  Endoscopic surgery relies on two-dimensional views, posing challenges for
surgeons in depth perception and instrument manipulation. While Simultaneous
Localization and Mapping (SLAM) has emerged as a promising solution to address
these limitations, its implementation in endoscopic procedures presents
significant challenges due to hardware limitations, such as the use of a
monocular camera and the absence of odometry sensors. This study presents a
robust deep learning-based SLAM approach that combines state-of-the-art and
newly developed models. It consists of three main parts: the Monocular Pose
Estimation Module that introduces a novel unsupervised method based on the
CycleGAN architecture, the Monocular Depth Estimation Module that leverages the
novel Zoe architecture, and the 3D Reconstruction Module which uses information
from the previous models to create a coherent surgical map. The performance of
the procedure was rigorously evaluated using three publicly available datasets
(Hamlyn, EndoSLAM, and SCARED) and benchmarked against two state-of-the-art
methods, EndoSFMLearner and EndoDepth. The integration of Zoe in the MDEM
demonstrated superior performance compared to state-of-the-art depth estimation
algorithms in endoscopy, whereas the novel approach in the MPEM exhibited
competitive performance and the lowest inference time. The results showcase the
robustness of our approach in laparoscopy, gastroscopy, and colonoscopy, three
different scenarios in endoscopic surgery. The proposed SLAM approach has the
potential to improve the accuracy and efficiency of endoscopic procedures by
providing surgeons with enhanced depth perception and 3D reconstruction
capabilities.
