Visual localization is used by robots and self-driving cars to estimate their position and in AR apps to interact with the physical world both indoors and out in the open. This article gives an overview of current state-of-the-art methods and their advantages and drawbacks.
Evaluating our method on public datasets, we show that it can successfully solve challenging situations in dynamic environments which cause state-of-the-art baseline VSLAM algorithms to fail and that it maintains performance on static scenes.
A new double depth-map representation of the human shape allows to recover 3D details from a single image. Using 2 depth maps (visible and hidden) makes representations more efficient and easier to handle.