Research on Point Cloud Registration Method Based on Improved Harmony Search Algorithm

When measuring and inspecting hull surfaces using a 3D laser scanner, the large volume and complex surface features of the hull necessitate multi-station scanning. Complete hull surface data is obtained by registering point cloud data from multiple stations. However, the registration process accumulates errors, resulting in the registration not meeting inspection accuracy requirements. To address the issue of cumulative errors in point cloud registration for large-scale laser scanning, a multi-station point cloud data registration error correction algorithm based on global control targets is proposed. This algorithm leverages the long-distance measurement and high accuracy of total stations to compensate for the large measurement errors of the scanner at long distances. By applying the coordinate information of global control targets measured by the total station to the point cloud data registration, cumulative registration errors are effectively avoided, thereby improving measurement accuracy. Experimental results show that the proposed algorithm reduces the cumulative error of the first and last point clouds to 16% of that of traditional registration, effectively eliminating registration errors and meeting inspection requirements.