{"ID":5937033,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-09T14:33:30.924921582Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05162","arxiv_id":"2607.05162","title":"Geometry-Aware Visual Odometry for Bronchoscopic Navigation via High-Gain Observer Fusion","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.","short_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 wi...","url_abs":"https://arxiv.org/abs/2607.05162","url_pdf":"https://arxiv.org/pdf/2607.05162v1","authors":"[\"Mohammadreza Kasaei\",\"Francis Xiatian Zhang\",\"Feng Li\",\"Farshid Alambeigi\",\"Kevin Dhaliwal\",\"Mohsen Khadem\"]","published":"2026-07-06T14:46:33Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}