A new method for fast calculation of low dynamic carrier velocity based on improved singular value decomposition (SVD)-Harris is proposed to solve the problem that the poor real-time performance of low dynamic carrier velocity calculation under the indoor environment with uneven illumination. Firstly, we use the SVD to compress and reconstruct the two adjacent visual images and use the improved Harris corner detection algorithm to detect the feature points of the two frames. Secondly, we use the normalized cross correlation (NCC) template matching algorithm to roughly match the feature points of two adjacent visual images. Thirdly, we use random sampling consistency (RANSAC) algorithm to eliminate the false matching point pairs. Finally, we use the information of the feature matching point pairs to calculate the carrier velocity. The experimental results show that the average calculation time of the traditional algorithm is 3.07 s, while that of the improved algorithm is 0.71 s. The error matching rate of the traditional algorithm is much greater than that of the improved algorithm. Compared with the traditional NCC template matching method, the proposed algorithm not only guarantees the accuracy of the velocity calculation of the low dynamic carrier, but also greatly improves the calculation efficiency of the carrier velocity under the indoor environment with uneven illumination. This study provides a theoretical basis for realizing the real-time visual navigation of indoor mobile robot.