Change-Resilient Localization Estimation

Indoor localization is essential in applications such as augmented reality or robotics. Existing solutions for localization in static scenes work well even for large environments, but localization in environments with movable objects whose pose in the scene change between sessions remains challenging. In this paper, we propose a change-resilient localization method based on a novel geometric descriptor computed only from geometric primitives. Our method is capable of re-identifying primitives that have moved in the scene. We leverage this feature to update a stored reference model (anchor) of the environment to accommodate the changes, which enables localization that is resilient to changes in the scene. We report on a set of experiments demonstrating the robustness and scalability of our method. In addition, we present use cases highlighting the importance of being able to update a reference model.