Dynamic Bronchial Environment Reconstruction for Robotic Lung Invasive Surgery

SLAM (Simultaneous Localization and Mapping) has a wide application prospect in navigation of autonomous endoscopic minimally invasive surgery. However, traditional methods like ORB-SLAM2 often struggle in dynamic environments and exhibit significant modeling inaccuracies, particularly in bronchial scenes where respiratory motion induces continuous deformation of the airway structure. To address this challenge, we propose a novel monocular SLAM-based framework for dynamic bronchial environment reconstruction tailored to robotic lung invasive surgery. Firstly, a pseudo-static processing method that builds map sequences at identical respiratory phases across cycles was built. Furthermore, we present a voxel model optimization technique using curvature-consistent graph interpolation to refine the bronchial lumen surface, eliminating pores and redundancies in the voxel map. Experiments conducted on real patient demonstrate that our method has good accuracy and robustness. Compared to existing state-of-the-art methods, our framework achieves superior reconstruction completeness, showing strong potential for clinical use in robotic bronchoscopy. Note to Practitioners—This paper was motivated by the problem of coping with dynamic environments for robotic bronchoscopy. Existing SLAM approaches generally fail to detect and segment dynamic objects in bronchial environment. This paper presents a novel approach for reconstructing dynamic bronchial environments using monocular SLAM. While this paper presents promising results, it is essential to evaluate the technology in clinical scenarios to assess its safety and efficacy. We acknowledge limitations in map quality due to noise and errors. In future research, we will focus on increasing the accuracy of the map.