幾何学認識に基づく気管支内視鏡ナビゲーションのための高ゲインオブザーバ融合によるビジュアルオドメトリ
Geometry-Aware Visual Odometry for Bronchoscopic Navigation via High-Gain Observer Fusion
気管支内視鏡ナビゲーションにおいて、テクスチャの乏しい気道画像や管腔構造の特異点に頑健な、消失点キューを活用した幾何学認識ビジュアルオドメトリ手法を提案し、実肺データで誤差を50%以上削減した。
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著者: Mohammadreza Kasaei, Francis Xiatian Zhang, Feng Li, Farshid Alambeigi, Kevin Dhaliwal, Mohsen Khadem
分類: cs.RO
原文アブストラクト
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.
