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

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10.1007:s00138-017-0905-8.pdf

Date

2017-12-27

Author

Turan, Mehmet
Pilavci, Yusuf Yigit
Ganiyusufoglu, Ipek
Araujo, Helder
Konukoglu, Ender
Sitti, Metin

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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.

Subject Keywords

Endoscopic capsule robots, 3D map reconstruction, Sparse-then-dense feature tracking

URI

https://hdl.handle.net/11511/52082

Journal

Machine Vision and Applications

DOI

https://doi.org/10.1007/s00138-017-0905-8

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

M. Turan, Y. Y. Pilavci, I. Ganiyusufoglu, H. Araujo, E. Konukoglu, and M. Sitti, “Sparse-then-dense alignment-based 3D map reconstruction method for endoscopic capsule robots,” Machine Vision and Applications, pp. 345–359, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52082.