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Related Experiment Video

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

Chin-Sheng Chen1, Po-Chun Chen2, Chih-Ming Hsu3

  • 1Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei 106, Taiwan. saint@ntut.edu.tw.

Sensors (Basel, Switzerland)
|November 26, 2016
PubMed
Summary

This study introduces a modified viewpoint feature histogram (MVFH) for 3D object recognition and pose estimation. The MVFH descriptor improves accuracy, especially for objects with symmetrical poses, enhancing robotic grasping systems.

Keywords:
Kinect sensoriterative closest pointviewpoint feature histogram descriptorvision-guided robot

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Area of Science:

  • Robotics
  • Computer Vision
  • 3D Perception

Background:

  • Object recognition and pose estimation are crucial for robotic manipulation.
  • Existing methods like the viewpoint feature histogram (VFH) struggle with symmetrical objects.

Purpose of the Study:

  • To develop a novel 3D feature descriptor for robust object recognition and pose estimation.
  • To enhance the accuracy of pose identification for objects with six-degrees-of-freedom in mobile manipulation and grasping applications.

Main Methods:

  • Utilized Microsoft Kinect for 3D point cloud data acquisition.
  • Developed a modified viewpoint feature histogram (MVFH) descriptor combining surface shape and viewpoint direction.
  • Employed iterative closest point for refined pose estimation after initial MVFH recognition.

Main Results:

  • The MVFH descriptor demonstrated improved accuracy in object pose estimation compared to VFH.
  • The method effectively handles objects with symmetrical poses, a limitation of previous approaches.
  • Successful application in vision-guided robotic grasping systems was shown.

Conclusions:

  • The MVFH descriptor offers a reliable solution for 3D object recognition and pose estimation in challenging scenarios.
  • This advancement is vital for enabling more sophisticated and accurate robotic manipulation and grasping.