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Ingestible wireless capsule endoscopy is an emerging minimally invasivediagnostic technology for inspection of the GI tract and diagnosis of a widerange of diseases and pathologies. Medical device companies and many researchgroups have recently made substantial progresses in converting passive capsuleendoscopes to active capsule robots, enabling more accurate, precise, andintuitive detection of the location and size of the diseased areas. Since areliable real time pose estimation functionality is crucial for activelycontrolled endoscopic capsule robots, in this study, we propose a monocularvisual odometry (VO) method for endoscopic capsule robot operations. Our methodlies 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 extractionand inference of dynamics across the frames, respectively. Detailed analysesand evaluations made on a real pig stomach dataset proves that our systemachieves high translational and rotational accuracies for different types ofendoscopic capsule robot trajectories.

Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots