In endoscopy, many applications (e.g., surgical navigation) would benefit from a real-time method that can simultaneously track the endoscope and reconstruct the dense 3D geometry of the observed anatomy from a monocular endoscopic video. To this end, we develop a Simultaneous Localization and Mapping system by combining the learning-based appearance and optimizable geometry priors and factor graph optimization. The appearance and geometry priors are explicitly learned in an end-to-end differentiable training pipeline to master the task of pair-wise image alignment, one of the core components of the SLAM system. In our experiments, the proposed SLAM system is shown to robustly handle the challenges of texture scarceness and illumination variation that are commonly seen in endoscopy. The system generalizes well to unseen endoscopes and subjects and performs favorably compared with a state-of-the-art feature-based SLAM system. The code repository is available at https://github.com/lppllppl920/SAGE-SLAM.git.
@article{arxiv.2202.09487,
title = {SAGE: SLAM with Appearance and Geometry Prior for Endoscopy},
author = {Xingtong Liu and Zhaoshuo Li and Masaru Ishii and Gregory D. Hager and Russell H. Taylor and Mathias Unberath},
journal= {arXiv preprint arXiv:2202.09487},
year = {2022}
}