Large cumulative errors of laser odometers and inaccurate rotation estimation can be encountered when the Lightweight and Ground-Optimized Lidar Odometry And Mapping (LeGO-LOAM) with line and surface feature matching is used for real-time mapping and positioning of an automated guided vehicle indoors and outdoors. In view of these problems, this work adopts the LeGO-LOAM with tightly coupled Inertial Measurement Unit (IMU) and lidar to construct the joint error function of IMU and lidar with the initial position and pose information provided by IMU for the lidar. As a result, the joint iterative optimization of position and pose is achieved. To cope with the outdoor cases with less structured information, a hybrid matching algorithm depending on the tight coupling of IMU and lidar is further proposed on the basis of the high positioning accuracy of the point-to-point Iterative Closest Point (ICP) algorithm in light of the complementarity between LeGO-LOAM and ICP algorithms. When there is much structured information in the environment, the laser odometer employs the LeGO-LOAM algorithm, and ICP algorithm functions in the case of less structured information. The experimental results show that the hybrid matching algorithm based on the tight coupling of IMU and lidar can effectively reduce the relative pose error and cumulative error of the laser odometer. In addition, it is able to eliminate some map ghosting by improving the positioning accuracy of Automated Guided Vehicles (AGVs).