Aiming at the error accumulation problem ofthe visualinertial odometry algorithm based on multistate constraint kalman filter (MSCKF) in the augmentation process of the camera state equation, a multimode augmentation method of camera state equation is proposed. In this method, the stability of the visual feature tracking state is strictly judged firstly; then, two methods are automatically selected to augment the camera state equation, the first method optimally solves the relative pose parameters of the camera based on visual image information,another method is based on the recursion results of inertial measurement unit (IMU) state combining the cameraIMU external parameters to initialize the camera pose parameters for new image frame. As a result, the error accumulation problem of IMU under the stable feature tracking state is solved. In the experiment part, the performance of the proposed algorithm is verified utilizing the EuRoC dataset and practical application dataset. The experiment results show that the improved MSCKF algorithm can effectively avoid the error accumulation of IMU under the stable feature tracking state,further fuse the complementary advantages of both visual and inertial systems,and improve the localization & orientation precision and stability of the carrier.