We aim to provide a large-scale point cloud-based 3D map that reflects the internal structure in a building for autonomous mobile robots. We propose a new method that iteratively updates the 3D map based on self-localization in dynamic environment. We assume that a patrol robot collects 3D points required for constructing a 3D map in database. We adopt multi-layer NDT (Normal Distributions Transform), which handles multiple horizontal and multiple vertical scan lines, to robustly estimate the robot’s 3D position in real environments. Our proposed method estimates a specified floor in the building and determines a 2D localization on the floor. Based on the self-localization, the method detects depth variations by taking advantage of 3D LiDAR. Once our method detects some dynamic changes on patrol, it replaces the previous 3D points corresponding to the space in 3D map with the latest 3D points. As our approach estimates an accurate self-localization in the 3D map, the 3D map updated by the method is seamless without giving uncomfortable feeling. We demonstrate that our iterative update method is an effective way of successively renewing the 3D map for inside a building.