  Traditional monocular Visual Simultaneous Localization and Mapping (vSLAM)
systems can be divided into three categories: those that use features, those
that rely on the image itself, and hybrid models. In the case of feature-based
methods, new research has evolved to incorporate more information from their
environment using geometric primitives beyond points, such as lines and planes.
This is because in many environments, which are man-made environments,
characterized as Manhattan world, geometric primitives such as lines and planes
occupy most of the space in the environment. The exploitation of these schemes
can lead to the introduction of algorithms capable of optimizing the trajectory
of a Visual SLAM system and also helping to construct an exuberant map. Thus,
we present a real-time monocular Visual SLAM system that incorporates real-time
methods for line and VP extraction, as well as two strategies that exploit
vanishing points to estimate the robot's translation and improve its
rotation.Particularly, we build on ORB-SLAM2, which is considered the current
state-of-the-art solution in terms of both accuracy and efficiency, and extend
its formulation to handle lines and VPs to create two strategies the first
optimize the rotation and the second refine the translation part from the known
rotation. First, we extract VPs using a real-time method and use them for a
global rotation optimization strategy. Second, we present a translation
estimation method that takes advantage of last-stage rotation optimization to
model a linear system. Finally, we evaluate our system on the TUM RGB-D
benchmark and demonstrate that the proposed system achieves state-of-the-art
results and runs in real time, and its performance remains close to the
original ORB-SLAM2 system
