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Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots




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.

Author(s): Mehmet Turan, Evin Pinar Ornek, Nail Ibrahimli, Can Giracoglu, Yasin Almalioglu, Mehmet Fatih Yanik, Metin Sitti
Journal: ArXiv e-prints
Year: 2018
Month: March
Day: 2

Department(s): Physical Intelligence
Bibtex Type: Article (article)

Eprint: 1803.01047


  title = {Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots},
  author = {},
  journal = {ArXiv e-prints},
  month = mar,
  year = {2018},
  eprint = {1803.01047},
  month_numeric = {3}