Analysis of modeling nasal narrow space based on visual slam

Abstract

Abstract. Now, surgeries are becoming more stable, safe, efficient and low-cost. During the surgical treatment of nasal diseases, surgical robotic robotics can help operate accurately and reduce the discomfort after surgery. However, due to the internal space of the nasal cavity being relatively narrow, it is difficult for the nasal surgical robot to contain multiple vision sensors and the monocular camera could not get information about the depth of the 3D objects in the scene, so the existing surgical robots cannot accomplish the three-dimensional modeling about the internal space of nasal cavity well. In practice, doctors still have to analyze pictures from the robotic, which may decrease the efficiency of the surgery and increase the risk to patients. This article designed a SLAM algorithm framework based on a depth estimation network, it can simulate the internal structure of the nasal cavity more accurately through pictures, which come from monocular endoscopic on surgical robotic. The insights gained in this study verify that the method of image segmentation can also make the depth representation of the nasal internal space more accurate and this method may help robots realize their self-position in the narrow area of the nasal cavity, which lays the foundations for the development of fully autonomous surgical robots.