Keywords
Point cloud registration
Point cloud data processing
Systematic error
Submitted : 2025-08-09
Accepted : 2025-08-24
Published : 2025-09-08
Abstract
Airborne LiDAR (Light Detection and Ranging) is an evolving high-tech active remote sensing technology that has the capability to acquire large-area topographic data and can quickly generate DEM (Digital Elevation Model) products. Combined with image data, this technology can further enrich and extract spatial geographic information. However, practically, due to the limited operating range of airborne LiDAR and the large area of task, it would be necessary to perform registration and stitching process on point clouds of adjacent flight strips. By eliminating grow errors, the systematic errors in the data need to be effectively reduced. Thus, this paper conducts research on point cloud registration methods in urban building areas, aiming to improve the accuracy and processing efficiency of airborne LiDAR data. Meanwhile, an improved post-ICP (Iterative Closest Point) point cloud registration method was proposed in this study to determine the accurate registration and efficient stitching of point clouds, which capable to provide a potential technical support for applicants in related field.
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