Research on a Point Cloud Registration Method Based on Dynamic Neighborhood Features
Source abstract
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: crossref · Quality: baseline
Research context
OpenAlex topics: Remote Sensing and LiDAR Applications · 3D Surveying and Cultural Heritage · Image Processing and 3D Reconstruction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
References (27)
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