A precise positioning technique is proposed based on the speeded up robust features (SURF) algorithm. By identifying the quick response (QR) code laid on the ground, this technique is used to complete positioning prediction and attitude correction. First, the acquired QR image is preprocessed, and the feature point information of the image is extracted by the SURF algorithm. The feature points of both the real-time image and the target image are then matched, and the transformation matrix between these two images is obtained by least square fitting. Finally, the transformation matrix is combined with the visual guidance model of the automatic guided vehicle (AGV), and the precise positioning of the AGV is realized. The experimental results show that for a heavy-duty AGV with a large structural size, the proposed method has robust positioning with a precision of ±1 mm.