Application of SLAM in endoscopic imaging

In recent years, there have been advancements in endoscopic applications that aim to improve surgical accuracy and outcomes. One particular area of focus has been the integration of Simultaneous Localization and Mapping (SLAM) techniques. This paper introduces SLAM methodologies specifically designed for endoscopic scenarios. The study introduces a SLAM system that combines the UNet architecture with ORB SLAM2 to enhance instrument tracking. A vSLAM method is proposed for endoscopic capsule robots. The goal is to improve the reconstruction of flexible environments inside the body in three dimensions. The paper also emphasizes the significance of camera positioning and imaging during procedures. It highlights the process involved in converting points into 2D image points, which is crucial for successful outcomes. Furthermore, the research explores the integration of localization systems for small-scale robotic capsules. This system offers a level of precision. Aims to optimize minimally invasive surgeries by minimizing complications and enhancing overall surgical outcomes. Advanced mathematical formulations are presented in detail to explain how the SLAM system functions and operates. The final part of the research delves into visualization and 3D reconstruction. It investigates the use of Generative Adversarial Network (GAN) frameworks to enhance the precision of depth maps.