Most current visual simultaneous localization and mapping (SLAM) solutions are based on the extraction of feature points for pose estimation, which still presents significant limitations in weakly textured environments. To address this, line features were introduced into the SLAM system to ensure stable operation in such low-texture scenes. However, current point-line fusion based visual SLAM schemes were often challenged by insufficient real-time performance and accuracy. Therefore, an improved visual-inertial SLAM algorithm based on point-line feature fusion was proposed.In the front-end of the algorithm, FAST (features from accelerated segment test) corners were employed as the feature point extraction method. The ELSED (enhanced line segment detection) algorithm was enhanced through short-segment merging and gradient threshold parameter adjustment. Furthermore, the quadtree-based method for uniform feature point distribution was extended to both points and lines, resulting in an improved point-line feature extraction algorithm. This improvement was designed to reduce the impact of high-texture regions and uneven feature distribution on system accuracy. For tracking both point and line features, an improved optical flow method was utilized. The initial value for the optical flow calculation was provided by the pose information from the inertial measurement unit (IMU) and the known depth of feature points, which replaces the traditional image pyramid iteration process, thereby saving computational resources and meeting the real-time requirements of the system.Finally, the proposed system was experimentally compared with leading open-source solutions in real-world scenarios to validate its real-time performance and accuracy. The experimental results demonstrate that the proposed algorithm can provide a highly robust localization solution for robots in applications such as industrial inspection and warehouse logistics, indicating significant potential for industrial application.