  Accurate relative pose is one of the key components in visual odometry (VO)
and simultaneous localization and mapping (SLAM). Recently, the self-supervised
learning framework that jointly optimizes the relative pose and target image
depth has attracted the attention of the community. Previous works rely on the
photometric error generated from depths and poses between adjacent frames,
which contains large systematic error under realistic scenes due to reflective
surfaces and occlusions. In this paper, we bridge the gap between geometric
loss and photometric loss by introducing the matching loss constrained by
epipolar geometry in a self-supervised framework. Evaluated on the KITTI
dataset, our method outperforms the state-of-the-art unsupervised ego-motion
estimation methods by a large margin. The code and data are available at
https://github.com/hlzz/DeepMatchVO.
