Six Degree-of-Freedom Localization of Endoscopic Capsule Robots using Recurrent Neural Networks embedded into a Convolutional Neural Network
2017
Article
pi
Since its development, ingestible wireless endoscopy is considered to be a painless diagnostic method to detect a number of diseases inside GI tract. Medical related engineering companies have made significant improvements in this technology in last decade; however, some major limitations still residue. Localization of the next generation steerable endoscopic capsule robot in six degreeof-freedom (DoF) and active motion control are some of these limitations. The significance of localization capability concerns with the doctors correct diagnosis of the disease area. This paper presents a very robust 6-DoF localization method based on supervised training of an architecture consisting of recurrent networks (RNN) embedded into a convolutional neural network (CNN) to make use of both just-in-moment information obtained by CNN and correlative information across frames obtained by RNN. To our knowledge, our idea of embedding RNNs into a CNN architecture is for the first time proposed in literature. The experimental results show that the proposed RNN-in-CNN architecture performs very well for endoscopic capsule robot localization in cases vignetting, reflection distortions, noise, sudden camera movements and lack of distinguishable features.
Author(s): | Turan, Mehmet and Abdullah, Abdullah and Jamiruddin, Redhwan and Araujo, Helder and Konukoglu, Ender and Sitti, Metin |
Journal: | arXiv preprint arXiv:1705.06196 |
Year: | 2017 |
Month: | May |
Day: | 17 |
Department(s): | Physical Intelligence |
Bibtex Type: | Article (article) |
DOI: | arXiv:1705.06196 |
BibTex @article{turan2017six, title = {Six Degree-of-Freedom Localization of Endoscopic Capsule Robots using Recurrent Neural Networks embedded into a Convolutional Neural Network}, author = {Turan, Mehmet and Abdullah, Abdullah and Jamiruddin, Redhwan and Araujo, Helder and Konukoglu, Ender and Sitti, Metin}, journal = {arXiv preprint arXiv:1705.06196}, month = may, year = {2017}, doi = {arXiv:1705.06196}, month_numeric = {5} } |