We propose an accurate and robust initialization approach for stereo visual-inertial SLAM systems. Unlike the current state-of-the-art method, which heavily relies on the accuracy of a pure visual SLAM system to estimate inertial variables without updating camera poses, potentially compromising accuracy and robustness, our approach offers a different solution. We realize the crucial impact of precise gyroscope bias estimation on rotation accuracy. This, in turn, affects trajectory accuracy due to the accumulation of translation errors. To address this, we first independently estimate the gyroscope bias and use it to formulate a maximum a posteriori problem for further refinement. After this refinement, we proceed to update the rotation estimation by performing IMU integration with gyroscope bias removed from gyroscope measurements. We then leverage robust and accurate rotation estimates to enhance translation estimation via 3-DoF bundle adjustment. Moreover, we introduce a novel approach for determining the success of the initialization by evaluating the residual of the normal epipolar constraint. Extensive evaluations on the EuRoC dataset illustrate that our method excels in accuracy and robustness. It outperforms ORB-SLAM3, the current leading stereo visual-inertial initialization method, in terms of absolute trajectory error and relative rotation error, while maintaining competitive computational speed. Notably, even with 5 keyframes for initialization, our method consistently surpasses the state-of-the-art approach using 10 keyframes in rotation accuracy.

翻译：我们提出了一种用于立体视觉-惯性SLAM系统的精确且鲁棒的初始化方法。与当前最先进方法不同——后者严重依赖纯视觉SLAM系统的准确性来估计惯性变量而不更新相机位姿，可能损害精度和鲁棒性——我们的方法提供了不同的解决方案。我们认识到精确的陀螺仪偏置估计对旋转准确性具有关键影响，而旋转误差会通过平移误差的积累进一步影响轨迹精度。为解决这一问题，我们首先独立估计陀螺仪偏置，并将其用于构建最大后验问题进行进一步优化。在此优化后，我们通过移除陀螺仪测量中的偏置进行IMU积分来更新旋转估计。随后，利用鲁棒且精确的旋转估计通过三维自由度光束法平差增强平移估计。此外，我们引入了一种通过评估法线极线约束残差来判断初始化是否成功的新方法。在EuRoC数据集上的大量评估表明，我们的方法在精度和鲁棒性方面表现卓越。在绝对轨迹误差和相对旋转误差指标上，它优于当前领先的立体视觉-惯性初始化方法ORB-SLAM3，同时保持了有竞争力的计算速度。值得注意的是，即使仅使用5个关键帧进行初始化，我们的方法在旋转精度上始终超越使用10个关键帧的最先进方法。