In the indoor visual positioning system, the offline Visual Map database is usually used to store the database images, and then user position is estimated by comparing with the images in the Visual Map database in the online phase.The database establishment in the offline phase can be realized with point-by-point sampling method or video stream sampling method.However, taking into account the similarity of database images, redundancy exists between the images in the database, which leads to more positioning time consumption in the online phase.Therefore, owing to the similarity between successive images, a Visual-Depth Map method which reduces the database scale is proposed based on image keyframes.In the offline phase, the method adopts a Kinect sensor which acquires image information and depth information simultaneously.And then, for establishing the Visual-Depth Map, the keyframe sequence is selected from original database images by the image keyframe algorithm that is based on the image similarity.In the online phase, the query image captured by the user is retrieved and matched with the database image in the Visual-Depth Map with higher similarity.The EPnP algorithm is employed to estimate the query camera pose so as to achieve the user position.Experimental results show that the proposed Visual-Depth Map method based on the image keyframe reduces the database scale and positioning time consumption comparing with traditional methods, under the precondition of the high positioning accuracy.