FUSION OF OPTICAL FLOW AND INERTIAL SENSORS FOR FOUR-ROTOR ROTORCRAFT STABILIZATION

This paper offers a set of novel navigation techniques that rely on the use of inertial sensors and wide-field optical flow information. The aircraft ground velocity and attitude states are estimated with an Unscented Information Filter (UIF) and are evaluated with respect to two sets of experimental flight data collected from an Unmanned Aerial Vehicle (UAV). Two different formulations are proposed, a full state formulation including velocity and attitude and a simplified formulation which assumes that the lateral and vertical velocity of the aircraft are negligible. An additional state is also considered within each formulation to recover the image distance which can be measured using a laser rangefinder. The results demonstrate that the full state formulation is able to estimate the aircraft ground velocity to within 1.3 m/s of a GPS receiver solution used as reference “truth” and regulate attitude angles within 1.4 degrees standard deviation of error for both sets of flight data.

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A Model-aided Optical Flow/Inertial Sensor Fusion Method for a Quadrotor

Journal of Navigation ◽

10.1017/s0373463316000539 ◽

2016 ◽

Vol 70 (2) ◽

pp. 325-341 ◽

Cited By ~ 7

Author(s):

Pin Lyu ◽

Jizhou Lai ◽

Hugh H.T. Liu ◽

Jianye Liu ◽

Wenjing Chen

Keyword(s):

Optical Flow ◽

Inertial Sensors ◽

Fault Tolerant ◽

Inertial Sensor ◽

Estimation Method ◽

Velocity Estimation ◽

Estimation Accuracy ◽

Sensor Faults ◽

Optical Flow Sensor ◽

Accuracy And Stability

In this paper, a fault-tolerant velocity estimation method is proposed for quadrotors in a GPS denied environment. A novel filter is developed in light of the quadrotor model and measurements from optical flow and inertial sensors. The proposed filter is capable of detecting and isolating the optical flow sensor faults, by which the velocity estimation accuracy and stability will be improved. It is also demonstrated that the wind velocity is observable in the proposed filter. Therefore, the new filter can also be implemented in a windy environment, which is a significant improvement to the previous model-aided inertial sensor estimator. At the end, some simulations are carried out to verify the advantages of our method.

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Combining inertial sensors and optical flow to assess finger movements: Pilot study for telehealth applications

10.1109/embc46164.2021.9629788 ◽

2021 ◽

Author(s):

Katherin Zumaeta ◽

Stefano E. Romero ◽

Estiven Torres ◽

Leslie Urdiales ◽

Andrea Ramirez ◽

...

Keyword(s):

Pilot Study ◽

Optical Flow ◽

Inertial Sensors ◽

Finger Movements

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Mental Models "Midst the Optical Flow

PsycEXTRA Dataset ◽

10.1037/e665412011-336 ◽

1992 ◽

Author(s):

Jeremy M. A. Beer

Keyword(s):

Optical Flow ◽

Mental Models

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Motion correction in PET/CT

Nuklearmedizin ◽

10.1055/s-0038-1625215 ◽

2005 ◽

Vol 44 (S 01) ◽

pp. S46-S50 ◽

Cited By ~ 5

Author(s):

M. Dawood ◽

N. Lang ◽

F. Büther ◽

M. Schäfers ◽

O. Schober ◽

...

Keyword(s):

Optical Flow ◽

Motion Correction ◽

Motion Vector ◽

Emission Tomography ◽

Cardiac Pet ◽

Novel Approach ◽

Positron Emission ◽

Pet Ct ◽

Flow Algorithm ◽

Motion Vector Field

Summary:Motion in PET/CT leads to artifacts in the reconstructed PET images due to the different acquisition times of positron emission tomography and computed tomography. The effect of motion on cardiac PET/CT images is evaluated in this study and a novel approach for motion correction based on optical flow methods is outlined. The Lukas-Kanade optical flow algorithm is used to calculate the motion vector field on both simulated phantom data as well as measured human PET data. The motion of the myocardium is corrected by non-linear registration techniques and results are compared to uncorrected images.

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