When the image texture is simple and relatively indistinct, it is difficult to implement pose estimation based on point features. For this problem, a visual SLAM (simultaneous localization and mapping) algorithm based on point-line features is proposed by using the distributed modular technology. Firstly, the point and line features in the environment captured by the camera are extracted and tracked according to the inter-frame feature matching. Then, the improved NICP (normal iterative closest point) algorithm and the key frame matching strategy are used to build the odometer system. Based on this, the loop detection based on point-line feature dictionary and the graph optimization method of GTSAM (Georgia Tech smoothing and mapping library) are introduced to obtain 3D point cloud map with globally consistent poses. A system framework is developed with robot technology middleware to enhance the scalability and flexibility of the functional modules while improving the real-time performance of the system. The experimental results on the standard datasets and in laboratory scenes verify the feasibility and effectiveness of the proposed method.