Three-Dimensional Point Cloud Initial Enrollment Algorithm Based on Centre-of-mass and Centering

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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.