  Perception-for-grasping is a challenging problem in robotics. Inexpensive
range sensors such as the Microsoft Kinect provide sensing capabilities that
have given new life to the effort of developing robust and accurate perception
methods for robot grasping. This paper proposes a new approach to localizing
enveloping grasp affordances in 3-D point clouds efficiently. The approach is
based on modeling enveloping grasp affordances as a cylindrical shells that
corresponds to the geometry of the robot hand. A fast and accurate fitting
method for quadratic surfaces is the core of our approach. An evaluation on a
set of cluttered environments shows high precision and recall statistics. Our
results also show that the approach compares favorably with some alternatives,
and that it is efficient enough to be employed for robot grasping in real-time.
