Ephemerality meets LiDAR-based Lifelong Mapping Hyeonjae Gil 1, Dongjae Lee 1, Giseop Kim 2, and A young Kim 1 Abstract -- Lifelong mapping is crucial for the long-term deployment of robots in dynamic environments. In this paper, we present ELite, an ephemerality-aided LiDAR-based lifelong mapping framework which can seamlessly align multiple session data, remove dynamic objects, and update maps in an end-to-end fashion. Map elements are typically classified as static or dynamic, but cases like parked cars indicate the need for more detailed categories than binary. Central to our approach is the probabilistic modeling of the world into two-stage ephemerality, which represent the transiency of points in the map within two different time scales. By leveraging the spatiotemporal context encoded in ephemeralities, ELite can accurately infer transient map elements, maintain a reliable up-to-date static map, and improve robustness in aligning the new data in a more fine-grained manner . Extensive real-world experiments on long-term datasets demonstrate the robustness and effectiveness of our system. I. INTRODUCTION Over the past decade, Light Detection and Ranging (LiDAR)-based mapping has significantly advanced [1-4], increasing the demand for long-term deployment of such systems in various fields, including urban areas or construction sites [5]. These environments are inherently dynamic; objects frequently move, and layouts change.