Technical University of Munich

@inproceedings{rusu_persistent_2008,
 author = {RB Rusu and ZC Marton and N Blodow and M Beetz},
 title = {Persistent Point Feature Histograms for {3D} Point Clouds},
 booktitle = {Proceedings of the 10th International Conference on Intelligent Autonomous
	Systems ({IAS-10)}, Baden-Baden, Germany},
 year = {2008},
 abstract = {This paper proposes a novel way of characterizing the local geometry
	of {3D} points, using persistent feature histograms. The relationships
	between the neighbors of a point are analyzed and the resulted values
	are stored in a 16-bin histogram. The histograms are pose and point
	cloud density invariant and cope well with noisy datasets. We show
	that geometric primitives have unique signatures in this feature
	space, preserved even in the presence of additive noise. To extract
	a compact subset of points which characterizes a point cloud dataset,
	we perform an in-depth analysis of all point feature histograms using
	different distance metrics. Preliminary results show that point clouds
	can be roughly segmented based on the uniqueness of geometric primitives
	feature histograms. We validate our approach on datasets acquired.
	from laser sensors in indoor (kitchen) environments.},
}