In order to address the issue of decreased localization accuracy in indoor visual localization caused by occluding objects such as furniture and pedestrians, an indoor visual localization system based on an occlusion removal algorithm was proposed. Firstly, the YOLOv11 object detection algorithm was used to identify and locate occluded objects, reset the pixels in the occluded area, and generate a masked image. Subsequently, the mask aware transformer(MAT) image restoration algorithm was used to restore the unobstructed image, in order to obtain complete environmental information for subsequent localization. During the localization phase, the essential matrix-based localization algorithm combined with SuperPoint feature extraction and LightGlue feature matching methods effectively improves robustness under lighting changes and viewpoint shifts. Experimental results show that the proposed system performs excellently on both the 7Scenes dataset and the custom LH dataset. On the 7Scenes dataset, the localization error is (0.063 m, 1.759°), which improves position accuracy by approximately 6% and reduces rotation error by about 4% compared to the widely used SuperPoint + SuperGlue algorithm in the indoor visual localization field. On the LH dataset, position accuracy is further improved by around 15%. When compared to the state-of-the-art algorithms, although the accuracy is slightly lower, the proposed method, which uses occlusion-free image datasets, still performs excellently in complex scenes, especially in handling occlusions, maintaining high robustness and precision. Additionally, the proposed method is more lightweight, enabling faster deployment and training, making it suitable for real-time application scenarios.