Abstract: In this work, we propose a lightweight dynamic object filtering algorithm for building LiDAR-based static point cloud maps in realtime. On one hand, we propose an egocentric motion detection method of using improved ICP to register 3D clusters and extract their poses and twists to identify dynamic objects. One the other hand, we connect the proposed dynamic object filter with LiDAR-based SLAM algorithms to build point cloud maps and validate the effectiveness of the proposed methodology on both our custom dataset and SemanticKITTI. We also compare the performance of the proposed method against state-of-the-art methods in terms of both filtering accuracy and processing time. As experimentally verified on SemanticKITTI, our method yields promising performance with relatively small time costs and therefore has great potential to be used as point cloud data source for a number of LiDAR-inertial-visual fusion mapping methods.