In this paper, we present a deep learning-based approach to
monocular visual odometry. We propose a LCGR(Local Convolution
and Global RNN) module which utilizes several independent
3D convolution layers to filter noise from features
extracted by FlowNet, as well as to model local information,
and a Bi-ConvLSTM layer to model time series and capture
global information. In addition, our network jointly predicts
optical flow as an auxiliary task by measuring photometric
consistency in a self-supervised way to help the encoder for
better motion feature extraction. In order to alleviate the effects
of non-Lambertian surfaces and dynamical objects in
the scene, a confidence mask layer is estimated and epipolar
constraint is added to the training process. Experiment results
indicate competitive performance of the proposed framework
to the state-of-art methods.