Efficient nonparametric belief propagation for pose estimation and manipulation of articulated objects

Karthik Desingh; Shiyang Lu; Anthony Opipari; Odest Chadwicke Jenkins

doi:10.1126/scirobotics.aaw4523

Skip to main content

ScienceMag.org

Search

Log in to view PDF

via AAAS login

AAAS login provides access to Science for AAAS members, and access to other journals in the Science family to users who have purchased individual subscriptions.

Log in through your institution

More options

Purchase digital access to this article

Download and print this article for your personal scholarly, research, and educational use.

Purchase this issue in print

Buy a single issue of Science for just $15 USD.

View Full Text

Science Robotics

Vol 4, Issue 30
29 May 2019

Table of Contents

Article Tools

Email

Thank you for your interest in spreading the word about Science Robotics.
NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Your Email

Your Name

Send To
Enter multiple addresses on separate lines or separate them with commas.

You are going to email the following Efficient nonparametric belief propagation for pose estimation and manipulation of articulated objects

Message Subject (Your Name) has forwarded a page to you from Science Robotics

Message Body (Your Name) thought you would like to see this page from the Science Robotics web site.

Your Personal Message
Download Powerpoint
Print
Save to my folders

Log In with your AAAS Login
Alerts

Please log in to add an alert for this article.

Log In with your AAAS Login

Sign In to Email Alerts with your Email Address
Email
Citation tools
Efficient nonparametric belief propagation for pose estimation and manipulation of articulated objects

By Karthik Desingh, Shiyang Lu, Anthony Opipari, Odest Chadwicke Jenkins

Science Robotics22 May 2019

A “pull” message passing algorithm is an efficient nonparametric belief propagation for pose estimation of articulated objects.

Supplementary Materials

Citation Manager Formats

BibTeX

Bookends

EasyBib

EndNote (tagged)

EndNote 8 (xml)

Medlars

Mendeley

Papers

RefWorks Tagged

Ref Manager

RIS

Zotero
Share
Efficient nonparametric belief propagation for pose estimation and manipulation of articulated objects

By Karthik Desingh, Shiyang Lu, Anthony Opipari, Odest Chadwicke Jenkins

Science Robotics22 May 2019

A “pull” message passing algorithm is an efficient nonparametric belief propagation for pose estimation of articulated objects.

Supplementary Materials
Share This Article:

Efficient nonparametric belief propagation for pose estimation and manipulation of articulated objects

Log in to view PDF

Log in through your institution

Log in through your institution

More options

Science Robotics

Article Tools

Efficient nonparametric belief propagation for pose estimation and manipulation of articulated objects

Citation Manager Formats

Efficient nonparametric belief propagation for pose estimation and manipulation of articulated objects

Stay Connected to Science Robotics

Citing Articles in:

share

Log in to view PDF

Log in through your institution

Log in through your institution

More options

Science Robotics

Article Tools

Efficient nonparametric belief propagation for pose estimation and manipulation of articulated objects

Citation Manager Formats

Efficient nonparametric belief propagation for pose estimation and manipulation of articulated objects

Stay Connected to Science Robotics

Related Content

Similar Articles in:

Citing Articles in:

Read the Latest Issue of Science

21 February 2020

Feature

Biosecurity

Neuroscience

SCI COMMUN

Climate

Working Life