Unsupervised Monocular Visual Odometry Based on Confidence Evaluation

With the rapid development of autonomous vehicle technologies, how to perform high-precision localization in unknown complex outdoor environment has become an important issue. Visual odometry is one of the low-cost and the most widely utilized localization methods. Traditional methods predict relative pose based on the principle of multi-view geometry, which is sensitive to camera parameters and environmental changes. This paper studies deep learning-based methods which can be more robust. A novel end-to-end unsupervised visual odometry framework based on confidence evaluation is proposed. Its process can be divided into two stages. The first is predicting the initial relative pose transformation with the help of confidence mask which is generated by measuring the relative similarity of geometric corresponding regions in associated images. The second is evaluating the confidence of the output pose estimate based on the trajectory geometric consistency and then refining it. Quantitative and qualitative evaluation of the proposed approach on KITTI dataset are presented to demonstrate its effectiveness in improving pose estimation accuracy and robustness.