Abstract—Line segment features become popular in SLAM
community. Usually, line-based SLAM systems utilize local
appearance descriptors for line segment tracking. However,
traditional descriptor-based line segment tracking algorithms
suffer from the problem that accuracy and speed cannot be
possessed simultaneously, which affects the performance of
line-based SLAM systems negatively. We propose a novel line
segment tracking method with an IMU-KLT line segment
prediction and a convex geometric distance minimization to
boost line segment tracking performance in both accuracy and
speed. Particularly, the proposed convex geometric distance
minimization uses a `1-norm model to minimize geometric
constraints between predicted line segments and extracted line
segments efficiently. Furthermore, the line segment tracking
is embedded into a VIO system and we adapt it to obtain
more reliable point tracking. Experimental results on public
datasets show that the proposed line segment tracking method
achieves much higher accuracy and much less time cost than
state-of-the-art level, where not only the number of correct
matches increases but also the inlier ratios are increased by
at least 35.1% along with a 3 times faster speed. Besides, the
VIO system combining the proposed line segment tracking is
improved in terms of accuracy.