Probabilistic normal distributions transform representation for accurate 3D point cloud registration

@article{Hong2017ProbabilisticND,
  title={Probabilistic normal distributions transform representation for accurate 3D point cloud registration},
  author={Hyunki Hong and B. Haynes Lee},
  journal={2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2017},
  pages={3333-3338}
}
This paper presents a probabilistic normal distributions transform (NDT) representation which improves the accuracy of point cloud registration by using the probabilities of point samples. Since conventional NDT does not generate distributions in cells having fewer point samples than the number threshold, it would be failed to represent the environment if the point cloud is divided by high-resolution cells. Also, it can lead to incorrect estimations of pose variations. To solve the problem, we… CONTINUE READING

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SHOWING 1-10 OF 12 REFERENCES

Are we ready for autonomous driving? The KITTI vision benchmark suite

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition
  • 2012
VIEW 2 EXCERPTS

The three-dimensional normal-distributions transform: an efficient representation for registration, surface analysis, and loop detection

M. Magnusson
  • Ph.D. dissertation, Örebro universitet, 2009.
  • 2009
VIEW 1 EXCERPT

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