Due to the limitations of measurement equipment and the influence of factors such as the environment and target, measurement errors may occur during the data acquisition process of airborne LiDAR bathymetry (ALB). The refractive index of water is defined as the propagation ratio of the speed of light waves in a vacuum to that in water; this ratio influences not only the propagation speed of the laser pulse in water but also the propagation direction of the laser pulse entering water. Therefore, the influence of refractive index changes in water on the ALB errors needs to be analyzed. To this end, the principle of ALB is first briefly introduced. Then, the calculation method for the refractive index of water is described with Snell’s law and an empirical formula. Finally, the influence of refractive index changes on ALB errors is analyzed using the derived formula at the water–air interface and in the water column. The experimental results showed that in a constant elevation of 50 m for a bathymetric floor, the refractive index changes in water caused by temperature, salinity, and depth are less than 0.001. The maximum bathymetric error and maximum planimetric error caused by the refractive index changes at the water–air interface are 0.036 m and 0.015 m, respectively. The ALB errors caused by refractive index changes in the water column are relatively low, and the water column does not need to be layered to calculate the ALB errors. The influence of refractive index changes in water on the ALB error is minimal, accounting for only a small proportion of all bathymetric errors. Thus, it is necessary to determine whether the effect of the ALB error due to refractive index changes in water needs to be corrected based on the accuracy requirements of the data acquisition. This study and analysis can provide a reference basis for correcting ALB errors.

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 airborne LiDAR bathymetry 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 RANSAC algorithm and 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 m and 0.041 m, respectively. Compared with that of the ICP alignment algorithm, the accuracy of the proposed method is improved by 0.114 m and 0.227 m, respectively, and compared with that of the NDT algorithm, the accuracy is improved by 0.316 m and 0.452 m, respectively; hence, the proposed method provides an effective solution for the registration of ALB data.