A lumen-adapted navigation scheme with spatial awareness from monocular vision for autonomous robotic endoscopy

Lumen is a common anatomical condition in endoscopic therapy or diagnostics. The navigation of autonomous robotic endoscopy usually involves vision techniques such as detection of lumen center or anatomically specific contour features. However, these methods may fail to achieve smooth interventions and result in large conveying force without spatial awareness of the tissue state. In this paper, a novel navigation pipeline based on a spatial-aware monocular vision is proposed. The spatial awareness pipeline starts with a data-driven depth estimation technique for reconstructing real-time approximate tissue surface. The spatial shape of the lumen described by the skeleton is then extracted using digital topology. We modify the skeleton to get a smooth pathway, and design an adaptive autonomous control strategy using geometric information from the pathway. We conduct experiments on a colon phantom and ex vivo pig intestines. We test turning performance of several bending segments with different angles in phantom, as well as the overall performance of a long-range intervention task in the phantom and pig intestines. The results show our navigation scheme achieve smoother intervention with lower conveying force. The proposed navigation method with spatial awareness can effectively improve the fluency of autonomous robotic endoscopy.