Integrated navigation algorithm based on vision-inertial extended Kalman filter for low-cost unmanned aerial vehicle

Abstract

In order to solve the problem of low attitude estimation accuracy of low-cost UAV without GNSS (Global Navigation Satellite System) signal, the integrated navigation algorithm was improved. Binocular vision sensor is introduced as the observation sensor, and the position and attitude angle of the image information collected by binocular camera were estimated by the SLAM theory, the position and attitude angle were entered into the EKF algorithm, and the Vision Inertial EKF (VI-EKF) algorithm is established. It realizes the high-precision navigation of low-cost UAV without GNSS signal. The biases of gyroscope and accelerometer are calculated by the relative motion increment between the two key frames of binocular camera and IMU pre-integration data, and these two biases were taken as observations to update IMU bias in the process of EKF algorithm. In this paper, the effectiveness of IMU pre-integration algorithm is verified firstly, and then the VI-EKF navigation algorithm is compared with the pure visual SLAM algorithm through dataset simulation. The simulation results show that the VI-EKF algorithm proposed in this paper has a good performance in the environment without GNSS signal.