基于三维点云聚类的坡度估计方法

doi:10.3969/j.issn.1000-1158.2018.03.04

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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.

Key words： metrology slope estimation three-dimensional point cloud sparse representation data clustering

Received: 13 April 2017 Published: 12 April 2018

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TB922

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	Articles by authors
	LI Hai-bo
	CAO Yun-feng
	DING Meng
	ZHUANG Li-kui

Cite this article:

LI Hai-bo,CAO Yun-feng,DING Meng, et al. A Method of Slope Estimation Based on Clustering of Three-dimensional Point Cloud[J]. Acta Metrologica Sinica, 2018, 39(3): 304-309.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2018.03.04 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2018/V39/I3/304

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