In order to solve the problem that the scale invariant feature transform (SIFT) has a large amount of calculation and cannot meet the requirements of accuracy and real-time in the navigation algorithm, a parallel SIFT algorithm FG-SIFT based on fast Gaussian blur is proposed. First, the two-dimensional Gaussian kernel function, which constructs the Gaussian pyramid, is separated into two one-dimensional Gaussian functions to reduce the computational complexity. Then, two infinite impulse response filters are used in series to approximate each one-dimensional Gaussian kernel function to further reduce the computational complexity. Finally, using the advantage of parallel processing, the parallel computing scheme of each part of the algorithm is designed. Simulation results show that the computational efficiency of FG-SIFT algorithm is 15 times higher than that of the original SIFT algorithm, and the running efficiency of FG-SIFT algorithm on graphics processing unit is nearly 2 times higher than that of SIFT without fast Gaussian blur. This algorithm greatly reduces the calculation time of feature point extraction and improves the real-time performance.