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Sensors 2016, 16(11), 1969; doi:10.3390/s16111969

Three-Dimensional Object Recognition and Registration for Robotic Grasping Systems Using a Modified Viewpoint Feature Histogram

1
Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei 106, Taiwan
2
Department of Mechanical Engineering, National Taipei University of Technology, Taipei 106, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Teen-Hang Meen, Shoou-Jinn Chang, Stephen D. Prior and Artde Donald Kin Tak Lam
Received: 3 June 2016 / Revised: 12 November 2016 / Accepted: 14 November 2016 / Published: 23 November 2016
View Full-Text   |   Download PDF [7101 KB, uploaded 23 November 2016]   |  

Abstract

This paper presents a novel 3D feature descriptor for object recognition and to identify poses when there are six-degrees-of-freedom for mobile manipulation and grasping applications. Firstly, a Microsoft Kinect sensor is used to capture 3D point cloud data. A viewpoint feature histogram (VFH) descriptor for the 3D point cloud data then encodes the geometry and viewpoint, so an object can be simultaneously recognized and registered in a stable pose and the information is stored in a database. The VFH is robust to a large degree of surface noise and missing depth information so it is reliable for stereo data. However, the pose estimation for an object fails when the object is placed symmetrically to the viewpoint. To overcome this problem, this study proposes a modified viewpoint feature histogram (MVFH) descriptor that consists of two parts: a surface shape component that comprises an extended fast point feature histogram and an extended viewpoint direction component. The MVFH descriptor characterizes an object’s pose and enhances the system’s ability to identify objects with mirrored poses. Finally, the refined pose is further estimated using an iterative closest point when the object has been recognized and the pose roughly estimated by the MVFH descriptor and it has been registered on a database. The estimation results demonstrate that the MVFH feature descriptor allows more accurate pose estimation. The experiments also show that the proposed method can be applied in vision-guided robotic grasping systems. View Full-Text
Keywords: vision-guided robot; Kinect sensor; viewpoint feature histogram descriptor; iterative closest point vision-guided robot; Kinect sensor; viewpoint feature histogram descriptor; iterative closest point
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chen, C.-S.; Chen, P.-C.; Hsu, C.-M. Three-Dimensional Object Recognition and Registration for Robotic Grasping Systems Using a Modified Viewpoint Feature Histogram. Sensors 2016, 16, 1969.

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