To address the problem of insufficient image feature information and the accumulated error of visual-inertial odometry (VIO) in an outdoor complex environment, this paper proposes a VIO and global navigation satellite system (GNSS) algorithm based on a combination of point and line features. First, the paper designs a minimization strategy using geometric constraints, which performs constraint matching between the front and back frames on the extracted line features. In addition, an improved VIO-GNSS loose coupling model is built, in which the accumulated error in VIO pose estimation is suppressed using the global observation value of the GNSS. The experimental results in the KIITI data set and measured data set show that the proposed algorithm can maintain good accuracy and robustness compared with several similar algorithms in large outdoor complex environments with good/rejected/interrupted GNSS signals and meet the real-time requirements of driving positioning.