  Visual odometry algorithms tend to degrade when facing low-textured scenes
-from e.g. human-made environments-, where it is often difficult to find a
sufficient number of point features. Alternative geometrical visual cues, such
as lines, which can often be found within these scenarios, can become
particularly useful. Moreover, these scenarios typically present structural
regularities, such as parallelism or orthogonality, and hold the Manhattan
World assumption. Under these premises, in this work, we introduce MSC-VO, an
RGB-D -based visual odometry approach that combines both point and line
features and leverages, if exist, those structural regularities and the
Manhattan axes of the scene. Within our approach, these structural constraints
are initially used to estimate accurately the 3D position of the extracted
lines. These constraints are also combined next with the estimated Manhattan
axes and the reprojection errors of points and lines to refine the camera pose
by means of local map optimization. Such a combination enables our approach to
operate even in the absence of the aforementioned constraints, allowing the
method to work for a wider variety of scenarios. Furthermore, we propose a
novel multi-view Manhattan axes estimation procedure that mainly relies on line
features. MSC-VO is assessed using several public datasets, outperforming other
state-of-the-art solutions, and comparing favourably even with some SLAM
methods.
