UAV Navigation With Monocular Visual Inertial Odometry Under GNSS-Denied Environment

Abstract

In global satellite navigation system (GNSS)-denied environments, unmanned aerial vehicle (UAV) navigation based on visual–inertial odometry (VIO) has been widely studied. However, existing VIO methods still suffer from some practical problems, such as image enhancement oversaturation and unreasonable weighting in backend optimization. Therefore, this article presents monocular VIO with point-line fusion and backend adaptive optimization to improve the positioning accuracy and robustness of UAV navigation systems. In the front end, we proposed an adaptive gamma image correction algorithm for image preprocessing to avoid image oversaturation, which is more conducive to image extraction and matching. Instead of the traditional line segment detector (LSD) line feature extraction algorithm, we employed an improved EDLines algorithm to enhance the efficiency of line feature extraction, better meeting the high dynamic real-time requirements of UAV. In the backend, we proposed a tightly coupled nonlinear adaptive optimization method based on a two-step approach to address the issue of unreasonable static weights. In the first step, we established a factor graph model and performed the first nonlinear optimization based on a priori visual weights. In the second step, we calculated the reprojection error and established a functional model that examines the relationship between the reprojection error and the information matrix. We updated the information matrix using the reprojection error to adaptively adjust the weights of the point features and line features in real time. Finally, we performed a second nonlinear reoptimization. The proposed method was compared with the monocular visual-inertial system (VINS-MONO) (Qin et al. 2018) and point and line features (PL)-VINS (Fu et al. 2020) methods, the experimental results showing that the positioning accuracy of the proposed method on the public EuRoc dataset (Burri et al. 2016) improved by an average of 32.3% compared with the PL-VINS method, and by an average of 33.8% in three real-world scenarios under changing illumination, weak texture, and large-scale complex scenarios. The results demonstrated that the proposed method exhibited better robustness and higher positioning accuracy in various complex environments.