Simultaneous Localization and Mapping (SLAM) as a key technology for environment sensing and map building, is flexible and efficient. However, the traditional backpack l LiDAR has problems such as incomplete point cloud features and point cloud stratification caused by large data noise, which makes the system unable to perform accurate positioning and map building. In order to improve the accuracy of autonomous positioning, a robot operating system is adopted to build an autonomous positioning framework, integrating multi-sensor data such as LiDAR, inertial measurement unit and global navigation satellite system (GNSS), and using a SLAM algorithm based on graph optimization to achieve environmental map construction and system pose estimation. The experimental results show that compared with the traditional SLAM algorithm, the improved SLAM algorithm reduces the trajectory X-axis error by 39%, the Y-axis error by 30%, and the plane positioning accuracy by 73% after integrating GNSS data. At the same time, there is a 63% enhancement in elevation positioning accuracy, and a significant increase in building accuracy, which provides new ideas for the application of backpack measurement systems in complex environments.