To address the issue of incomplete spatial environment information acquisition in traditional two-dimensional (2D) light detection and ranging (LiDAR) mapping, we propose a mapping strategy that leverages the fusion of solid-state LiDAR and 2D LiDAR using the Gmapping algorithm. First, we initiate a planar projection on the solid-state LiDAR point cloud data. Subsequently, the resultant laser data are combined with the optimal particle trajectory within the Gmapping algorithm to construct a grid map. This grid map is then integrated with the grid map carried by the optimal particle, resulting in a fused map designed to identify spatial obstacles. To enhance mapping accuracy, we employe an extended Kalman filter for the dynamic fusion of weights associated with the wheel odometer, laser odometer, and inertial measurement unit. This approach addresses the challenges posed by reduced fusion odometer accuracy in scenarios involving factors such as slippage or feature-matching failures of the laser odometer in environments with limited features. Subsequently, we conducte testing experiments on the fused map and the fusion mileage calculation method. The experimental outcomes demonstrate that the fused map effectively identifies spatial obstacles and the fused odometer exhibits an average positioning accuracy improvement of 17.0% compared to traditional methods.