A Spatial Alignment Method for UAV LiDAR Strip Adjustment in Nonurban Scenes

Light detecting and ranging (LiDAR) strip adjustment is a key prerequisite for subsequent applications based on point cloud data since it inevitably suffers from spatial discrepancies caused by laser ranging errors, mounting errors, etc. Most current LiDAR strip adjustment methods rely on the extraction of structural features, which are often unsuitable for nonurban scenes. Alternative strip adjustment methods based on correspondence distance minimalization ignore spatial alignment. To overcome these limitations, this article presents an accurate spatial alignment method for an unmanned aerial vehicle (UAV) LiDAR strip adjustment in nonurban scenes. First, we construct a novel point cloud feature descriptor called Spherical Shell Point Feature (SSPF) to extract multidimensional nonstructural features that are robust to nonurban point clouds. The constructed SSPF is then combined with point coordinates to generate embedded features, which simultaneously consider the point coordinates and spatial alignment. Finally, the embedded features are used by a two-stage matching method to match pair-wise points of two adjacent strips. The proposed method is validated on two nonurban datasets collected by two types of LiDARs, which reduces the digital surface model (DSM) discrepancies by 0.252 and 0.221 m, respectively, and proves its superiority compared to mainstream strip adjustment methods as well.