This research presents a novel algorithm for the initial enrollment of threedimensional point clouds, addressing the issue of accuracy enrollment algorithms, such
as the Iterative Closest Point (ICP), being prone to local optima in point cloud
enrollment. The proposed method employs a filtering technique to preprocess the point
cloud data, followed by establishing an angular shift model using the centre-of-mass
and mass center of the point cloud data. An iterative rotation model is then constructed
to determine the optimal angular shift, enabling the completion of the initial
enrollment. Furthermore, the effectiveness of the initial enrollment algorithm is
validated by comparing it with the conventional center-of-gravity-based initial
enrollment method, along with a subsequent accuracy enrollment using the ICP
algorithm. Comparative experiments demonstrate the superior performance of the
proposed algorithm in terms of initial enrollment effectiveness.
Keywords: Angular shift model, Centre-of-mass, Centering, Iterative Closest Point (ICP), Initial enrollment, Three-dimensional point clouds.
