Aiming at the adverse effect of luminosity and viewpoint change on feature point extraction stability in Visual Odometry (VO) of feature point method, a monocular VO method based on deep learning feature point method is proposed. The deep learning SuperPoint (DSP) feature point detector is obtained by self-supervised deep learning network training. Firstly, the brightness of the training image is adjusted by the brightness nonlinear point-by-point adjustment method. Secondly, the redundant DSP feature points are eliminated by using the non-maximum value suppression method. The two-way nearest neighbor algorithm is improved based on the nearest neighbor algorithm to solve the feature point matching problem. Finally, the equation for minimizing the reprojection error is established to solve the optimal pose and spatial point parameters. The experimental results on Hpatches and Visual Odometry datasets show that the DSP feature point detector enhances the robustness of feature matching to luminosity and viewpoint changes. Without the backend optimization, this method reduces the root mean square error obviously in comparison to ORB method. The real-time performance of the system is guaranteed, which provides a new solution for the VO of feature-based method.