To solve the decline in simultaneous localization and mapping (SLAM) accuracy caused by the inferior quality of line-feature extraction in low texture areas and dynamic lighting environments, this paper proposes a point-line binocular vision SLAM method based on the improved ELSED line-feature-extraction algorithm. In the feature-extraction stage, the angle difference and distance threshold are introduced to merge short line segments, and the mask technique is used to homogenize the distribution of line features to improve the quality of line features. In the feature-matching stage, a matching threshold condition and bidirectional consistency-detection mechanism are used to improve the feature-matching accuracy. In the back-end optimization, the joint optimization function is constructed by extending the PnPL formula of line features, and the reprojection errors of point and line features are uniformly incorporated into the optimization objective. Experiments are performed on the EuRoC, KITTI, and UMA datasets. The results show that the proposed algorithm significantly improves the robustness and positioning accuracy of the system compared with other point-line SLAM algorithms. Compared with the results yielded by ORB-SLAM3, the positioning accuracy on the three datasets improved by 49.6%, 19.6%, and 82.3%, respectively.