TY - GEN
T1 - SAGE
T2 - 39th IEEE International Conference on Robotics and Automation, ICRA 2022
AU - Liu, Xingtong
AU - Li, Zhaoshuo
AU - Ishii, Masaru
AU - Hager, Gregory D.
AU - Taylor, Russell H.
AU - Unberath, Mathias
N1 - Funding Information:
This work was supported in part by a fellowship from Intuitive Surgical.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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/lppllpp1920/SAGE-SLAM.git.
AB - 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/lppllpp1920/SAGE-SLAM.git.
UR - http://www.scopus.com/inward/record.url?scp=85136338371&partnerID=8YFLogxK
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U2 - 10.1109/ICRA46639.2022.9812257
DO - 10.1109/ICRA46639.2022.9812257
M3 - Conference contribution
AN - SCOPUS:85136338371
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 5587
EP - 5593
BT - 2022 IEEE International Conference on Robotics and Automation, ICRA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 May 2022 through 27 May 2022
ER -