In order to solve the problem of localization and mapping of mobile robots, we propose a new simultaneou localization and mapping (SLAM) algorithm based on the fusion of line feature and optical flow. Firstly, the mainstream visual SLAM algorithms use points as features, resulting in that the point cloud map is sparse and it is difficult to accurately express the environmental structure information. Aiming at this problem, we take straight lines instead of points as a feature to construct the map, and employ the graph optimization method to improve the accuracy of localization and map construction. Secondly, the processing speed of the localization system cannot achieve realtime requirement, we introduce the optical flow method to realize realtime localization. Experimental results demonstrate that the map construction based on line features has higher mapping accuracy, and the fusion algorithm overcomes the shortcomings of poor localization accuracy of the optical flow and the low processing speed of the feature matching method, providing more accurate realtime localization output. And it is robust to circumstances such as illumination change and low scene texture.