To address the problems of weak feature extraction ability, lower positioning accuracy and poor robustness of the feature pointbased visual simultaneous localization and mapping ( SLAM) algorithm in low-texture environment, this article proposes a featureenhanced visual SLAM algorithm based on the sparse direct method. Firstly, the image sequence is preprocessed to improve the feature extraction ability of the algorithm. Then, the pose is solved by combining the sparse direct method based on graph optimization and the feature point method. The operation efficiency and robustness of the algorithm are improved under the premise of ensuring the positioning accuracy of the algorithm. The experimental results of the TUM data set show that the positioning accuracy of the proposed algorithm is better than those of the current SLAM algorithms. In the scenario with sparse texture in the TUM data set, the number of feature points extracted by the algorithm is 9. 6 times more than that of the ORB-SLAM2 algorithm, and the average number of points per frame tracking time is reduced by 58% .