Detection And Tracking of Moving Objects (DATMO) is essential and necessary for mobile mapping system to generate clean and accurate point clouds maps since dynamic targets in real-world scenarios will deteriorate the performance of whole system. In this research, a robust LiDAR-SLAM system is presented incorporated with a real-time dynamic objects removal module to improve the accuracy of 6 DOF pose estimation and precision of maps. The key idea of the proposed method is to efficiently cluster the sparse point clouds of moving objects and then track them independently so as to relieve their influence on the odometry and mapping results. In the back-end, in order to further refine the point clouds maps, a valid probabilistic map fusion method is performed based on the <i>free-space</i> theory. We have evaluated our system on the dataset collected from daily crowded environments full of moving objects, providing competitive results with the state-of-the-art system both on the pose estimation and point cloud mapping.