  We present a robust deep learning based 6 degrees-of-freedom (DoF)
localization system for endoscopic capsule robots. Our system mainly focuses on
localization of endoscopic capsule robots inside the GI tract using only visual
information captured by a mono camera integrated to the robot. The proposed
system is a 23-layer deep convolutional neural network (CNN) that is capable to
estimate the pose of the robot in real time using a standard CPU. The dataset
for the evaluation of the system was recorded inside a surgical human stomach
model with realistic surface texture, softness, and surface liquid properties
so that the pre-trained CNN architecture can be transferred confidently into a
real endoscopic scenario. An average error of 7:1% and 3:4% for translation and
rotation has been obtained, respectively. The results accomplished from the
experiments demonstrate that a CNN pre-trained with raw 2D endoscopic images
performs accurately inside the GI tract and is robust to various challenges
posed by reflection distortions, lens imperfections, vignetting, noise, motion
blur, low resolution, and lack of unique landmarks to track.
