Aligning point cloud views using persistent feature histograms

@article{Rusu2008AligningPC,
  title={Aligning point cloud views using persistent feature histograms},
  author={Radu Bogdan Rusu and Nico Blodow and Zolt{\'a}n-Csaba M{\'a}rton and Michael Beetz},
  journal={2008 IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year={2008},
  pages={3384-3391}
}
  • R. RusuNico Blodow M. Beetz
  • Published 14 October 2008
  • Computer Science
  • 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
In this paper we investigate the usage of persistent point feature histograms for the problem of aligning point cloud data views into a consistent global model. Given a collection of noisy point clouds, our algorithm estimates a set of robust 16D features which describe the geometry of each point locally. By analyzing the persistence of the features at different scales, we extract an optimal set which best characterizes a given point cloud. The resulted persistent features are used in an… 

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