Abstract—Weak texture andmotion blur are always challenging
problems for visual-inertial odometry (VIO) systems. To improve
accuracy of VIO systems in the challenging scenes, we propose a
point-line-based VIO system with novel feature hybrids and with
novel predicting-matching for long line track. Point-line features
with shorter tracks are categorized into “MSCKF” features and
with longer tracks into “SLAM” features. Especially, “SLAM”
lines are added into the state vector to improve accuracy of the
proposed system. Besides, to ensure the reliability and stability
of detection and tracking of line features, we also propose a new
“Predicting-Matching” line segment tracking method to increase
the track lengths of line segments. Experimental results show that
the proposed method outperforms the state-of-the-art methods of
VINS-Mono [1], PL-VINS [2] and OpenVINS [3]) on both a public
dataset and a collected dataset in terms of accuracy. The collected
dataset is full of extremely weak textures and motion blurs. On this
dataset, the proposed method also obtains better accuracy than
ORB-SLAM3 [4].