提出新方法提升单目视觉惯性初始化精度，不依赖3D结构重建。
Improving Monocular Visual-Inertial Initialization with Structureless Visual-Inertial Bundle Adjustment
- 基于无结构的视觉惯性联合优化，避免重建3D点云
- 在真实数据集上显著提升初始化精度，保持实时性
- 适合对初始化准确性要求高的嵌入式运动追踪场景
单目视觉惯性里程计（VIO）因传感器体积小、功耗低，广泛应用于实时运动追踪。成功启动VIO算法的关键在于初始化模块。现有方法多依赖三维视觉点云重建，但状态向量包含运动状态和三维特征点，计算开销大。为此，有研究提出无结构初始化方法，可在不恢复三维结构的情况下求解初始状态。然而，该方法因旋转与平移解耦估计及线性约束，可能影响性能。为此，本文提出新型无结构视觉惯性捆绑调整方法，进一步优化先前无结构解。大量真实数据集实验表明，该方法显著提升初始化精度，同时保持实时性。
原文摘要 · Abstract (English)
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.
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