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

Content Provider	IEEE Xplore Digital Library
Author	Donnarumma, F. Lippiello, V. Saveriano, M.
Copyright Year	2012
Description	Author affiliation: CoTeSys Lab, Fakultät für Elektrotechnik und Informationstechnik, Technische Universität München, Karlstrasse, 80333, Munich, Germany (Saveriano, M.) \|\| PRISMA Lab, Dipartimento di Informatica e Sistemistica, Università degli Studi di Napoli Federico II, via Claudio 21, 80125, Naples, Italy (Lippiello, V.) \|\| Istituto di Scienze e Tecnologie della Cognizione, CNR, via S. Martino della Battaglia, 00185, Rome, Italy (Donnarumma, F.)
Abstract	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.
Starting Page	3475
Ending Page	3480
File Size	563817
Page Count	6
File Format	PDF
ISBN	9781467317375
ISSN	21530858
e-ISBN	9781467317368
DOI	10.1109/IROS.2012.6385511
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-07
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Principal component analysis Noise Shape Navigation Robots Accuracy
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Fast Incremental Clustering and Representation of a 3D Point Cloud Sequence with Planar Regions

Laplacian PCA and Its Applications

Fixed-rank representation for unsupervised visual learning

Discriminative Subspace Clustering

Parts-based 3D object classification

Automatic hierarchical classification of silhouettes of 3D objects

Arm gesture recognition and humanoid imitation using functional principal component analysis

InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation

The 3D-3D Registration Problem Revisited

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

Similar Documents

Fast Incremental Clustering and Representation of a 3D Point Cloud Sequence with Planar Regions

Laplacian PCA and Its Applications

Fixed-rank representation for unsupervised visual learning

Discriminative Subspace Clustering

Parts-based 3D object classification

Automatic hierarchical classification of silhouettes of 3D objects

Arm gesture recognition and humanoid imitation using functional principal component analysis

InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation

The 3D-3D Registration Problem Revisited

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