Vision-based particle filtering for quad-copter attitude estimation using multirate delayed measurements.
Vision-based particle filtering for quad-copter attitude estimation using multirate delayed measurements.
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DOI：
10.3389/frobt.2023.1090174
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发表时间：
2023
影响因子：
3.4
通讯作者：
Montazeri, Allahyar
中科院分区：
文献类型：
--
作者：
Sadeghzadeh-Nokhodberiz, Nargess;Iranshahi, Mohammad;Montazeri, Allahyar
在本文中，通过扩展采样重要性重采样（SIR）粒子滤波器（PF）来解决配备多速率相机和陀螺仪传感器的四轴飞行器系统的姿态估计问题。与陀螺仪等惯性传感器相比，相机等姿态测量传感器通常采样率低且处理时间延迟。采用欧拉角的离散姿态运动学，其中陀螺仪噪声测量被视为模型输入，从而产生随机不确定系统模型。然后，提出了多速率延迟PF，以便当没有相机测量可用时，仅执行采样部分。在这种情况下，延迟的相机测量用于权重计算和重新采样。最后，通过 DJI Tello 四轴飞行器系统上的数值模拟和实验工作证明了该方法的有效性。相机捕获的图像使用Python-OpenCV中的ORB特征提取方法和单应性方法进行处理，用于从Tello的图像帧计算旋转矩阵。
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.