UAV Navigation With Monocular Visual Inertial Odometry Under GNSS-Denied Environment
Haolong Luo, Guangyun Li, Danping Zou, Kailin Li, Xueqiang Li, Zidi Yang
Abstract
In GNSS-denied environments, unmanned aerial vehicle (UAV) navigation based on visual inertial odometry has been widely studied. However, existing visual-inertial odometry methods still suffer from some practical problems such as image enhancement oversaturation and unreasonable weighting in backend optimization. Therefore, this paper presents monocular visual-inertial odometry with point-line fusion and backend adaptive optimization to improve the positioning accuracy and robustness of UAV navigation system. In the frontend, 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 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 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 re-optimization. The proposed method was compared with the VINS-MONO [1] and PL-VINS [2] methods, the experimental results showing that the positioning accuracy of the proposed method on the public EuRoc dataset [3] 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.