Fast incremental clustering and representation of a 3D point cloud sequence with planar regions

An incremental clustering technique to partition 3D point clouds into planar regions is presented in this paper. The algorithm works in real-time on unknown and noisy data, without any initial assumption. An iterative cluster growing technique is proposed in order to correctly classify a flow of 3D points and to merge close regions. The computational efficiency of the approach is achieved by using an Incremental Principal Component Analysis (IPCA) technique, and with the adoption of a compact geometrical representation based on the concave-hull computation of each cluster. This solution adds a more realistic representation of the observed environment and reduces the number of points needed to identify the cluster shape. The effectiveness of the proposed algorithm has been validated with both synthetic and real data sets. © 2012 IEEE.

Tipo Pubblicazione: 
Contributo in atti di convegno
Author or Creator: 
Donnarumma, Francesco
Lippiello, Vincenzo
Saveriano, Matteo
Publisher: 
Institute of Electrical and Electronics Engineers,, New York, NY , Stati Uniti d'America
Source: 
Intelligent Robots and Systems (IROS), pp. 3475–3480, 2012
Date: 
2012
Resource Identifier: 
http://www.cnr.it/prodotto/i/313881
https://dx.doi.org/10.1109/IROS.2012.6385511
info:doi:10.1109/IROS.2012.6385511
http://www.scopus.com/record/display.url?eid=2-s2.0-84872340920&origin=inward
urn:isbn:9781467317375
Language: 
Eng
ISTC Author: