Estimating the camera pose given images of a single camera is a traditional task in mobile robots and autonomous vehicles. This problem is called monocular visual odometry and it often relies on geometric approaches that require engineering effort for a specific scenario. Deep learning methods have shown to be generalizable after proper training and a considerable amount of available data. Transformer-based architectures have dominated the state-of-the-art in natural language processing and computer vision tasks, such as image and video understanding. In this work, we deal with the monocular visual odometry as a video understanding task to estimate the 6-DoF camera's pose. We contribute by presenting the TSformer-VO model based on spatio-temporal self-attention mechanisms to extract features from clips and estimate the motions in an end-to-end manner. Our approach achieved competitive state-of-the-art performance compared with geometry-based and deep learning-based methods on the KITTI visual odometry dataset, outperforming the DeepVO implementation highly accepted in the visual odometry community.

利用基于自注意力机制的TSformer-VO模型，将单目视觉里程计作为视频理解任务，从视频片段中提取特征并通过端到端的方式估计摄像机的6-DoF位姿，取得了与基于几何和深度学习的方法相比具有竞争力的业内领先表现。

基于Transformer模型的单目视觉里程计：一种视频理解方法