A novel skyline context descriptor for rapid localization of terrestrial laser scans to airborne laser scanning point clouds

By utilizing the airborne laser scanning (ALS) and terrestrial laser scanning (TLS), the land surface information from both top view and side view can be captured rapidly. However, due to the different perspective views, resolutions, and ranges, the automatic localization of multiple TLS scans to ALS is challenging. To address this issue, this paper proposes a novel skyline context-based method. First, the ground points in ALS are extracted and used as potential TLS locations, and the corresponding skyline contexts are generated. After that, a 3D skyline-based k-d tree is built for searching the corresponding coarse localizations of TLS scans. The final refinement is done by the trimmed iterative closest point algorithm (T-ICP). 5 datasets with different ALS sizes and over one hundred Us scans are undertaken to evaluate the performance of the proposed method. For one ALS data with mean point distance of 0.1 m, the average localization accuracy reached about 0.13 m. The experimental results indicate that the proposed method performs well for automatic localization of TLS scans to ALS point clouds, with advantages in both precision and adaptability.