Vision-based particle filtering for quad-copter attitude estimation using multirate delayed measurements

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https://doi.org/10.3389/frobt.2023.1090174
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Available under license: CC BY: Creative Commons Attribution 4.0 International License

Keywords

multi-rate sensor fusion, attitude estimation, gyroscope (gyro), camera, quad-copter, particle filtering, UAV

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Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Vision-based particle filtering for quad-copter attitude estimation using multirate delayed measurements. / Sadeghzadeh-Nokhodberiz, Nargess; Iranshahi, Mohammad; Montazeri, Allahyar.
In: Frontiers in Robotics and AI, Vol. 10, 1090174, 30.05.2023.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Bibtex

@article{fb0c7fefd6a246aaaa3399f09567f45a,

title = "Vision-based particle filtering for quad-copter attitude estimation using multirate delayed measurements",

abstract = "In this paper, the problem of attitude estimation of a quad-copter system equipped with a multi-rate camera and gyroscope sensors is addressed through extension of a sampling importance re-sampling (SIR) particle filter (PF). Attitude measurement sensors, such as cameras, usually suffer from a slow sampling rate and processing time delay compared to inertial sensors, such as gyroscopes. A discretized attitude kinematics in Euler angles is employed where the gyroscope noisy measurements are considered the model input, leading to a stochastic uncertain system model. Then, a multi-rate delayed PF is proposed so that when no camera measurement is available, the sampling part is performed only. In this case, the delayed camera measurements are used for weight computation and re-sampling. Finally, the efficiency of the proposed method is demonstrated through both numerical simulation and experimental work on the DJI Tello quad-copter system. The images captured by the camera are processed using the ORB feature extraction method and the homography method in Python-OpenCV, which is used to calculate the rotation matrix from the Tello{\textquoteright}s image frames.",

keywords = "multi-rate sensor fusion, attitude estimation, gyroscope (gyro), camera, quad-copter, particle filtering, UAV",

author = "Nargess Sadeghzadeh-Nokhodberiz and Mohammad Iranshahi and Allahyar Montazeri",

year = "2023",

month = may,

day = "30",

doi = "10.3389/frobt.2023.1090174",

language = "English",

volume = "10",

journal = "Frontiers in Robotics and AI",

issn = "2296-9144",

publisher = "Frontiers Media S.A.",

}

RIS

TY - JOUR

T1 - Vision-based particle filtering for quad-copter attitude estimation using multirate delayed measurements

AU - Sadeghzadeh-Nokhodberiz, Nargess

AU - Iranshahi, Mohammad

AU - Montazeri, Allahyar

PY - 2023/5/30

Y1 - 2023/5/30

N2 - In this paper, the problem of attitude estimation of a quad-copter system equipped with a multi-rate camera and gyroscope sensors is addressed through extension of a sampling importance re-sampling (SIR) particle filter (PF). Attitude measurement sensors, such as cameras, usually suffer from a slow sampling rate and processing time delay compared to inertial sensors, such as gyroscopes. A discretized attitude kinematics in Euler angles is employed where the gyroscope noisy measurements are considered the model input, leading to a stochastic uncertain system model. Then, a multi-rate delayed PF is proposed so that when no camera measurement is available, the sampling part is performed only. In this case, the delayed camera measurements are used for weight computation and re-sampling. Finally, the efficiency of the proposed method is demonstrated through both numerical simulation and experimental work on the DJI Tello quad-copter system. The images captured by the camera are processed using the ORB feature extraction method and the homography method in Python-OpenCV, which is used to calculate the rotation matrix from the Tello’s image frames.

AB - In this paper, the problem of attitude estimation of a quad-copter system equipped with a multi-rate camera and gyroscope sensors is addressed through extension of a sampling importance re-sampling (SIR) particle filter (PF). Attitude measurement sensors, such as cameras, usually suffer from a slow sampling rate and processing time delay compared to inertial sensors, such as gyroscopes. A discretized attitude kinematics in Euler angles is employed where the gyroscope noisy measurements are considered the model input, leading to a stochastic uncertain system model. Then, a multi-rate delayed PF is proposed so that when no camera measurement is available, the sampling part is performed only. In this case, the delayed camera measurements are used for weight computation and re-sampling. Finally, the efficiency of the proposed method is demonstrated through both numerical simulation and experimental work on the DJI Tello quad-copter system. The images captured by the camera are processed using the ORB feature extraction method and the homography method in Python-OpenCV, which is used to calculate the rotation matrix from the Tello’s image frames.

KW - multi-rate sensor fusion

KW - attitude estimation

KW - gyroscope (gyro)

KW - camera

KW - quad-copter

KW - particle filtering

KW - UAV

U2 - 10.3389/frobt.2023.1090174

DO - 10.3389/frobt.2023.1090174

M3 - Journal article

C2 - 37323641

VL - 10

JO - Frontiers in Robotics and AI

JF - Frontiers in Robotics and AI

SN - 2296-9144

M1 - 1090174

ER -

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