A Grid-Continuity-Constraint Method for Extracting Single-Photon Lidar Point Cloud

The existing single-photon LiDAR, such as ICESat-2/ATLAS, are with high observation sensitivity and significant background noise, which usually require effective filtering methods for removing noise. This paper proposes an improved adaptive photon point cloud signal extraction method based on the principle of point cloud density denoising. The proposed method first adaptively determines grid width and height according to the point cloud distribution characteristics to partition point cloud data into grids. Then, it conducts grid continuity tests as units for point cloud filtering. Finally, it employs K-means clustering and cloth simulation filter algorithms to accurately extract reliable vertical control points. Experimental results on ATL03 photon point clouds show that the proposed method achieves promising effectiveness for point cloud data with different terrain variations by achieving approximately 99.0%, 99.9%, and 99.5% in terms of signal point recall rate (Recall) , precision rate (Precision) , and F-measure, respectively. From the registration experimental results with reference point clouds, the elevation errors of photon elevation points are 0.960 m, 0.957 m, and 0.872 m, respectively, outperforming the official ATL08 control group provided by the authorities.