  Registration of unmanned aerial vehicle laser scanning (ULS) and ground light
detection and ranging (LiDAR) point clouds in forests is critical to create a
detailed representation of a forest structure and an accurate inversion of
forest parameters. However, forest occlusion poses challenges for marker-based
registration methods, and some marker-free automated registration methods have
low efficiency due to the process of object (e.g., tree, crown) segmentation.
Therefore, we use a divide-and-conquer strategy and propose an automated and
efficient method to register ULS and ground LiDAR point clouds in forests.
Registration involves coarse alignment and fine registration, where the coarse
alignment of point clouds is divided into vertical and horizontal alignment.
The vertical alignment is achieved by ground alignment, which is achieved by
the transformation relationship between normal vectors of the ground point
cloud and the horizontal plane, and the horizontal alignment is achieved by
canopy projection image matching. During image matching, vegetation points are
first distinguished by the ground filtering algorithm, and then, vegetation
points are projected onto the horizontal plane to obtain two binary images. To
match the two images, a matching strategy is used based on canopy shape context
features, which are described by a two-point congruent set and canopy overlap.
Finally, we implement coarse alignment of ULS and ground LiDAR datasets by
combining the results of ground alignment and image matching and finish fine
registration. Also, the effectiveness, accuracy, and efficiency of the proposed
method are demonstrated by field measurements of forest plots. Experimental
results show that the ULS and ground LiDAR data in different plots are
registered, of which the horizontal alignment errors are less than 0.02 m, and
the average runtime of the proposed method is less than 1 second.
