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.

本研究解决了内窥镜手术中因依赖二维视图而导致的深度感知和器械操作困难的问题。通过结合深度学习算法，提出了一种创新的单目视觉SLAM方法，包含单目位姿估计、深度估计以及三维重建模块。研究结果证明该方法在不同内窥镜手术场景中展现出了优越的性能，能够显著提升外科手术的准确性和效率。

BodySLAM：用于外科应用的通用单目视觉SLAM框架