  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.
