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Surface-based GICP

Michiel Vlaminck (UGent) , Hiep Luong (UGent) and Wilfried Philips (UGent)
(2018) 2018 15TH CONFERENCE ON COMPUTER AND ROBOT VISION (CRV). p.262-268
Author
Organization
Abstract
In this paper we present an extension of the Generalized ICP algorithm for the registration of point clouds for use in lidar-based SLAM applications. As opposed to the plane-to-plane cost function, which assumes that each point set is locally planar, we propose to incorporate additional information on the underlying surface into the GICP process. Doing so, we are able to deal better with the artefacts that are typically present in lidar point clouds, including an inhomogeneous and sparse point density, noise and missing data. Experiments on lidar sequences of the KITTI benchmark demonstrate that we are able to substantially reduce the positional error compared to the original GICP algorithm.
Keywords
3D registration, point cloud, GICP, surface reconstruction

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Citation

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MLA
Vlaminck, Michiel, et al. “Surface-Based GICP.” 2018 15TH CONFERENCE ON COMPUTER AND ROBOT VISION (CRV), IEEE, 2018, pp. 262–68, doi:10.1109/CRV.2018.00044.
APA
Vlaminck, M., Luong, H., & Philips, W. (2018). Surface-based GICP. 2018 15TH CONFERENCE ON COMPUTER AND ROBOT VISION (CRV), 262–268. https://doi.org/10.1109/CRV.2018.00044
Chicago author-date
Vlaminck, Michiel, Hiep Luong, and Wilfried Philips. 2018. “Surface-Based GICP.” In 2018 15TH CONFERENCE ON COMPUTER AND ROBOT VISION (CRV), 262–68. IEEE. https://doi.org/10.1109/CRV.2018.00044.
Chicago author-date (all authors)
Vlaminck, Michiel, Hiep Luong, and Wilfried Philips. 2018. “Surface-Based GICP.” In 2018 15TH CONFERENCE ON COMPUTER AND ROBOT VISION (CRV), 262–268. IEEE. doi:10.1109/CRV.2018.00044.
Vancouver
1.
Vlaminck M, Luong H, Philips W. Surface-based GICP. In: 2018 15TH CONFERENCE ON COMPUTER AND ROBOT VISION (CRV). IEEE; 2018. p. 262–8.
IEEE
[1]
M. Vlaminck, H. Luong, and W. Philips, “Surface-based GICP,” in 2018 15TH CONFERENCE ON COMPUTER AND ROBOT VISION (CRV), Toronto, CANADA, 2018, pp. 262–268.
@inproceedings{8574433,
  abstract     = {{In this paper we present an extension of the Generalized ICP algorithm for the registration of point clouds for use in lidar-based SLAM applications. As opposed to the plane-to-plane cost function, which assumes that each point set is locally planar, we propose to incorporate additional information on the underlying surface into the GICP process. Doing so, we are able to deal better with the artefacts that are typically present in lidar point clouds, including an inhomogeneous and sparse point density, noise and missing data. Experiments on lidar sequences of the KITTI benchmark demonstrate that we are able to substantially reduce the positional error compared to the original GICP algorithm.}},
  author       = {{Vlaminck, Michiel and Luong, Hiep and Philips, Wilfried}},
  booktitle    = {{2018 15TH CONFERENCE ON COMPUTER AND ROBOT VISION (CRV)}},
  isbn         = {{9781538664810}},
  keywords     = {{3D registration,point cloud,GICP,surface reconstruction}},
  language     = {{eng}},
  location     = {{Toronto, CANADA}},
  pages        = {{262--268}},
  publisher    = {{IEEE}},
  title        = {{Surface-based GICP}},
  url          = {{http://doi.org/10.1109/CRV.2018.00044}},
  year         = {{2018}},
}
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