改进单目视觉惯性初始化：基于无结构的视觉惯性光束法平差
Improving Monocular Visual-Inertial Initialization with Structureless Visual-Inertial Bundle Adjustment
AI总结：
针对单目VIO初始化中结构重建计算量大且无结构方法精度受限的问题，提出无结构视觉惯性光束法平差以优化初始解，在真实数据集上显著提升精度并保持实时性。
摘要：
单目视觉惯性里程计（VIO）凭借传感器套件体积小、功耗低等优势，已广泛应用于各类实时运动跟踪应用。为了成功启动VIO算法，初始化模块至关重要。大多数初始化方法依赖于3D视觉点云的重建。这些方法由于状态向量同时包含运动状态和3D特征点，导致计算成本较高。为解决这一问题，近期一些研究者提出了无结构初始化方法，该方法无需恢复3D结构即可求解初始状态。然而，由于旋转和平移的分离估计以及线性约束，该方法可能牺牲性能。为提高其精度，我们提出了一种新颖的无结构视觉惯性光束法平差方法，以进一步优化先前的无结构解。在真实世界数据集上的大量实验表明，我们的方法在保持实时性能的同时，显著提升了VIO初始化精度。
英文摘要：
Monocular visual inertial odometry (VIO) has facilitated a wide range of real-time motion tracking applications, thanks to the small size of the sensor suite and low power consumption. To successfully bootstrap VIO algorithms, the initialization module is extremely important. Most initialization methods rely on the reconstruction of 3D visual point clouds. These methods suffer from high computational cost as state vector contains both motion states and 3D feature points. To address this issue, some researchers recently proposed a structureless initialization method, which can solve the initial state without recovering 3D structure. However, this method potentially compromises performance due to the decoupled estimation of rotation and translation, as well as linear constraints. To improve its accuracy, we propose novel structureless visual-inertial bundle adjustment to further refine previous structureless solution. Extensive experiments on real-world datasets show our method significantly improves the VIO initialization accuracy, while maintaining real-time performance.
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。
