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.

翻译：传感器间的时间错配（时间偏移）在低成本视觉惯性里程计（VIO）系统中普遍存在。此类时间错配会为状态估计引入不一致的约束，导致显著的定位漂移，尤其是在高动态运动场景中。本文聚焦于在线时间标定，以削弱高动态运动VIO中由时间偏移引起的定位漂移。对于时间偏移观测模型，现有方法大多依赖精确的状态估计或稳定的视觉跟踪。对于预测模型，当前方法将时间偏移简化为带有高斯白噪声的常数值。然而，这些理想条件在实际高动态场景中鲜有满足，导致性能不佳。本文引入在线时间偏移建模网络（TON）以增强实时时间标定能力。TON提升了时间偏移观测与预测建模的精度。具体而言，在观测建模方面，我们提出特征速度观测网络，以在不稳定视觉跟踪条件下增强特征速度的计算能力。在预测建模方面，我们提出时间偏移预测网络以学习其演化规律。为凸显本方法的有效性，我们将所提TON分别集成至基于优化和基于滤波的VIO系统中。通过仿真与真实世界实验验证了本方法的增强性能。此外，为助力VIO社区发展，我们将公开方法代码于：https://github.com/Franky-X/FVON-TPN。