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 TLS 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.

通过使用机载激光扫描（ALS）和地面激光扫描（TLS），可以快速捕获顶视图和侧视图中的陆地表面信息。但是，由于透视图，分辨率和范围的不同，将多个TLS扫描自动定位到ALS具有挑战性。为了解决这个问题，本文提出了一种新颖的基于天际线上下文的方法。首先，提取ALS中的地面点并将其用作潜在的TLS位置，并生成相应的天际线上下文。之后，将构建基于3D天际线的kd树，以搜索TLS扫描的相应粗略位置。最后的改进是通过修剪的迭代最近点算法（T-ICP）完成的。评估了5种具有不同ALS大小和超过100次TLS扫描的数据集，以评估该方法的性能。对于一个平均点距离为0.1 m的ALS数据，平均定位精度约为0.13 m。实验结果表明，所提出的方法在将TLS扫描自动定位到ALS点云方面表现良好，在精度和适应性方面均具有优势。