  In this paper, a novel solution is introduced for visual Simultaneous
Localization and Mapping (vSLAM) that is built up of Deep Learning components.
The proposed architecture is a highly modular framework in which each component
offers state of the art results in their respective fields of vision-based deep
learning solutions. The paper shows that with the synergic integration of these
individual building blocks, a functioning and efficient all-through deep neural
(ATDN) vSLAM system can be created. The Embedding Distance Loss function is
introduced and using it the ATDN architecture is trained. The resulting system
managed to achieve 4.4% translation and 0.0176 deg/m rotational error on a
subset of the KITTI dataset. The proposed architecture can be used for
efficient and low-latency autonomous driving (AD) aiding database creation as
well as a basis for autonomous vehicle (AV) control.
