Measurement acceptance plays a crucial supervisory and guiding role in mining engineering. However, traditional blasting acceptance processes and methods in underground mines are insufficient to meet modern production needs and affect the efficiency and quality of underground mining. To address this issue, the Yanqianshan Iron Mine -213 m level roadway was studied to explore a new measurement and acceptance method based on a high-precision laser SLAM (Simultaneous Localization and Mapping) algorithm. By obtaining point cloud data of the roadway before and after underground mine excavation, the foundation for subsequent data analysis and processing was established. In the data processing phase, methods such as point cloud denoising, ICP (Iterative Closest Point) registration, point cloud segmentation, and slicing were employed to create comprehensive measurement and acceptance processes for underground mining engineering. Point cloud denoising effectively removes noise and enhances data purity and credibility. The ICP registration method ensures precise alignment of point clouds through iterative optimization, maintaining high data consistency. Point cloud segmentation and slicing techniques offer practical solutions for accurately calculating irregular explosion volumes. The research results demonstrate that this high-precision laser SLAM measurement acceptance method improves work quality and efficiency. It ensures construction quality in underground mining and provides critical technical support for optimizing underground blasting designs.