Closed-form integration of IMU error state covariance for optimization-based Visual-Inertial State Estimator

In this paper, we propose a closed-form IMU error state covariance integration method for optimization-based visual-inertial state estimator. We derive a closed-form solutions of the IMU error state covariance propagation in pre-integration process, yielding improved accuracy of IMU residual information matrix. Our visual-inertial state estimator is based on a tightly-coupled, sliding-window optimization framework, which jointly estimate the IMU states and landmarks and performing marginalization to limit the computational cost. Finally, the system are validated in park environment dataset, the result shows our proposed method is effective.