The back-end optimization part of the simultaneous localization and mapping (SLAM) algorithm based on graph optimization generally uses a direct nonlinear optimization method. However, the calculation time of the direct nonlinear optimization method increases proportionally with the cube of the graph size, and optimizing large-scale pose graphs has become a crucial bottleneck for mobile robots. Therefore, under the framework of graph optimization, the SLAM algorithm based on sparse pose optimization is used in this work to efficiently calculate the large sparse matrix of the constraint graph through the direct linear sparse matrix solving method. Additionally, it is processed and optimized by using the spanning-tree initialization method. At the same time, experiments are performed on an autonomously built mobile robot platform and the SLAM algorithm based on sparse pose optimization is compared with Gmapping and Hector algorithms in different indoor environments. Results show that the proposed algorithm is superior in mapping accuracy and has a lower CPU load.