Abstract: Aiming at solving the problem of timestamp misalignment of dynamic exposure monocular visual-inertial system (VINS), a timestamp delay estimation method was proposed. First, different factors, which lead to the timestamp delay, such as sensor exposure time, internal sensor latency, filtering and data transmission, were analyzed. With the above analysis, the dynamic exposure timestamp delay model was established. Second, based on the B-spline curve fitting and cross-correlation, a rough estimation of the time delay was obtained. Then, the objective function was constructed and optimized by nonlinear optimization method to obtain the accurate estimation of the timestamp delays corresponding to different exposure times. Finally, the parameters of the dynamic exposure timestamp delay model were obtained by fitting the estimated results to the proposed model with least squares. The real physical experimental results show that the proposed dynamic exposure timestamp delay model is consistent with the actual physical hardware time delay characteristic, and the estimation accuracy of visual-inertial odometry is improved after compensating the timestamp delay with the proposed model, which can provide more accurate data for vision-based localization, navigation, mapping and path planning.