  In this work we present a monocular visual odometry (VO) algorithm which
leverages geometry-based methods and deep learning. Most existing VO/SLAM
systems with superior performance are based on geometry and have to be
carefully designed for different application scenarios. Moreover, most
monocular systems suffer from scale-drift issue.Some recent deep learning works
learn VO in an end-to-end manner but the performance of these deep systems is
still not comparable to geometry-based methods. In this work, we revisit the
basics of VO and explore the right way for integrating deep learning with
epipolar geometry and Perspective-n-Point (PnP) method. Specifically, we train
two convolutional neural networks (CNNs) for estimating single-view depths and
two-view optical flows as intermediate outputs. With the deep predictions, we
design a simple but robust frame-to-frame VO algorithm (DF-VO) which
outperforms pure deep learning-based and geometry-based methods. More
importantly, our system does not suffer from the scale-drift issue being aided
by a scale consistent single-view depth CNN. Extensive experiments on KITTI
dataset shows the robustness of our system and a detailed ablation study shows
the effect of different factors in our system.
