Endo-VMFuseNet: A Deep Visual-Magnetic Sensor Fusion Approach for Endoscopic Capsule Robots

Date

2018-05-25

Author

Turan, Mehmet
Almalioglu, Yasin
Gilbert, Hunter B.
Sari, Alp Eren
Soylu, Ufuk
Sitti, Metin

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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 sensor fusion for endoscopic capsule robots in the case of asynchronous and asymmetric sensor data without any need of calibration between sensors. The results performed on real pig stomach datasets show that our method achieves high precision for both translational and rotational movements and contains various advantages over traditional sensor fusion techniques.

Subject Keywords

Robot sensing systems, Magnetic separation, Magnetic levitation, Sensor fusion, Magnetic cores, Magnetic resonance imaging

URI

https://hdl.handle.net/11511/68320

Conference Name

IEEE International Conference on Robotics and Automation (ICRA)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

M. Turan, Y. Almalioglu, H. B. Gilbert, A. E. Sari, U. Soylu, and M. Sitti, “Endo-VMFuseNet: A Deep Visual-Magnetic Sensor Fusion Approach for Endoscopic Capsule Robots,” Brisbane, Australia, 2018, p. 5386, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/68320.