This paper presents a visual simultaneous localization and mapping (SLAM) algorithm that combines the point and line features using a grouping strategy. The algorithm aims to improve the poor robustness of line feature extraction and low matching efficiency in the visual SLAM algorithm. First, the line segments grouping strategy merges potential homologous line segments to generate high-quality long line segments using the common junctions to screen the effective line features. Then, these line features are matched according to the affine invariance of point and line features. Finally, the error function of the point and line fusion features is constructed to minimize the reprojection error, thereby improving the estimation accuracy of the camera pose. When experimentally tested on the KITTI, EuRoC, TUM, and YorkUrban datasets, the algorithm effectively improved the robustness of feature extraction, thus improving the accuracy of camera pose estimation and mapping.