Most of today's SLAM approaches learn the map of the environment in the first stage (referred to as mapping) and subsequently use this static map for planning and navigation. This method is suboptimal in dynamic contexts because changes in the environment can result in poor performance of the localization components essential for loop closure detection and relocalization. To address the limitations of the mapping-navigation dualism, continual SLAM has been proposed, which focuses on methods that can continually update the knowledge of the environment and the corresponding map. However, continual SLAM poses challenges, particularly for real-time navigation of large maps, and many of the existing techniques are not yet mature for practical application. In this paper, we present a continual learning approach aimed at accurate and efficient robot localization on large maps, advancing the goal of continual SLAM. Our approach incrementally trains a region prediction neural network to recognize familiar places and preselect a subset of map nodes for localization and map optimization. We integrate this method into RTAB-Map, a well-known graph-based SLAM system, and validate its practical applicability through assessments on several real-world SLAM datasets.