外观
论文arXiv 2609.211861 个月前
利用线特征和消失点的鲁棒无结构单目视觉惯性初始化
原文链接：Robust Structureless Monocular Visual Inertial Initialization Exploiting Line Features and Vanishing Points（arXiv 2609.21186 · 发布日期：2026-09-18）
Robust Structureless Monocular Visual Inertial Initialization Exploiting Line Features and Vanishing Points
SLIM-init利用二维线特征与消失点约束单目VIO初始化，并在公开基准和退化运动序列上验证。
核心要点
- 针对退化运动下VIO初始化病态、需强激励或昂贵三维结构重建的问题。
- 方法无需显式三维地标重建，直接利用跟踪二维线特征的几何约束。
- 引入线导出的消失点提供旋转约束，并结合线对极与线法向投影残差。
- 实验显示其较现有初始化方法提升准确性和鲁棒性，代码已开源。
验证范围与局限：验证范围为公开基准和具有挑战性的自定义退化运动序列。
英文摘要 (Abstract)
Accurate initialization is essential for reliable visual-inertial odometry (VIO), but it is often ill-conditioned under degenerate motions. Existing methods typically require restrictive excitation motions to ensure sufficient observability or rely on computationally expensive 3D structure reconstruction, limiting efficient and practical deployment. To address these limitations, we propose SLIM-init, a structureless monocular VIO initializer that directly exploits geometric constraints from tracked 2D line features without explicit 3D landmark reconstruction. Specifically, SLIM-init leverages line-derived vanishing points (VPs) as translation-invariant orientation cues to provide robust rotation-only constraints under degenerate scenarios such as low-parallax or translation-dominant motions. It further incorporates a line epipolar residual to constrain translation and a line-normal projection residual to improve the conditioning of linear alignment, enhancing the accuracy and robustness of initial state estimation. Extensive experiments on a public benchmark and challenging custom degenerate-motion sequences demonstrate improved accuracy and robustness over state-of-the-art initialization methods. The source code is available at: https://github.com/cjunwan/SLIM-init.
作者：Choi, Junwan, Woongrae Jo, Dong-Uk Seo, Jinwoo Jeon, Hyun Myung
来源
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