Aiming at the increasing demands for positioning precision and availability in indoor environments

simultaneous localization and mapping（ SLAM） based on vision has become a hot field in indoor positioning research

but the visual SLAM algorithm may be less robust in low-texture environments due to the use of points as features

or it may be too sensitive to the environment because it uses only photometric calculations. A semi-direct monocular visual positioning algorithm based on the fusion of photometric and point/line features is proposed

combined with the direct method based on photometry to construct a relatively dense inverse depth scene graph to optimize poses

and adds straight line features to the point-based algorithm to improve feature richness and pose estimation accuracy. Experiments on the EuRo C dataset and actual scene show that the proposed method has higher pose estimation precision and robustness.