Light Detection And Ranging (LiDAR) technology has provided an impactful way to capture 3D environmental map. However, consistent mapping in sensing-degenerated and perceptually-limited scenes (e.g. multi-story buildings) or under high dynamic sensor motion (e.g. rotating platform) remains a significant challenge. To this end, an efficient multi-floor indoor mapping system is proposed in this paper utilizing a versatile rotating LiDAR platform. In the front end, measurements from motor, IMU and LiDAR are tightly integrated to track the fast motion state of system, based on iterative Error State Kalman Filter (ESKF). Then linear and planar features are extracted from the point cloud map voxelized with adaptive resolutions. A sliding-window-based batch optimization is performed to simultaneously optimize the states of local frames with reference to the map consistency. In the experiments, we investigated the influence of rotating speed on the mapping performance as well as the superiority of rotating mechanism when compared to standard LiDAR setup. Moreover, comparative studies with one of the SOTA work, FAST-LIO2, have shown the competitive mapping results in multi-floor indoor environments.