  In this paper, we develop a robust, efficient visual SLAM system that
utilizes spatial inhibition of low threshold, baseline lines, and closed-loop
keyframe features. Using ORB-SLAM2, our methods include stereo matching, frame
tracking, local bundle adjustment, and line and point global bundle adjustment.
In particular, we contribute re-projection in line with the baseline. Fusing
lines in the system consume colossal time, and we reduce the time from
distributing points to utilizing spatial suppression of feature points. In
addition, low threshold key points can be more effective in dealing with low
textures. In order to overcome Tracking keyframe redundant problems, an
efficient and robust closed-loop tracking key frame is proposed. The proposed
SLAM has been extensively tested in KITTI and EuRoC datasets, demonstrating
that the proposed system is superior to state-of-the-art methods in various
scenarios.
