DynaLoc: Real-Time Camera Relocalization from a Single RGB Image in Dynamic Scenes based on an Adaptive Regression Forest

Camera relocalization is an important component in localization systems such as augmented reality or robotics when camera tracking loss occurs. It uses models built from known information of a scene. However, these models cannot perform in a dynamic environment which contains moving objects. In this paper, we propose an adaptive regression forest and apply it to our DynaLoc, a real-time camera relocalization approach from a single RGB image in dynamic environments. Our adaptive regression forest is able to fine tune and update continuously itself from evolving data in real-time. This is performed by updating a relevant subset of leaves, which gives uncertain predictions. The results of camera relocalization in dynamic scenes report that our method is able to address a large number of moving objects or a whole scene to gradually change in order to obtain high accuracy avoiding accumulation of error. Moreover, our method achieves results as accurate as the best state-of-the-art methods on static scenes dataset.