial vehicles endowed with a stereo vision system, is proposed in this paper. The inspection task consists of simultaneously controlling the inspection velocity along the surface, the relative yaw angle between the vehicle and the observed plane, as well as the orthogonal distance. A virtual spherical camera is considered at the center of gravity of the vehicle. Then, a stereo optical flow, as if it had been acquired by the virtual camera, is generated from the visual data provided by the stereo vision system. The 3D visual measurements are also employed to estimate the relative position and orientation of the observed plane. Hence, the absolute vehicle velocity is estimated by using a robust translational average optical flow by integrating the total stereo flow. Finally, an inspection control and a hovering control are proposed. The effectiveness of the described approach has been demonstrated with a dynamic simulation in an environment composed of two adjacent walls.

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“…By integrating optical flow over an aperture, in this case a solid angle on the sphere, we obtain scaled information on the actual velocity of the vehicle. First, assume that the target plane is textured, the normal direction η is known and the available data areṗ, R and where R and are estimated from the IMU data (Metni et al 2005). The average optical flow is obtained by integrating the observed optical flow over a solid angle W 2 of the sphere around the pole normal to the target plane (Fig.…”

Section: Assumptionmentioning

confidence: 99%

A terrain-following control approach for a VTOL Unmanned Aerial Vehicle using average optical flow

et al. 2010

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This paper presents a nonlinear controller for terrain following of a vertical take-off and landing vehicle (VTOL). The VTOL vehicle is assumed to be a rigid body, equipped with a minimum sensor suite (camera, IMU and barometric altimeter) maneuvering over a textured rough terrain made of a concatenation of planar surfaces. Assuming that the forward velocity is separately regulated to a desired value, the proposed control approach ensures terrain following and guarantees that the vehicle does not collide with the ground during the task. The proposed control acquires an optical flow from multiple spatially separate observation points, typically obtained via multiple cameras or non collinear directions in a unique camera. The proposed control algorithm has been tested extensively in simulation and then implemented on a quadrotor UAV to demonstrate the performance of the closed-loop system. Electronic supplementary materialThe online version of this article (

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Attitude and gyro bias estimation for a flying UAV

Cited by 45 publications

References 9 publications

An Adaptive Complementary Filter For Gyroscope/Vision Integrated Attitude Estimation

An Adaptive Complementary Filter For Gyroscope/Vision Integrated Attitude Estimation

Wall inspection control of a VTOL unmanned aerial vehicle based on a stereo optical flow

A terrain-following control approach for a VTOL Unmanned Aerial Vehicle using average optical flow

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