Concerning the problem that the accuracy and real-time effects of virtual-real registration in Augmented Reality (AR) based on vision are greatly affected by the changes of illumination, occlusion and perspective, which is easy to lead to failure of registration, a virtual-real registration method of natural features based on Binary Robust Invariant Scalable Keypoints-Speeded Up Robust Features (BRISK-SURF) algorithm was proposed. Firstly, Speeded Up Robust Features (SURF) feature extractor was used to detect the feature points. Then, Binary Robust Invariant Scalable Keypoints (BRISK) descriptor was used to describe the feature points in binary, and the feature points were matched accurately and efficiently by combining Hamming distance. Finally, the virtual-real registration was realized according to the homography relationship between images. Experiments were performed from the aspects of image feature matching and virtual-real registration. Results show that the average precision of BRISK-SURF algorithm is basically the same with that of SURF algorithm, is about 25% higher than that of BRISK algorithm, and the average recall of BRISK-SURF is increased by about 10% compared to that of BRISK algorithm; the result of the virtual-real registration method based on BRISK-SURF is close to the reference standard data with high precision and good real-time performance. The Experimental results illustrate that the proposed method has high recognition accuracy, registration precision and real-time effects for images with different illuminations, occlusions and perspectives. Besides, the interactive tourist resource presentation and experience system based on AR is realized by using the proposed method.