In dynamic environments, the presence of moving objects such as pedestrians and vehicles within the sensor′s field of view causes significant interference in the creation of static point cloud maps. These dynamic objects often result in “ghost” artifacts, severely affecting the accuracy and completeness of the generated maps. To tackle this challenge, this paper proposes a high-precision algorithm for static point cloud map generation specifically designed for complex dynamic scenarios. The goal is to effectively eliminate dynamic interference points while preserving map construction accuracy. The method begins by serializing multi-frame point cloud data and applying region-based ground segmentation to minimize the impact of ground points on dynamic point identification. A multi-stage offline dynamic point cloud filtering strategy is then implemented. In the first two stages of dynamic point identification, two distributed descriptors (D-POD and D-PODV) are used to capture the spatial occupancy and distribution patterns of the point cloud. These descriptors are combined with the scan ratio test (SRT) and radial ratio test (RRT) to precisely identify both strongly and weakly dynamic points. In the third stage, an improved adaptive DBSCAN clustering algorithm, based on regional density, is employed to further refine the removal of irregular dynamic point clouds. Experimental results using the publicly available SemanticKITTI dataset show that the proposed algorithm effectively filters out dynamic points, resulting in high-precision static point cloud maps across diverse complex scenarios. Compared to state-of-the-art dynamic point cloud filtering algorithms, ERASOR and Removert, the proposed method achieves average improvements of 3.95% and 14.56% in static point retention, and 13.44% and 17.46% in dynamic point rejection, respectively. By employing staged filtering for both strongly and weakly dynamic point clouds, the proposed method successfully eliminates various types of dynamic objects while maximizing the preservation of static information in the original point cloud. This ensures the structural integrity of the global map, providing robust support for the creation of high-precision and highly reliable static point cloud maps across a wide range of applications.