  We present a novel end-to-end visual odometry architecture with guided
feature selection based on deep convolutional recurrent neural networks.
Different from current monocular visual odometry methods, our approach is
established on the intuition that features contribute discriminately to
different motion patterns. Specifically, we propose a dual-branch recurrent
network to learn the rotation and translation separately by leveraging current
Convolutional Neural Network (CNN) for feature representation and Recurrent
Neural Network (RNN) for image sequence reasoning. To enhance the ability of
feature selection, we further introduce an effective context-aware guidance
mechanism to force each branch to distill related information for specific
motion pattern explicitly. Experiments demonstrate that on the prevalent KITTI
and ICL_NUIM benchmarks, our method outperforms current state-of-the-art model-
and learning-based methods for both decoupled and joint camera pose recovery.
