Abstract: In visual SLAM (simultaneous localization and mapping) algorithm with fusion of point and line features, new errors are introduced due to the poor accuracy of line feature matching. The accumulation of point and line feature errors leads to the failure of data association. To solve the problem, a line feature matching method based on line point invariants is proposed, which encodes the local geometric relationship between the line segment and two adjacent feature points. Line matching process is completed directly by the existing feature points, which can improve the accuracy and efficiency of line matching effectively. In addition, weights are used in the fusion process of point and line features. When constructing the error function, the weights of point and line features are reasonably distributed according to the richness of scene features. Experiments on the TUM indoor dataset and KITTI road dataset show that compared with the existing point line SLAM system, the proposed point-line SLAM system can improve the accuracy of line matching in visual SLAM effectively and the operating efficiency in feature matching, which makes the line features play an active role in the SLAM process, and the stability of data association is improved.