Design and experiment of multi rotor UAV control system with spectral remote sensing load

Pei, Xinbiao; Wu, Helong; Ma, Pengtao; Yan, Yi; Hao, Peng; Bai, Yue

doi:10.3788/irla201948.0118003

Cited by 1 publication

(1 citation statement)

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“…VI-EKF navigation algorithm can make up for the shortcomings of vision and inertial navigation, and carry out highprecision navigation [1][2][3] .Inertial navigation has the characteristics of error accumulation over time [4] ,however, visual navigation depends on the quality of image information, illumination and high-speed motion will cause image blur and missing feature points, which will affect the navigation accuracy [5][6][7][8][9] .In order to solve the shortcomings of the two navigation methods, the navigation method combining vision and inertial navigation has become a research hotspot in recent years [10][11][12][13][14][15][16][17] .The combination of vision sensor and inertial navigation system can provide complementarity in frequency response and noise characteristics, and can ensure the position and attitude estimation accuracy of the system in the case of poor GNSS signal. GNSS signal can provide longitude, latitude, speed and height information, provide accurate speed and position information for UAV, and improve the navigation accuracy of UAV.…”

Section: Introductionmentioning

confidence: 99%

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

Wu¹,

Sun²,

Hu³

et al. 2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

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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.

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