Attitude estimation of aircraft based on quaternion SRCKF-SLAM algorithm

Abstract

In complex terrain environments such as high mountains and hills, traditional agricultural machinery cannot accurately complete tasks such as crop management and harvesting. This paper used plant protection drones as carriers to study the observation content of crops during their navigation process. Aiming at the low accuracy of the traditional quaternion cubature Kalman filtering algorithm for the attitude estimation of the carrier nonlinear state model, a quaternon-based square root cubature Kalman filtering algorithm was proposed in this paper. The algorithm takes the attitude quaternion error and the gyro drift error as the state quantity, and measures the attitude quaternion of SINS/SLAM navigation. The square root cubature Kalman filter algorithm is used for pose estimation, which not only solves the standardization problem of traditional quaternion, but also reduces the state dimension and complexity of the square root UKF algorithm of traditional quaternion, and improves the numerical stability. Compared with the quaternion SRUKF and quaternion SRCDKF algorithm, the simulation results showed that the new algorithm estimated the error mean values of the roll angle, pitch angle and runt angle, which are 0.05?, 0.08?, and 0.03?, respectively. The error is the smallest, and algorithm accuracy is about 30% higher than the quaternion SRUKF-SLAM algorithm, and it has high filtering accuracy and numerical stability, and the best time-consuming performanc.