  In this paper, we consider the problems in the practical application of
visual simultaneous localization and mapping (SLAM). With the popularization
and application of the technology in wide scope, the practicability of SLAM
system has become a new hot topic after the accuracy and robustness, e.g., how
to keep the stability of the system and achieve accurate pose estimation in the
low-texture and dynamic environment, and how to improve the universality and
real-time performance of the system in the real scenes, etc. This paper
proposes a real-time stereo indirect visual SLAM system, PLD-SLAM, which
combines point and line features, and avoid the impact of dynamic objects in
highly dynamic environments. We also present a novel global gray similarity
(GGS) algorithm to achieve reasonable keyframe selection and efficient loop
closure detection (LCD). Benefiting from the GGS, PLD-SLAM can realize
real-time accurate pose estimation in most real scenes without pre-training and
loading a huge feature dictionary model. To verify the performance of the
proposed system, we compare it with existing state-of-the-art (SOTA) methods on
the public datasets KITTI, EuRoC MAV, and the indoor stereo datasets provided
by us, etc. The experiments show that the PLD-SLAM has better real-time
performance while ensuring stability and accuracy in most scenarios. In
addition, through the analysis of the experimental results of the GGS, we can
find it has excellent performance in the keyframe selection and LCD.
