  For robust visual-inertial SLAM in perceptually-challenging indoor
environments,recent studies exploit line features to extract descriptive
information about scene structure to deal with the degeneracy of point
features. But existing point-line-based SLAM methods mainly use Pl\"ucker
matrix or orthogonal representation to represent a line, which needs to
calculate at least four variables to determine a line. Given the numerous line
features to determine in each frame, the overly flexible line representation
increases the computation burden and comprises the accuracy of the results. In
this paper, we propose inverse depth representation for a line, which models
each extracted line feature using only two variables, i.e., the inverse depths
of the two ending points. It exploits the fact that the projected line's pixel
coordinates on the image plane are rather accurate, which partially restrict
the line. Using this compact line presentation, Inverse Depth Line SLAM (IDLS)
is proposed to track the line features in SLAM in an accurate and efficient
way. A robust line triangulation method and a novel line re-projection error
model are introduced. And a two-step optimization method is proposed to firstly
determine the lines and then to estimate the camera poses in each frame. IDLS
is extensively evaluated in multiple perceptually-challenging datasets. The
results show it is more accurate, robust, and needs lower computational
overhead than the current state-of-the-art of point-line-based SLAM methods.
