With the increasing demand of navigation technology, indoor localization technology has been getting more and more important. Traditional approaches often based on WiFi localization with high cost and cannot be promoted massively. In this paper, we propose a method for the indoor wireless localization based on image retrieval, with high accuracy rate of localization but lower cost. We use Scale-Invariant Feature Transform (SIFT) operator for image feature extraction specifically, and choose the Fast Library for Approximate Nearest Neighbors (FLANN) algorithm to search the target image according to features of the sample image, so as to correct the user’s real-time position. The experiment result shows that the position accuracy of the proposed algorithm is up to 85.7%, proving that it’s an efficient way for indoor localization.

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