Improving Monocular Visual Odometry Using Learned Depth

Monocular visual odometry (VO) is an important task in robotics and computer vision. Thus far, how to build accurate robust monocular VO systems that can work well diverse scenarios remains largely unsolved. In this article, we propose a framework exploit depth estimation for improving VO. The core of our module with strong generalization capability scenes. It consists two separate working modes assist the localization mapping. With single image input, predicts relative help on accuracy. sparse map RGB generate scale-consistent dense Compared current learning-based methods, method demonstrates stronger ability More significantly, able boost performances existing geometry-based methods by large margin.