A Scanline Keypoint Constraint-Based Method for Correcting Scale-like Errors in UAV-ALB

To address scale-like errors in local point clouds caused by pitch angle errors in unmanned aerial vehicle-borne LiDAR bathymetry (UAV-ALB) systems, this study proposes a correction method based on scanline keypoint constraints. First, the point cloud is traversed using scanline information to extract geometric keypoint features from each scanline. Next, a scale-like error correction model is constructed based on the distance differences between keypoints at the start and end of overlapping scanlines, and the pitch angle correction value is estimated using a forward–backward fusion approach. The estimated correction is then applied to the attitude data, and the point cloud is reprocessed. This procedure is iterated until the convergence criterion is satisfied. Experimental results show that, after correction, the horizontal root mean square errors (RMSE) of the two datasets decrease from 0.217 and 0.283 m to 0.025 and 0.030 m, respectively, while the elevation RMSE values decrease from 0.227 and 0.241 m to 0.006 and 0.004 m, respectively. These results demonstrate that the proposed method effectively mitigates scale-like errors in UAV-ALB point clouds and substantially improves their geometric consistency and elevation accuracy.