ExMaps: Long-Term Localization in Dynamic Scenes using Exponential Decay

Visual camera localization using offline maps is widespread in robotics and mobile applications. Most state-of-the-art localization approaches assume static scenes, so maps are often reconstructed once and then kept constant. However, many scenes are dynamic and as changes in the scene happen, future localization attempts may struggle or fail entirely. Therefore, it is important for successful long-term localization to update and maintain maps as new observations of the scene, and changes in it, arrive. We propose a novel method for automatically discovering which points in a map remain stable over time, and which are due to transient changes. To this end, we calculate a stability store for each point based on its visibility over time, weighted by an exponential decay over time. This allows us to consider the impact of time when scoring points, and to distinguish which points are useful for long-term localization. We evaluate our method on the CMU Extended Seasons dataset (outdoors) and a new indoor dataset of a retail shop, and show the benefit of maintaining a `live map' that integrates updates over time using our exponential decay based method over a static `base map'.