OneSLAM to map them all: a generalized approach to SLAM for monocular endoscopic imaging based on tracking any point

Timo Teufel, Hongchao Shu, Roger D. Soberanis-Mukul, Jan Emily Mangulabnan, Manish Sahu, S. Swaroop Vedula, Masaru Ishii, Gregory D. Hager, Russell H. Taylor, Mathias Unberath

Abstract: Monocular SLAM algorithms are the key enabling technology for image-based surgical navigation systems for endoscopic procedures. Due to the visual feature scarcity and unique lighting conditions encountered in endoscopy, classical SLAM approaches perform inconsistently. Many of the recent approaches to endoscopic SLAM rely on deep learning models. They show promising results when optimized on singular domains such as arthroscopy, sinus endoscopy, colonoscopy or laparoscopy, but are limited by an inability to generalize to different domains without retraining.