A fast registration algorithm of rock point cloud based on spherical projection and feature extraction

doi:10.1007/s11704-016-6191-1

Front. Comput. Sci.

2019, Vol. 13

Issue (1) : 170-182 https://doi.org/10.1007/s11704-016-6191-1

RESEARCH ARTICLE

A fast registration algorithm of rock point cloud based on spherical projection and feature extraction

Yaru XIAN, Jun XIAO(

), Ying WANG

School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China

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Abstract

Point cloud registration is an essential step in the process of 3D reconstruction. In this paper, a fast registration algorithm of rock mass point cloud is proposed based on the improved iterative closest point (ICP) algorithm. In our proposed algorithm, the point cloud data of single station scanner is transformed into digital images by spherical polar coordinates, then image features are extracted and edge points are removed, the features used in this algorithm is scale-invariant feature transform (SIFT). By analyzing the corresponding relationship between digital images and 3D points, the 3D feature points are extracted, from which we can search for the two-way correspondence as candidates. After the false matches are eliminated by the exhaustive search method based on random sampling, the transformation is computed via the Levenberg-Marquardt-Iterative Closest Point (LM-ICP) algorithm. Experiments on real data of rock mass show that the proposed algorithm has the similar accuracy and better registration efficiency compared with the ICP algorithm and other algorithms.

Keywords rock point cloud registration LM-ICP spherical projection feature extraction

Corresponding Author(s): Jun XIAO

Just Accepted Date: 30 September 2016 Online First Date: 20 December 2017 Issue Date: 31 January 2019

Cite this article:

Yaru XIAN,Jun XIAO,Ying WANG. A fast registration algorithm of rock point cloud based on spherical projection and feature extraction[J]. Front. Comput. Sci., 2019, 13(1): 170-182.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-016-6191-1
https://academic.hep.com.cn/fcs/EN/Y2019/V13/I1/170

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