解决视觉惯性导航系统的时间偏移问题，实现在线自动校准。
Universal Online Temporal Calibration for Optimization-based Visual-Inertial Navigation Systems
- 将时间偏移作为状态变量融入优化模型，同步估计姿态与时间差。
- 在真实和仿真数据上均实现更精准的偏移估计与更快收敛速度。
- 兼容多种优化框架，适合需要高精度实时导航的场景。
6自由度（6DoF）运动估计算法结合视觉与惯性传感器在众多实际应用中日益重要。然而，精确校准两类传感器间的时间偏移是实现准确、鲁棒跟踪的前提。为此，我们提出一种通用的在线时间校准策略，适用于基于优化的视觉-惯性导航系统。技术上，我们将时间偏移量 td 作为优化残差模型中的状态参数，利用 td、角速度和线速度将惯性测量单元（IMU）状态对齐至对应图像时间戳，使时间偏差可在追踪过程中与其他状态一同优化。由于该方法仅调整残差模型结构，可适配不同前端追踪框架的优化系统。我们在 EuRoC 数据集和仿真数据上进行了评估，大量实验表明，本方法在噪声传感器数据下仍能实现更精确的时间偏移估计和更快的收敛速度。
原文摘要 · Abstract (English)
6-Degree of Freedom (6DoF) motion estimation with a combination of visual and inertial sensors is a growing area with numerous real-world applications. However, precise calibration of the time offset between these two sensor types is a prerequisite for accurate and robust tracking. To address this, we propose a universal online temporal calibration strategy for optimization-based visual-inertial navigation systems. Technically, we incorporate the time offset td as a state parameter in the optimization residual model to align the IMU state to the corresponding image timestamp using td, angular velocity and translational velocity. This allows the temporal misalignment td to be optimized alongside other tracking states during the process. As our method only modifies the structure of the residual model, it can be applied to various optimization-based frameworks with different tracking frontends. We evaluate our calibration method with both EuRoC and simulation data and extensive experiments demonstrate that our approach provides more accurate time offset estimation and faster convergence, particularly in the presence of noisy sensor data.
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