The autonomous navigation system requires a static map for better usability. However, urban environments are filled with dynamic objects, such as vehicles and pedestrians that leave traces in the 3D
point cloud map. These traces can act as obstacles and impede navigation systems. As a result, extensive studies have been conducted to remove dynamic objects based on geometric information, so it is challenging to remove dynamic objects in highly dynamic environments. To tackle this problem, we propose a method for dynamic object removal in 3D point cloud maps. The proposed method combines a pseudo occupancy-based method, morphological image processing, and deep learning.