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Dexterous Manipulation Based on Object Recognition and Accurate Pose Estimation Using RGB-D Data.

Udaka A Manawadu1, Naruse Keitaro1

  • 1Graduate School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, Fukushima 965-0006, Japan.

Sensors (Basel, Switzerland)
|November 9, 2024
PubMed
Summary

This study introduces an automated system for industrial valve manipulation, enhancing object recognition and pose estimation accuracy. A novel zone-based strategy improves robotic arm dexterity, even in challenging orientations.

Keywords:
3D object recognition3D pose estimationdexterous manipulationpoint cloud

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Area of Science:

  • Robotics
  • Computer Vision
  • Industrial Automation

Background:

  • Automating industrial valve manipulation requires precise object recognition and pose estimation.
  • Existing systems face challenges with varying object orientations and distances.

Purpose of the Study:

  • To develop an integrated system for object recognition, six-degrees-of-freedom pose estimation, and dexterous manipulation.
  • To enhance the accuracy of pose estimation for industrial valves using multi-perspective point clouds.
  • To create a robust manipulation strategy for challenging scenarios.

Main Methods:

  • An Intel RealSense D435 camera and JACO robotic arm were utilized.
  • Object recognition involved scene segmentation, geometric and model recognition, and dynamic cluster merging.
  • Pose estimation employed the random sample consensus algorithm.
  • A zone-based dexterous manipulation strategy was developed to adjust camera positioning.

Main Results:

  • The system demonstrated reliable performance within acceptable error thresholds for objects within a ±15° view range.
  • Increased errors were observed at extreme orientations and distances, particularly for ball valves.
  • The zone-based manipulation strategy effectively mitigated errors in difficult scenarios, improving reliability.

Conclusions:

  • The integrated system offers improved object recognition and pose estimation for robotic manipulation.
  • The zone-based strategy enhances dexterous manipulation reliability in industrial settings.
  • This research contributes a novel robot motion model for industrial automation.