LIU , Ziming

In this paper, we address the problem of increasing the precision of dense direct stereo visual odometry methods. Dense methods need a dense depth map to generate warped images (virtual views) that will match with reference images if the estimated pose is good. Previous works have shown that generating the depth map by machine learning methods...

Visual odometry is an important part of the perception module of autonomous robots. Recent advances in deep learning approaches have given rise to hybrid visual odometry approaches that combine both deep networks and traditional pose estimation methods. One limitation of deep learning approaches is the availability of ground truth data needed...

Hybrid visual odometry methods achieve state-of-the-art performance by fusing both data-based deep learning networks and model-based localization approaches. However, these methods also suffer from deep learning domain gap problems, which leads to an accuracy drop of the hybrid visual odometry approach when new type of data is considered. This...