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Endo-VMFuseNet: Deep Visual-Magnetic Sensor Fusion Approach for Uncalibrated, Unsynchronized and Asymmetric Endoscopic Capsule Robot Localization Data

2017

Article

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In the last decade, researchers and medical device companies have made major advances towards transforming passive capsule endoscopes into active medical robots. One of the major challenges is to endow capsule robots with accurate perception of the environment inside the human body, which will provide necessary information and enable improved medical procedures. We extend the success of deep learning approaches from various research fields to the problem of uncalibrated, asynchronous, and asymmetric sensor fusion for endoscopic capsule robots. The results performed on real pig stomach datasets show that our method achieves sub-millimeter precision for both translational and rotational movements and contains various advantages over traditional sensor fusion techniques.

Author(s): Turan, M. and Almalioglu, Y. and Gilbert, H. and Eren Sari, A. and Soylu, U. and Sitti, M.
Journal: ArXiv e-prints
Year: 2017
Month: September
Day: 22

Department(s): Physical Intelligence
Research Project(s): Pill-sized endoscopic swallowable robots
Bibtex Type: Article (article)

URL: https://arxiv.org/abs/1709.06041v2

BibTex

@article{2017arXiv170906041T,
  title = {Endo-VMFuseNet: Deep Visual-Magnetic Sensor Fusion Approach for Uncalibrated, Unsynchronized and Asymmetric Endoscopic Capsule Robot Localization Data},
  author = {Turan, M. and Almalioglu, Y. and Gilbert, H. and Eren Sari, A. and Soylu, U. and Sitti, M.},
  journal = {ArXiv e-prints},
  month = sep,
  year = {2017},
  url = {https://arxiv.org/abs/1709.06041v2},
  month_numeric = {9}
}