In urban environments, creating an accurate and reliable map is crucial for mobile robotic tasks, including localization, path planning, and navigation. However, the presence of dynamic objects, such as vehicles and pedestrians, can potentially affect static mapping performance. To tackle this challenge, we propose an efficient and robust dynamic removal framework, which establishes a high-quality static map by eliminating interference from dynamic objects. In our framework, a packet-based region growth and secondary fine segmentation (RG-SF) algorithm is proposed to perform adaptive segmentation of the point cloud within a range image, thereby ensuring the accurate removal of dynamic objects. Additionally, we leverage a convex hull triangle mesh (CHTM) to represent the geometric relationships among matched LiDAR points. Furthermore, we utilize the correlation between LiDAR points to distinguish points belonging to static or dynamic scenes by detecting changes in the geometric relationship of adjacent points. Subsequently, dynamic objects are labeled and removed using a multilevel voting strategy, resulting in a comprehensive global static map. Experimental results from SemanticKITTI and our custom dataset showcase that our dynamic removal method significantly enhances static mapping performance and effectively mitigates the influence of dynamic objects across various scenes, surpassing other state-of-the-art methods.