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Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots


Turan, Mehmet; Ornek, Evin Pinar; Ibrahimli, Nail; Giracoglu, Can; Almalioglu, Yasin; Yanik, Mehmet Fatih; Sitti, Metin (2018). Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots. arXiv 1803.01047, Institute of Neuroinformatics.

Abstract

In the last decade, many medical companies and research groups have tried to convert passive capsule endoscopes as an emerging and minimally invasive diagnostic technology into actively steerable endoscopic capsule robots which will provide more intuitive disease detection, targeted drug delivery and biopsy-like operations in the gastrointestinal(GI) tract. In this study, we introduce a fully unsupervised, real-time odometry and depth learner for monocular endoscopic capsule robots. We establish the supervision by warping view sequences and assigning the re-projection minimization to the loss function, which we adopt in multi-view pose estimation and single-view depth estimation network. Detailed quantitative and qualitative analyses of the proposed framework performed on non-rigidly deformable ex-vivo porcine stomach datasets proves the effectiveness of the method in terms of motion estimation and depth recovery.

Abstract

In the last decade, many medical companies and research groups have tried to convert passive capsule endoscopes as an emerging and minimally invasive diagnostic technology into actively steerable endoscopic capsule robots which will provide more intuitive disease detection, targeted drug delivery and biopsy-like operations in the gastrointestinal(GI) tract. In this study, we introduce a fully unsupervised, real-time odometry and depth learner for monocular endoscopic capsule robots. We establish the supervision by warping view sequences and assigning the re-projection minimization to the loss function, which we adopt in multi-view pose estimation and single-view depth estimation network. Detailed quantitative and qualitative analyses of the proposed framework performed on non-rigidly deformable ex-vivo porcine stomach datasets proves the effectiveness of the method in terms of motion estimation and depth recovery.

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Additional indexing

Item Type:Working Paper
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2018
Deposited On:12 Mar 2019 11:18
Last Modified:15 Jul 2020 07:48
Publisher:Arxiv - Computer Science
Series Name:arXiv
OA Status:Closed
Free access at:Official URL. An embargo period may apply.
Official URL:https://arxiv.org/abs/1803.01047

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