A Point Cloud Matching Algorithm for Large-scale Scenarios
A new point cloud matching method is proposed to address the issues of slow speed and inconsistent matching results in traditional algorithms in large-scale point cloud matching.This method first uses the KD tree to find the point with the minimum depth in point cloud and uses it as a seed point.Then,an improved region growth segmentation algorithm improving in depth information and curvature is used to extract the upper surface area of point cloud,and the point cloud boundary is extracted in this area.Finally,the point cloud matching algorithm is validated using improved point pair features.The experimental results show that compared with traditional algorithms,the proposed method has significantly improved matching speed and consistency of matching results,and has practical application value in handling large-scale point cloud matching.
large-scale point cloudKD treeimproved region growth segmentation algorithmpoint pair feature
国家科技期刊平台
NSTL
万方数据
维普
