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：作为当前先进立体视觉惯性SLAM框架ORB-SLAM3中的初始化方法，其存在一定局限性。该方法的成功依赖于纯立体SLAM系统的性能，并基于一个基本假设：纯视觉SLAM能够准确估计相机轨迹，而这对于惯性参数估计至关重要。同时，针对ORB-SLAM3进一步改进的初始化方法Stereo-NEC，由于采用关键点跟踪并利用常规对极约束来估计陀螺仪偏差，导致计算耗时较长。为解决现有方法的不足，本文提出一种旨在提升初始化阶段平移精度的新方法。本方法的核心思想是：在固定旋转估计的前提下，通过独立的3自由度光束法平差来优化平移估计，而非采用ORB-SLAM3的6自由度光束法平差。此外，与ORB-SLAM3直接通过立体视觉里程计获取（在挑战性场景中可能效果欠佳）的旋转估计不同，本方法将通过结合IMU测量值与陀螺仪偏差来更新旋转估计。我们在公开基准数据集EuRoC上进行了广泛评估，结果表明本方法在精度方面表现优异。