计量学报2018，Vol.39Issue(3)：304-309,6.DOI:10.3969/j.issn.1000-1158.2018.03.04
基于三维点云聚类的坡度估计方法
A Method of Slope Estimation Based on Clustering of Three-dimensional Point Cloud
摘要
Abstract
In order to improve the precision of slope estimation in Mars landing,a method of slope estimation based on clustering of three-dimensional point cloud and random searching the optimal fitting plane was presented.The three-dimensional point cloud data obtained by light detection and ranging (LIDAR) were addressed with sparse representation.Then the data were clustered and segmented according to the sparse coefficients.So the subspaces were determined and the data points in subspace were used to fit plane.The optimal plane can be obtained by random search and the angle between the normal vectors can be got by calculation.The angle obtained equals the slope angle in value.So far,the slope angle estimation was completed.The experiments show that this method can estimate the slope angle very accurately.Compared with the common estimation method,this algorithm has low relative error.关键词
计量学/坡度估计/三维点云/稀疏表示/数据聚类Key words
metrology/slope estimation/three-dimensional point cloud/sparse representation/data clustering分类
通用工业技术引用本文复制引用
李海波,曹云峰,丁萌,庄丽葵..基于三维点云聚类的坡度估计方法[J].计量学报,2018,39(3):304-309,6.基金项目
国家自然科学基金(61673211) （61673211）
航天科学基金(2014320003010432) （2014320003010432）
江苏省研究生培养创新工程(KYLX_0282) （KYLX_0282）
