This letter introduces a novel monocular visual odometry network structure, leveraging the Swin Transformer as the backbone network, named SWformer-VO. It can directly estimate the six degrees of freedom camera pose under monocular camera conditions by utilizing a modest volume of image sequence data with an end-to-end methodology. SWformer-VO introduces an Embed module called “Mixture Embed”, which fuses consecutive pairs of images into a single frame and converts them into tokens passed into the backbone network. This approach replaces traditional temporal sequence schemes by addressing the problem at the image level. Building upon this foundation, various parameters of the backbone network are continually improved and optimized. Additionally, experiments are conducted to explore the impact of different layers and depths of the backbone network on accuracy. Excitingly, on the KITTI dataset, SWformer-VO demonstrates superior accuracy compared with common deep learning-based methods such as SFMlearner, Deep-VO, TSformer-VO, Depth-VO-Feat, GeoNet, Masked Gans and others introduced in recent years. Moreover, the effectiveness of SWformer-VO is also validated on our self-collected dataset consisting of nine indoor corridor routes for visual odometry.