BodySLAM: A Generalized Monocular Visual SLAM Framework for Surgical Applications

TL;DR

BodySLAM introduces a deep learning-based monocular SLAM framework tailored for endoscopic surgery, combining novel pose estimation, depth prediction, and 3D mapping to improve surgical navigation without additional hardware.

Contribution

It presents a comprehensive MVSLAM system with novel components like CycleVO and integrates state-of-the-art depth estimation, addressing hardware limitations in endoscopic procedures.

Findings

CycleVO achieved low inference time and robust generalization.

Zoe outperformed existing depth estimation algorithms.

BodySLAM demonstrated strong performance across diverse endoscopic datasets.

Abstract

Endoscopic surgery relies on two-dimensional views, posing challenges for surgeons in depth perception and instrument manipulation. While Monocular Visual Simultaneous Localization and Mapping (MVSLAM) has emerged as a promising solution, its implementation in endoscopic procedures faces significant challenges due to hardware limitations, such as the use of a monocular camera and the absence of odometry sensors. This study presents BodySLAM, a robust deep learning-based MVSLAM approach that addresses these challenges through three key components: CycleVO, a novel unsupervised monocular pose estimation module; the integration of the state-of-the-art Zoe architecture for monocular depth estimation; and a 3D reconstruction module creating a coherent surgical map. The approach is rigorously evaluated using three publicly available datasets (Hamlyn, EndoSLAM, and SCARED) spanning laparoscopy, gastroscopy, and colonoscopy scenarios, and benchmarked against four state-of-the-art methods. Results demonstrate that CycleVO exhibited competitive performance with the lowest inference time among pose estimation methods, while maintaining robust generalization capabilities, whereas Zoe significantly outperformed existing algorithms for depth estimation in endoscopy. BodySLAM's strong performance across diverse endoscopic scenarios demonstrates its potential as a viable MVSLAM solution for endoscopic applications.