利用气道管腔消失点提升内窥镜视觉里程计稳定性
Geometry-Aware Visual Odometry for Bronchoscopic Navigation via High-Gain Observer Fusion
- 通过检测气道管腔的消失点生成3D射线，融合得到稳定前向朝向
- 相比最优基线方法，轨迹误差降低超50%，相对位姿误差最低
- 适合资源受限场景下需高鲁棒性导航的支气管介入手术
支气管导航对肺部干预至关重要，但现有平台严重依赖术前CT或外部传感器，限制了其在重症监护和资源匮乏环境中的应用。纯视觉导航具备可扩展性，但传统视觉里程计（VO）在纹理贫乏的气道图像、镜面反射及管状解剖结构的消失点奇异点下表现不佳，导致频繁跟踪失败与漂移。本文提出一种几何感知的视觉里程计框架，显式利用气道管腔的消失点线索。检测到的管腔被反投影至3D射线，加权融合后获得即使在视差缺失时仍稳定的前向朝向。该朝向结合基于逼近感的速度估计，通过定制高增益观测器与噪声VO输出融合，强制执行气道跟随先验并抑制漂移。我们在机械通气的人体离体肺上，以电磁追踪为真值进行了验证。相比当前最优方案（ORB-SLAM2、LoFTR-VO、DPVO），本方法绝对轨迹误差降低超过50%，所有测试序列中相对位姿误差最低。
原文摘要 · Abstract (English)
Navigational bronchoscopy is critical for pulmonary interventions, yet current platforms depend heavily on pre-operative CT or external sensors, limiting their use in critical care and resource-constrained settings. Vision-only navigation offers a scalable alternative, but conventional visual odometry (VO) struggles with texture-poor airway images, specularities, and the vanishing-point singularities of tubular anatomy, leading to frequent tracking failures and drift. We present a geometry-aware VO framework that explicitly leverages vanishing-point cues from airway lumens. Detected lumens are back-projected to 3D rays, whose weighted fusion yields a stable forward heading even when parallax cues are absent. This heading, together with looming-based velocity estimates, is fused with noisy VO outputs using a bespoke high-gain observer that enforces airway-following priors and rejects drift. We validate the method on ex-vivo mechanically ventilated human lungs with electromagnetic tracking ground truth. Compared to state-of-the-art pipelines (ORB-SLAM2, LoFTR-VO, DPVO), our approach reduces absolute trajectory error by more than 50% and achieves the lowest relative pose error across all test sequences.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。