by RB Rusu, ZC Marton, N Blodow and M Beetz
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.
Reference:
Persistent Point Feature Histograms for 3D Point Clouds (RB Rusu, ZC Marton, N Blodow and M Beetz), In Proceedings of the 10th International Conference on Intelligent Autonomous Systems (IAS-10), Baden-Baden, Germany, 2008.
Bibtex Entry:
@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.},
}