Most existing visual-inertial odometry (VIO) initialization methods rely on accurate pre-calibrated extrinsic parameters. However, during long-term use, irreversible structural deformation caused by temperature changes, mechanical squeezing, etc. will cause changes in extrinsic parameters, especially in the rotational part. Existing initialization methods that simultaneously estimate extrinsic parameters suffer from poor robustness, low precision, and long initialization latency due to the need for sufficient translational motion. To address these problems, we propose a novel VIO initialization method, which jointly considers extrinsic orientation and gyroscope bias within the normal epipolar constraints, achieving higher precision and better robustness without delayed rotational calibration. First, a rotation-only constraint is designed for extrinsic orientation and gyroscope bias estimation, which tightly couples gyroscope measurements and visual observations and can be solved in pure-rotation cases. Second, we propose a weighting strategy together with a failure detection strategy to enhance the precision and robustness of the estimator. Finally, we leverage Maximum A Posteriori to refine the results before enough translation parallax comes. Extensive experiments have demonstrated that our method outperforms the state-of-the-art methods in both accuracy and robustness while maintaining competitive efficiency.

翻译：现有的大多数视觉惯性里程计初始化方法依赖于精确预标定的外参。然而，在长期使用过程中，由温度变化、机械挤压等引起的不可逆结构变形会导致外参发生变化，尤其是旋转部分。现有的同时估计外参的初始化方法由于需要足够的平移运动，存在鲁棒性差、精度低和初始化延迟长的问题。为解决这些问题，我们提出了一种新颖的VIO初始化方法，该方法在标准对极约束框架内联合考虑外参定向与陀螺仪偏置，实现了更高的精度和更好的鲁棒性，且无需延迟旋转标定。首先，我们设计了一种仅旋转约束用于外参定向与陀螺仪偏置估计，该约束将陀螺仪测量值与视觉观测紧密耦合，并可在纯旋转场景下求解。其次，我们提出了一种加权策略与失效检测策略，以增强估计器的精度与鲁棒性。最后，在获得足够平移视差之前，我们利用最大后验估计对结果进行优化。大量实验表明，我们的方法在保持竞争力的效率的同时，在精度和鲁棒性方面均优于现有最先进的方法。