As the current initialization method in the state-of-the-art Stereo Visual-Inertial SLAM framework, ORB-SLAM3 has limitations. Its success depends on the performance of the pure stereo SLAM system and is based on the underlying assumption that pure visual SLAM can accurately estimate the camera trajectory, which is essential for inertial parameter estimation. Meanwhile, the further improved initialization method for ORB-SLAM3, known as Stereo-NEC, is time-consuming due to applying keypoint tracking to estimate gyroscope bias with normal epipolar constraints. To address the limitations of previous methods, this paper proposes a method aimed at enhancing translation accuracy during the initialization stage. The fundamental concept of our method is to improve the translation estimate with a 3 Degree-of-Freedom (DoF) Bundle Adjustment (BA), independently, while the rotation estimate is fixed, instead of using ORB-SLAM3's 6-DoF BA. Additionally, the rotation estimate will be updated by considering IMU measurements and gyroscope bias, unlike ORB-SLAM3's rotation, which is directly obtained from stereo visual odometry and may yield inferior results when operating in challenging scenarios. We also conduct extensive evaluations on the public benchmark, the EuRoC dataset, demonstrating that our method excels in accuracy.

翻译：作为当前先进的立体视觉-惯性SLAM框架ORB-SLAM3中的初始化方法，其存在一定的局限性。该方法的成功依赖于纯立体SLAM系统的性能，并且基于一个基本假设：纯视觉SLAM能够准确估计相机轨迹，这对于惯性参数估计至关重要。同时，ORB-SLAM3的进一步改进初始化方法——Stereo-NEC，由于应用关键点跟踪并利用常规对极约束来估计陀螺仪偏差，因此非常耗时。为了解决先前方法的局限性，本文提出了一种旨在提升初始化阶段平移精度的方法。我们方法的基本思想是，在固定旋转估计的前提下，通过一个独立的3自由度（DoF）光束法平差（BA）来改进平移估计，而不是使用ORB-SLAM3的6-DoF BA。此外，与ORB-SLAM3直接从立体视觉里程计获取（在挑战性场景中可能效果不佳）的旋转估计不同，我们的旋转估计将通过考虑IMU测量值和陀螺仪偏差进行更新。我们还在公开基准数据集EuRoC上进行了广泛的评估，结果表明我们的方法在精度方面表现优异。