Sparse-then-dense alignment-based 3D map reconstruction method for endoscopic capsule robots

Turan, Mehmet and Pilavci, Yusuf Yigit and Ganiyusufoğlu, İpek and Araujo, Helder and Konukoglu, Ender and Sitti, Metin (2018) Sparse-then-dense alignment-based 3D map reconstruction method for endoscopic capsule robots. Machine Vision and Applications, 29 (2). pp. 345-359. ISSN 0932-8092 (Print) 1432-1769 (Online)

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Despite significant progress achieved in the last decade to convert passive capsule endoscopes to actively controllable robots, robotic capsule endoscopy still has some challenges. In particular, a fully dense three-dimensional (3D) map reconstruction of the explored organ remains an unsolved problem. Such a dense map would help doctors detect the locations and sizes of the diseased areas more reliably, resulting in more accurate diagnoses. In this study, we propose a comprehensive medical 3D reconstruction method for endoscopic capsule robots, which is built in a modular fashion including preprocessing, keyframe selection, sparse-then-dense alignment-based pose estimation, bundle fusion, and shading-based 3D reconstruction. A detailed quantitative analysis is performed using a non-rigid esophagus gastroduodenoscopy simulator, four different endoscopic cameras, a magnetically activated soft capsule robot, a sub-millimeter precise optical motion tracker, and a fine-scale 3D optical scanner, whereas qualitative ex-vivo experiments are performed on a porcine pig stomach. To the best of our knowledge, this study is the first complete endoscopic 3D map reconstruction approach containing all of the necessary functionalities for a therapeutically relevant 3D map reconstruction.
Item Type: Article
Uncontrolled Keywords: 3D map reconstruction; Endoscopic capsule robots; Sparse-then-dense feature tracking
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences
Depositing User: İpek Ganiyusufoğlu
Date Deposited: 22 May 2023 12:08
Last Modified: 22 May 2023 12:08

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