立体视觉惯性初始化的平移精度研究进展
Advancements in Translation Accuracy for Stereo Visual-Inertial Initialization
AI总结：
针对ORB-SLAM3及改进方法Stereo-NEC的初始化局限，提出固定旋转、用3自由度BA优化平移并结合IMU更新旋转的方法，在EuRoC数据集上验证了精度优势。
摘要：
作为当前最先进的立体视觉惯性SLAM框架中的初始化方法，ORB-SLAM3存在局限性。其成功依赖于纯立体SLAM系统的性能，且基于纯视觉SLAM能够准确估计相机轨迹这一基本假设，而该轨迹估计对惯性参数估计至关重要。同时，针对ORB-SLAM3的进一步改进初始化方法Stereo-NEC，由于采用关键点跟踪结合常规对极约束来估计陀螺仪偏置，存在耗时较长的问题。为解决现有方法的局限性，本文提出一种旨在提升初始化阶段平移精度的方法。该方法的核心思路是：固定旋转估计值，独立通过3自由度（DoF）光束法平差（BA）优化平移估计，而非采用ORB-SLAM3的6自由度BA。此外，与ORB-SLAM3直接从立体视觉里程计获取旋转估计（在挑战性场景下可能效果不佳）不同，本方法会结合IMU测量值与陀螺仪偏置更新旋转估计。我们还在公开基准数据集EuRoC上开展了大量评估，结果表明所提方法在精度方面表现优异。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
