  Point-cloud data acquired using a terrestrial laser scanner (TLS) play an
important role in digital forestry research. Multiple scans are generally used
to overcome occlusion effects and obtain complete tree structural information.
However, it is time-consuming and difficult to place artificial reflectors in a
forest with complex terrain for marker-based registration, a process that
reduces registration automation and efficiency. In this study, we propose an
automatic coarse-to-fine method for the registration of point-cloud data from
multiple scans of a single tree. In coarse registration, point clouds produced
by each scan are projected onto a spherical surface to generate a series of
two-dimensional (2D) images, which are used to estimate the initial positions
of multiple scans. Corresponding feature-point pairs are then extracted from
these series of 2D images. In fine registration, point-cloud data slicing and
fitting methods are used to extract corresponding central stem and branch
centers for use as tie points to calculate fine transformation parameters. To
evaluate the accuracy of registration results, we propose a model of error
evaluation via calculating the distances between center points from
corresponding branches in adjacent scans. For accurate evaluation, we conducted
experiments on two simulated trees and a real-world tree. Average registration
errors of the proposed method were 0.26m around on simulated tree point clouds,
and 0.05m around on real-world tree point cloud.
