Cloud-based robot grasping with the google object recognition engine
Venue
IEEE Int’l Conf. on Robotics and Automation (2013), pp. 8
Publication Year
2013
Authors
Ben Kehoe, Akihiro Matsukawa, Sal Candido, James Kuffner, Ken Goldberg
BibTeX
Abstract
Rapidly expanding internet resources and wireless networking have potential to
liberate robots and automation systems from limited onboard computation, memory,
and software. "Cloud Robotics" describes an approach that recognizes the wide
availability of networking and incorporates opensource elements to greatly extend
earlier concepts of "Online Robots" and "Networked Robots". In this paper we
consider how cloud-based data and computation can facilitate 3D robot grasping. We
present a system architecture, implemented prototype, and initial experimental data
for a cloud-based robot grasping system that incorporates a Willow Garage PR2 robot
with onboard color and depth cameras, Google’s proprietary object recognition
engine, the Point Cloud Library (PCL) for pose estimation, Columbia University’s
GraspIt! toolkit and OpenRAVE for 3D grasping and our prior approach to
sampling-based grasp analysis to address uncertainty in pose. We report data from
experiments in recognition (a recall rate of 80% for the objects in our test set),
pose estimation (failure rate under 14%), and grasping (failure rate under 23%) and
initial results on recall and false positives in larger data sets using confidence
measures.
