Abstract— In this paper we present a comprehensive per-
ception system with applications to mobile manipulation and
grasping for personal robotics. Our approach makes use of
dense 3D point cloud data acquired using stereo vision cameras
by projecting textured light onto the scene. To create models
suitable for grasping, we extract the supporting planes and
model object clusters with different surface geometric primi-
tives. The resultant decoupled primitive point clusters are then
reconstructed as smooth triangular mesh surfaces, and their use
is validated in grasping experiments using OpenRAVE [1]. To
annotate the point cloud data with primitive geometric labels
we make use of our previously proposed Fast Point Feature
Histograms [2] and probabilistic graphical methods (Condi-
tional Random Fields), and obtain a classification accuracy of
98.27% for different object geometries. We show the validity of
our approach by analyzing the proposed system for the problem
of building object models usable in grasping applications with
the PR2 robot. | 