You are in : Home » Results & submissions » Select UOA » 13 - Electrical and Electronic Engineering, Metallurgy and Materials » View submission: University of Surrey » Outputs » Detail

Output details

13 - Electrical and Electronic Engineering, Metallurgy and Materials

University of Surrey

Return to search Previous output Next output

Output 0 of 0 in the submission

Article title

A Probabilistic Framework for 3D Visual Object Representation

Type

D - Journal article

DOI

10.1109/TPAMI.2009.64

Title of journal

IEEE Transactions on Pattern Analysis and Machine Intelligence

Article number

Volume number

Issue number

First page of article

1790

ISSN of journal

0162-8828

Year of publication

2009

URL

Number of additional authors

Additional information

This paper presents a novel statistical approach to learning 3D visual object representation for 6D pose estimation. This approach was an essential component of the PACOPLUS project and allowed a robot to discover new objects by autonomous manipulation and learning their shape over time. This research enables automonous object learning for robots and helps move robots from the lab to urban environments. This approach has been used in EU projects Xperience (http://www.xperience.org) and Intellact (http://www.intellact.eu/). Long term exploitation includes implementation for the deployment of service and care robotics, and further projects are in preparation with industrial partners.

Interdisciplinary

Cross-referral requested

Research group

None

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract