  Temporal misalignment (time offset) between sensors is common in low cost
visual-inertial odometry (VIO) systems. Such temporal misalignment introduces
inconsistent constraints for state estimation, leading to a significant
positioning drift especially in high dynamic motion scenarios. In this article,
we focus on online temporal calibration to reduce the positioning drift caused
by the time offset for high dynamic motion VIO. For the time offset observation
model, most existing methods rely on accurate state estimation or stable visual
tracking. For the prediction model, current methods oversimplify the time
offset as a constant value with white Gaussian noise. However, these ideal
conditions are seldom satisfied in real high dynamic scenarios, resulting in
the poor performance. In this paper, we introduce online time offset modeling
networks (TON) to enhance real-time temporal calibration. TON improves the
accuracy of time offset observation and prediction modeling. Specifically, for
observation modeling, we propose feature velocity observation networks to
enhance velocity computation for features in unstable visual tracking
conditions. For prediction modeling, we present time offset prediction networks
to learn its evolution pattern. To highlight the effectiveness of our method,
we integrate the proposed TON into both optimization-based and filter-based VIO
systems. Simulation and real-world experiments are conducted to demonstrate the
enhanced performance of our approach. Additionally, to contribute to the VIO
community, we will open-source the code of our method on:
https://github.com/Franky-X/FVON-TPN.
