  Although a wide variety of deep neural networks for robust Visual Odometry
(VO) can be found in the literature, they are still unable to solve the drift
problem in long-term robot navigation. Thus, this paper aims to propose novel
deep end-to-end networks for long-term 6-DoF VO task. It mainly fuses relative
and global networks based on Recurrent Convolutional Neural Networks (RCNNs) to
improve the monocular localization accuracy. Indeed, the relative sub-networks
are implemented to smooth the VO trajectory, while global subnetworks are
designed to avoid drift problem. All the parameters are jointly optimized using
Cross Transformation Constraints (CTC), which represents temporal geometric
consistency of the consecutive frames, and Mean Square Error (MSE) between the
predicted pose and ground truth. The experimental results on both indoor and
outdoor datasets show that our method outperforms other state-of-the-art
learning-based VO methods in terms of pose accuracy.
