Addressing the challenge of trajectory drift in visual Simultaneous Localization and Mapping (SLAM) due to point features in texture-deficient indoor settings, this study introduces a binocular visual SLAM system that combines point and line features. It emphasizes the extraction and matching of line features within binocular visual SLAM. An enhanced line feature extraction technique, based on the Line Segment Detector (LSD) algorithm, is proposed. This includes improvements like length and gradient filtering, and the amalgamation of short lines. Additionally, the matching issue is redefined as an optimization challenge, creating a cost function based on geometric constraints. A novel, efficient line segment triangulation approach, leveraging the L1-norm sparse solution, is developed for effective line matching and triangulation. Experimental evidence shows that our method surpasses traditional descriptor-based approaches across various datasets, especially in texture-sparse indoor areas, achieving a remarkable average matching accuracy of 91.67% and a swift average matching time of 7.4 ms. Employing this technique, our binocular visual SLAM system records positioning errors of 1.24, 7.49, and 3.67 m on texture-sparse datasets, outperforming leading algorithms like ORBSLAM2 and PL-SLAM in positioning precision.