Attitude Determination and Attitude Estimation in Aircraft and Spacecraft Navigation. A survey

Dhahbane, Djamel; Nemra, Abdelkrim; Sakhi, Samir

doi:10.1109/ihsh51661.2021.9378714

Cited by 8 publications

(2 citation statements)

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“…Attitude estimation is the procedure of estimating orientation of the vehicle with respect to a reference frame using sensory measurements such as inertial and attitude sensors. Although least square error (LSE) and maximum likelihood (ML) approaches can be classified as early attitude estimation methods, model-based Bayesian approaches are most common and precise approaches can be found in Dhahbane et al. (2021) .…”

Section: Introductionmentioning

confidence: 99%

“…3389/frobt.2023.1090174 sensors. Although least square error (LSE) and maximum likelihood (ML) approaches can be classified as early attitude estimation methods, model-based Bayesian approaches are most common and precise approaches can be found in Dhahbane et al (2021). Modelbased approaches normally employ vehicle kinematics and/or dynamics to provide a prediction from the orientation, and the predicted attitude is updated through the sensory measurements (Sadeghzadeh- Nokhodberiz and Poshtan, 2016;Ozaki and Kuroda, 2021).…”

Section: Introductionmentioning

confidence: 99%

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

Sadeghzadeh-Nokhodberiz

Iranshahi

Montazeri

2023

Front. Robot. AI

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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.

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Section: Introductionmentioning

confidence: 99%