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In order to improve the low localization accuracy of visual simultaneous localization and mapping (SLAM) system in low texture environment, an improved oriented fast and rotated brief (ORB) feature point extraction strategy and a keyframe selection mechanism are proposed. Firstly, multi-scale analysis and feature detection method based on local gray level are used to overcome the shortcomings of general ORB algorithm which lacks scale and rotation description. Secondly, an image information enhancement method based on Gaussian blur is proposed to solve the problem that the traditional ORB feature point extraction method is easy to fail in the environment where the texture information is not prominent, and the image is segmented to make the feature points evenly distributed. Finally, in order to eliminate inferior keyframes, a keyframe selection mechanism combining time factor and feature point number factor is designed. The proposed method is transplanted to ORB _ SLAM2 and tested on the TUM dataset. The experimental results show that the localization error of the visual SLAM system is reduced by 14.688 % on average, which confirms the effectiveness of the proposed method.