To address challenges related to low registration accuracy and efficiency in multi-source point cloud data registration for buildings,an enhanced Trimmed Iterative Closest Point (TrICP) method incorporating principal component analysis (PCA) is introduced.Firstly,the PCA algorithm is used to calculate the principal aXis direction of the point clouds and create a correc- tion matriX based on the direction reversal problem,so as to obtain the initial good positional transformation of the two groups of point clouds and complete the coarse alignment step of the point clouds;then,the improved Trimmed Iterative Closest Point algorithm is used to complete the fine alignment of the two kinds of point clouds for the problems of searching point pairs and iteration number in Trimmed Iterative Closest Point.The eXperimental data show that the improved alignment algorithm signif- icantly reduces iteration counts,enhances alignment accuracy,preserves point cloud integrity,and boosts alignment efficiency.Comparative analysis shows accuracy improvements of 56.75%,39.6%,and 28.08% over three other algorithms,highlight- ing the effectiveness of the proposed approach.