  In this work, we propose a novel deep online correction (DOC) framework for
monocular visual odometry. The whole pipeline has two stages: First, depth maps
and initial poses are obtained from convolutional neural networks (CNNs)
trained in self-supervised manners. Second, the poses predicted by CNNs are
further improved by minimizing photometric errors via gradient updates of poses
during inference phases. The benefits of our proposed method are twofold: 1)
Different from online-learning methods, DOC does not need to calculate gradient
propagation for parameters of CNNs. Thus, it saves more computation resources
during inference phases. 2) Unlike hybrid methods that combine CNNs with
traditional methods, DOC fully relies on deep learning (DL) frameworks. Though
without complex back-end optimization modules, our method achieves outstanding
performance with relative transform error (RTE) = 2.0% on KITTI Odometry
benchmark for Seq. 09, which outperforms traditional monocular VO frameworks
and is comparable to hybrid methods.
