A novel SLAM method for laparoscopic scene reconstruction with feature patch tracking

As one of the Minimally Invasive Surgery (MIS), laparoscopy is widely applied in gastrointestinal surgery. Benefits from its minimally invasive procedure and fast postoperative rehabilitation for patients. However, it is a huge challenge for surgeons to operate slender surgical instruments under a limited field of view taken by laparoscopy which requires the surgeon to have rich surgical experience. In laparoscopic surgeries, an accurate 3D reconstruction mode with internal anatomy structure and laparoscopic positions in the abdominal cavity can effectively help the surgeon to reduce the dependence on surgical experience. In addition, reconstructing the structure of the surgical scene is also a key step for data registration in surgical navigation and augmented reality surgery. In this paper, a novel Simultaneous Localization and Mapping (SLAM) method based on feature patch tracking by Kernel Correlation Filter (KCF) is proposed for 3D dense point clouds reconstruction, which is called KCF-SLAM. It reconstructs the laparoscopic surgery scene accurately and densely under stereo laparoscopic conditions. The proposed method is validated on a public in-vivo data set captured by a stereo laparoscope. Compared with the feature-based SLAM, the proposed method can work efficiently when the image texture is missing or insignificant. The results suggest that the proposed method can reconstruct the dense point cloud of laparoscopic scene stably and accurately.