  The RGB-D camera maintains a limited range for working and is hard to
accurately measure the depth information in a far distance. Besides, the RGB-D
camera will easily be influenced by strong lighting and other external factors,
which will lead to a poor accuracy on the acquired environmental depth
information. Recently, deep learning technologies have achieved great success
in the visual SLAM area, which can directly learn high-level features from the
visual inputs and improve the estimation accuracy of the depth information.
Therefore, deep learning technologies maintain the potential to extend the
source of the depth information and improve the performance of the SLAM system.
However, the existing deep learning-based methods are mainly supervised and
require a large amount of ground-truth depth data, which is hard to acquire
because of the realistic constraints. In this paper, we first present an
unsupervised learning framework, which not only uses image reconstruction for
supervising but also exploits the pose estimation method to enhance the
supervised signal and add training constraints for the task of monocular depth
and camera motion estimation. Furthermore, we successfully exploit our
unsupervised learning framework to assist the traditional ORB-SLAM system when
the initialization module of ORB-SLAM method could not match enough features.
Qualitative and quantitative experiments have shown that our unsupervised
learning framework performs the depth estimation task comparable to the
supervised methods and outperforms the previous state-of-the-art approach by
$13.5\%$ on KITTI dataset. Besides, our unsupervised learning framework could
significantly accelerate the initialization process of ORB-SLAM system and
effectively improve the accuracy on environmental mapping in strong lighting
and weak texture scenes.
