In this paper, we present a sequential video deblurring method based on a spatio-temporal recurrent network for visual SLAM. The method can be applied to any SLAM systems to make sure continuous localization even with blurred images. The quality of the deblurring method is evaluated on real-world problems: feature points extraction and SLAM, which prove the method can significantly improve the performance of tracking accuracy especially in some severe cases containing strong camera shake or fast motion.