Perception for mobile manipulation and grasping using active stereo

Abstract

In this paper we present a comprehensive perception 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 primitives. The resultant decoupled primitive point clusters are then reconstructed as smooth triangular mesh surfaces, and their use is validated in grasping experiments using OpenRAVE . To annotate the point cloud data with primitive geometric labels we make use of our previously proposed Fast Point Feature Histograms and probabilistic graphical methods (Conditional 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 (see Figure 1).