Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots

Autor:	Turan, Mehmet, Ornek, Evin Pinar, Ibrahimli, Nail, Giracoglu, Can, Almalioglu, Yasin, Yanik, Mehmet Fatih, Sitti, Metin
Rok vydání:	2018
Předmět:	Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	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. Comment: submitted to IROS 2018
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1803.01047 Zobrazit plný text záznamu View this record from Arxiv