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

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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.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61471338), Youth Innovation Promotion Association CAS (2015361), Key Research Program of Frontier Sciences, CAS (QYZDY-SSW-SYS004), Beijing Nova Program (z171100001117048), and President Fund of UCAS.

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Authors and Affiliations

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

    Yaru Xian, Jun Xiao & Ying Wang

Authors
  1. Yaru Xian
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  2. Jun Xiao
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  3. Ying Wang
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Corresponding author

Correspondence to Jun Xiao.

Additional information

Yaru Xian received the bachelor’s degree from Beihang University, China in 2013 and the master’s degree from University of Chinese Academy of Sciences, China in 2016. Her research interests include computer graphics, computer vision and image processing.

Jun Xiao is now an associate professor in University of Chinese Academy of Sciences, China. He obtained his PhD degree in the Graduate University of the Chinese Academy of Sciences, China in 2008. His research interests include computer graphics, computer vision, image processing and 3D reconstruction.

YingWang is now a professor in University of Chinese Academy of Sciences, China. She received her PhD degree in Beijing Institute of Technology, China in 1996. Her research interests include computer graphics, visualization and computer vision.

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Xian, Y., Xiao, J. & Wang, Y. A fast registration algorithm of rock point cloud based on spherical projection and feature extraction. Front. Comput. Sci. 13, 170–182 (2019). https://doi.org/10.1007/s11704-016-6191-1

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  • DOI: https://doi.org/10.1007/s11704-016-6191-1

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