  In existing self-supervised depth and ego-motion estimation methods,
ego-motion estimation is usually limited to only leveraging RGB information.
Recently, several methods have been proposed to further improve the accuracy of
self-supervised ego-motion estimation by fusing information from other
modalities, e.g., depth, acceleration, and angular velocity. However, they
rarely focus on how different fusion strategies affect performance. In this
paper, we investigate the effect of different fusion strategies for ego-motion
estimation and propose a new framework for self-supervised learning of depth
and ego-motion estimation, which performs ego-motion estimation by leveraging
RGB and inferred depth information in a Multi-Layer Fusion manner. As a result,
we have achieved state-of-the-art performance among learning-based methods on
the KITTI odometry benchmark. Detailed studies on the design choices of
leveraging inferred depth information and fusion strategies have also been
carried out, which clearly demonstrate the advantages of our proposed
framework.
