Leveraging line features to improve location accuracy of point-based visual-inertial SLAM (VINS) is gaining importance as they provide additional constraint of scene structure regularity, however, real-time performance has not been focused. This paper presents PL-VINS, a real-time optimizationbased monocular VINS method with point and line, developed based on state-of-the-art point-based VINS-Mono [1]. Observe that current works use LSD [2] algorithm to extract lines, however, the LSD is designed for scene shape representation instead of specific pose estimation problem, which becomes the bottleneck for the real-time performance due to its expensive cost. In this work, a modified LSD algorithm is presented by studying hidden parameter tuning and length rejection strategy. The modified LSD can run three times at least as fast as the LSD. Further, by representing a line landmark with Pl ücker coordinate, the line reprojection residual is modeled as midpointto-line distance then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl ücker coordinate. Experiments in public EuRoc benchmark dataset show the location error of our method is down 12-16% compared to VINS-Mono at the same work frequency on a low-