  We proposed an end-to-end deep learning-based simultaneous localization and
mapping (SLAM) system following conventional visual odometry (VO) pipelines.
The proposed method completes the SLAM framework by including tracking,
mapping, and sequential optimization networks while training them in an
unsupervised manner. Together with the camera pose and depth map, we estimated
the observational uncertainty to make our system robust to noises such as
dynamic objects. We evaluated our method using public indoor and outdoor
datasets. The experiment demonstrated that our method works well in tracking
and mapping tasks and performs comparably with other learning-based VO
approaches. Notably, the proposed uncertainty modeling and sequential training
yielded improved generality in a variety of environments.
