To address the inaccuracies of indoor map construction due to insufficient information derived from single data sources and errors in multi-sensor calibration, an indoor floor plan construction algorithm is proposed based on multi-source data fusion. The RGB-D sensor was used to obtain 3D structural features in indoor scenes, and the features were then integrated into LiDAR point clouds in the algorithm. Accordingly, an indoor floor plan with 3D structural features was constructed. In the algorithm, images collected by a depth camera in the RGB-D sensor were first converted into pseudo-LiDAR point clouds. Next, a filter based on polynomial function fitting was used to stratify and calibrate the pseudo-LiDAR point clouds, and then the LiDAR and pseudo-LiDAR point clouds were fused. Finally, the fused point cloud data were used to create the indoor 2D floor plan. Experimental results show that the proposed point-cloud hierarchical filtering and calibration method effectively fuses the LiDAR and pseudo-LiDAR point clouds, and the accuracy of the indoor 2D floor plan constructed by the fusion point clouds is significantly improved.