ICRA 2012 Paper Abstract

Close

Paper WeD07.5

Tang, Jie (University of California, Berkeley), Miller, Stephen (Stanford University), Singh, Arjun (University of California, Berkeley), Abbeel, Pieter (UC Berkeley)

A Textured Object Recognition Pipeline for Color and Depth Image Data

Scheduled for presentation during the Invited Session "Results of ICRA 2011 Robot Challenge" (WeD07), Wednesday, May 16, 2012, 17:30−17:45, Meeting Room 7 (Remnicha)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on June 18, 2018

Keywords Computer Vision for Robotics and Automation, Recognition, Visual Learning

Abstract

We present an object recognition system which leverages the additional sensing and calibration information available in a robotics setting together with large amounts of training data to build high fidelity object models for a dataset of textured household objects. We then demonstrate how these models can be used for highly accurate detection and pose estimation in an end-to-end robotic perception system incorporating simultaneous segmentation, object classification, and pose fitting. The system can handle occlusions, illumination changes, multiple objects, and multiple instances of the same object. The system placed first in the ICRA 2011 Solutions in Perception instance recognition challenge. We believe the presented paradigm of building rich 3D models at training time and including depth information at test time is a promising direction for practical robotic perception systems.

 

 

Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2018 PaperCept, Inc.
Page generated 2018-06-18  00:49:46 PST  Terms of use