Registration of Airborne LiDAR Bathymetry Seafloor Point Clouds Based on the Adaptive Matching of Corresponding Points

Complex terrains in coastal zones and shallow water areas around islands and reefs can be quickly detected via airborne LiDAR bathymetry (ALB) technology. Due to equipment placement deviations and measurement uncertainty, measurement deviations can occur in the overlapping areas of adjacent strips, so it is particularly necessary to register point clouds. To address areas with few seafloor structures and to overcome the challenges of extracting features in water areas with small terrain changes, a registration method for ALB seafloor point clouds based on the adaptive matching of corresponding points is proposed. First, the normal vector zenith angle, curvature change, and omnidirectional variance in the seafloor points are calculated. Then, the corresponding points are adaptively matched according to the terrain feature similarity and distance constraints. Finally, the random sample consensus (RANSAC) algorithm and iterative closest point (ICP) algorithm are used for coarse registration and fine registration, respectively. The experimental results show that the method provides high accuracy and a uniform distribution of corresponding points. The root mean square error (RMSE) values when the registration method is applied to a flat area and coral reef area are 0.102 and 0.041 m, respectively. Compared with that of the ICP alignment algorithm, the accuracy of the proposed method is improved by 0.114 and 0.227 m, respectively, and compared with that of the normal distributions transform (NDT) algorithm, the accuracy is improved by 0.316 and 0.452 m, respectively; hence, the proposed method provides an effective solution for the registration of ALB data.