Abstract: A monocular visual odometry method based on generative adversarial network (GAN) and self-attention mechanism is proposed, named SAGANVO (SAGAN visual odometry). It applies a generative adversarial network learning framework to depth estimation and visual odometry tasks. The GAN generates realistic target frame to accurately solve the depth map of a scene and 6-DoF (degree of freedom) pose. Meanwhile, the self-attention mechanism is incorporated into the network model in order to improve the deep network ability to learn scene details and edge contours. Finally, high-quality results of the proposed model and method are demonstrated on the public dataset KITTI and compared with the existing methods, and it is proved that SAGANVO has better performance in depth estimation and visual odometry than the state-of-art methods.