Photovoltaic power stations have the environmental characteristics of large-scale sites, sparse structural elements, and narrow corridors caused by arrays of photovoltaic modules. In response to the issues of inaccurate pose estimation and incomplete mapping encountered while using the simultaneous localization and mapping (SLAM) algorithm with a two-dimensional LiDAR-equipped inspection robot for location and mapping in photovoltaic power stations, we propose an algorithm by adopting the Cartographer algorithm as a framework and incorporating a front-end optimization strategy based on factor graph optimization. Herein, we construct inertial measurement unit (IMU) factors through preintegration processing and match pose factors from LiDAR data scanning. Then, we jointly add them as constraints to the factor graph for optimization to obtain more accurate estimated poses and embed these poses into the original algorithm for map construction. Additionally, Experiments were conducted in a simulated photovoltaic power station and a simulated narrow corridor with mainstream filtering, Cartographer, and improved algorithms. Results reveal that our improved algorithm generates maps with a higher dimensional accuracy and a more accurate overall description.