Adaptive Covariance Matrix based on Blur Evaluation for Visual-Inertial Navigation

The covariance matrix in the current mainstream visual-inertial navigation system is artificially set and the weight of visual information cannot be adjusted by different blur degree, which cause the poor accuracy and robustness in the whole system. In order to solve this problem, this paper proposed a navigation scheme based on adaptive covariance matrix. This method used the Laplacian operator to evaluate the blur degree of image by a score. And then the visual covariance matrix is adjusted according to the different scores, which can adjust the weight in the fusion system according to the image quality. By doing this, the algorithm can improve the accuracy of the system. The simulation results show that the proposed method can effectively improve the system accuracy. Compared with the traditional method, the proposed algorithm has stronger robustness when motion blur occur.