Year: 2025
Type: article
Abstract: This paper introduces a method that can enhance the accuracy and efficiency of point cloud data registration. This method selects the centroid of the point cloud as the feature point and uses the projected distance of this feature point within the dynamic neighborhood to other points as the feature information. Through this feature information, it accomplishes the registration of two sets of point cloud data. This method increases the density and integrity of point cloud data, improves the accuracy and robustness of point cloud registration, and the selection of feature points reduces the computational load thereby enhancing processing efficiency. The introduction of the dynamic neighborhood enables the method to flexibly handle point cloud data of different scales and densities. Experimental results show that the proposed method has good performance in terms of accuracy and efficiency for achieving point cloud data registration and dealing with data under various complex conditions and can effectively improve the effect of point cloud data registration and fusion.
Source: Applied Sciences
DOI: 10.3390/app15074036
Authors: Xinrui Liu, Rutao Wang, Zongsheng Wang
Institution: Shandong University of Science and Technology
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4409219735