  The amount of texture can be rich or deficient depending on the objects and
the structures of the building. The conventional mono visual-initial navigation
system (VINS)-based localization techniques perform well in environments where
stable features are guaranteed. However, their performance is not assured in a
changing indoor environment. As a solution to this, we propose Adaptive Line
and point feature-based Visual Inertial Odometry (ALVIO) in this paper. ALVIO
actively exploits the geometrical information of lines that exist in abundance
in an indoor space. By using a strong line tracker and adaptive selection of
feature-based tightly coupled optimization, it is possible to perform robust
localization in a variable texture environment. The structural characteristics
of ALVIO are as follows: First, the proposed optical flow-based line tracker
performs robust line feature tracking and management. By using epipolar
geometry and trigonometry, accurate 3D lines are recovered. These 3D lines are
used to calculate the line re-projection error. Finally, with the
sensitivity-analysis-based adaptive feature selection in the optimization
process, we can estimate the pose robustly in various indoor environments. We
validate the performance of our system on public datasets and compare it
against other state-of the-art algorithms (S-MSKCF, VINS-Mono). In the proposed
algorithm based on point and line feature selection, translation RMSE increased
by 16.06% compared to VINS-Mono, while total optimization time decreased by up
to 49.31%. Through this, we proved that it is a useful algorithm as a real-time
pose estimation algorithm.
