Abstract:Aiming at the limitation of motion blur on the reliability of visual simultaneous localization and mapping system in high dynamic environment, a motion deblurring visual SLAM scheme based on generative adversarial network and AKAZE feature points is studied. Firstly, the AKAZE feature points are extracted and detected for the blurred image generated by the rapid movement of the camera, and the image block weight is calculated according to the richness of feature points distribution. Combined with the variance information of gray image, the quantitative relationship table between feature points and blur degree is established; After that, the image reaching the fuzzy score threshold is synchronously inputted to the improved GAN network model. The network restores the texture information of the central fuzzy frame in the form of end-to-end. Finally, the output clear image is re pose estimated to participate in the ORB-SLAM2 back-end optimization process. Tested on the open data set tum, for the data set seriously affected by ambiguity, the scheme can significantly reduce the overall error of camera trajectory estimation and maintain good robustness.