Sadeghzadeh-Nokhodberiz Nargess, Iranshahi Mohammad, Montazeri Allahyar
Electrical and Computer Engineering Department, Qom University of Technology, Qom, Iran.
Engineering Department, Lancaster University, Lancaster, United Kingdom.
Front Robot AI. 2023 May 30;10:1090174. doi: 10.3389/frobt.2023.1090174. eCollection 2023.
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.
本文通过扩展采样重要性重采样（SIR）粒子滤波器（PF），解决了配备多速率相机和陀螺仪传感器的四旋翼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速率较低且存在处理时间延迟。采用欧拉角离散化姿态运动学，将陀螺仪的噪声测量值视为模型输入，从而得到一个随机不确定系统模型。然后，提出了一种多速率延迟PF，使得在没有相机测量值时，仅执行采样部分。在这种情况下，延迟的相机测量值用于权重计算和重采样。最后，通过在大疆Tello四旋翼系统上的数值模拟和实验工作，证明了所提方法的有效性。相机拍摄的图像使用Python-OpenCV中的ORB特征提取方法和单应性方法进行处理，该方法用于从Tello的图像帧计算旋转矩阵。
