Simultaneous localization and mapping (SLAM), as an important means of sensing the surrounding environment, has a direct impact on the execution efficiency of autonomous grounding vehicles (UGV). At present, the most common means used to map the environment is to use hardware devices such as LiDAR, inertial measurement unit (IMU) or camera to collect data with environmental information, and then run the SLAM algorithm to construct the environment map. It is crucial by designing a system that can integrate testing and evaluation of the SLAM algorithm to promote the optimization iteration of the algorithm. In this paper, we not only collected the data from different sensors on a real UVG in the physical world test field, but also imported these data into the virtual simulation platform to construct a map of the real world test field. By maneuvering a vehicle in this virtual test field and gathering the data from the corresponding sensors installed, a typical LiDAR SLAM algorithm based on the combination of virtuality and reality was tested and evaluated as a case study.