  In feature-based simultaneous localization and mapping (SLAM), line features
complement the sparsity of point features, making it possible to map the
surrounding environment structure. Existing approaches utilizing line features
have primarily employed a measurement model that uses line re-projection.
However, the direction vectors used in the 3D line mapping process cannot be
corrected because the line measurement model employs only the lines' normal
vectors in the Pl\"{u}cker coordinate. As a result, problems like degeneracy
that occur during the 3D line mapping process cannot be solved. To tackle the
problem, this paper presents a UV-SLAM, which is an unconstrained line-based
SLAM using vanishing points for structural mapping. This paper focuses on using
structural regularities without any constraints, such as the Manhattan world
assumption. For this, we use the vanishing points that can be obtained from the
line features. The difference between the vanishing point observation
calculated through line features in the image and the vanishing point
estimation calculated through the direction vector is defined as a residual and
added to the cost function of optimization-based SLAM. Furthermore, through
Fisher information matrix rank analysis, we prove that vanishing point
measurements guarantee a unique mapping solution. Finally, we demonstrate that
the localization accuracy and mapping quality are improved compared to the
state-of-the-art algorithms using public datasets.

Robotics