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In the last decade, researchers and medical device companies have made majoradvances towards transforming passive capsule endoscopes into active medicalrobots. One of the major challenges is to endow capsule robots with accurateperception of the environment inside the human body, which will providenecessary information and enable improved medical procedures. We extend thesuccess of deep learning approaches from various research fields to the problemof uncalibrated, asynchronous, and asymmetric sensor fusion for endoscopiccapsule robots. The results performed on real pig stomach datasets show thatour method achieves sub-millimeter precision for both translational androtational movements and contains various advantages over traditional sensorfusion techniques.

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