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Foundations of Robotics
Seminar, November 5, 2008
Time
and Place | Seminar Abstract
Object recognition and full pose estimation in cluttered environments
Alvaro Collet
Carnegie Mellon University - Robotics Institute
NSH 1507
Talk 4:00 pm
Robust perception is a vital capability for robotic manipulation in
unstructured scenes. In this context, full pose estimation of relevant
objects in a scene is a critical step towards the introduction of
robots into household environments. In this talk, we present an
approach for building metric 3D models of objects using local
descriptors from several images. Each model is optimized to fit a set
of calibrated training images, thus obtaining the best possible
alignment between the 3D model and the real object. Given a new test
image, we match the local descriptors to our stored models online,
using a novel combination of the RANSAC and Mean Shift algorithms to
register multiple instances of each object. A robust initialization
step allows for arbitrary rotation, translation and scaling of objects
in the test images. The resulting system provides markerless 6-DOF
pose estimation for complex objects in cluttered scenes. We provide
experimental results demonstrating orientation and translation
accuracy, as well a physical implementation of the pose output being
used by an autonomous robot to perform grasping in highly cluttered
scenes.
The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.