Geometry-Aware Visual Odometry for Bronchoscopic Navigation via High-Gain Observer Fusion
2026-07-06 • Robotics
Robotics
AI summaryⓘ
The authors developed a new way to help doctors navigate inside lungs using only camera images during bronchoscopy, without relying on CT scans or extra sensors. They improved how the system understands directions by using the shape of the airway tubes, especially the vanishing points seen in images. Their method combines this idea with traditional motion tracking to reduce errors and avoid getting lost. They tested it on real human lungs and showed it works better than existing methods by a big margin.
navigational bronchoscopyvisual odometryvanishing pointairway lumen3D back-projectionpulmonary interventiontrajectory errorrelative pose errorelectromagnetic trackingORB-SLAM2
Authors
Mohammadreza Kasaei, Francis Xiatian Zhang, Feng Li, Farshid Alambeigi, Kevin Dhaliwal, Mohsen Khadem
Abstract
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.