  Ingestible wireless capsule endoscopy is an emerging minimally invasive
diagnostic technology for inspection of the GI tract and diagnosis of a wide
range of diseases and pathologies. Medical device companies and many research
groups have recently made substantial progresses in converting passive capsule
endoscopes to active capsule robots, enabling more accurate, precise, and
intuitive detection of the location and size of the diseased areas. Since a
reliable real time pose estimation functionality is crucial for actively
controlled endoscopic capsule robots, in this study, we propose a monocular
visual odometry (VO) method for endoscopic capsule robot operations. Our method
lies on the application of the deep Recurrent Convolutional Neural Networks
(RCNNs) for the visual odometry task, where Convolutional Neural Networks
(CNNs) and Recurrent Neural Networks (RNNs) are used for the feature extraction
and inference of dynamics across the frames, respectively. Detailed analyses
and evaluations made on a real pig stomach dataset proves that our system
achieves high translational and rotational accuracies for different types of
endoscopic capsule robot trajectories.
