In various applications of mobile robotics, such as automated warehousing logistics scenarios, due to the limitation of lidar installation location. The adoption of a single LiDAR for Simultaneous Localization and Mapping (SLAM) introduces challenges pertaining to restricted field of view and complexities in achieving loop closure. In response, we proposes a multi-LiDAR localization and mapping methodology incorporating tight coupling with an Inertial Measurement Unit (IMU), building upon the FAST-LIO2 algorithm. This approach not only expands the perceptual range of the robot but also enhances localization precision and mapping effect. Through rigorous evaluation via offline tests utilizing public datasets and online experiments conducted on the experimental platform, the proposed algorithm demonstrates marked enhancements in localization accuracy and mapping effect compared to the M-LOAM and FAST-LIO2 algorithms, concurrently exhibiting reduced loop closure drift.