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Filtering Organized 3D Point Clouds for Bin Picking Applications.

Marek Franaszek1, Prem Rachakonda1, Kamel S Saidi1

  • 1National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.

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|April 3, 2024
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Summary
This summary is machine-generated.

This study introduces a novel filtering technique to remove outlier points from 3D data in robotic bin-picking. The new method significantly improves outlier removal efficacy in cluttered manufacturing scenes compared to existing procedures.

Keywords:
bin pickingfiltering 3D point cloudsegmentationstatistical outlier removal

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Area of Science:

  • Robotics
  • Computer Vision
  • Perception Systems

Background:

  • Robotic bin-picking relies on perception systems for object identification.
  • Spurious 3D data points (outliers) contaminate perception data, hindering obstacle avoidance and part segmentation.
  • Existing outlier removal methods, often designed for outdoor scenarios, perform poorly with organized 3D point clouds from manufacturing environments.

Purpose of the Study:

  • To develop and present a new filtering technique for outlier removal in organized 3D point clouds specific to robotic bin-picking tasks.
  • To address the limitations of generic outlier removal procedures in cluttered manufacturing scenes.

Main Methods:

  • A novel filtering technique was developed for organized 3D point clouds.
  • The technique was specifically designed to handle cluttered scenes typical in bin-picking.
  • Performance was evaluated against a generic statistical outlier removal procedure using six diverse datasets.

Main Results:

  • The new filtering technique demonstrated superior outlier removal efficacy compared to the generic statistical outlier removal procedure.
  • The method showed particular effectiveness on datasets with a high density of outliers.
  • Improved performance was observed in the context of robotic bin-picking applications.

Conclusions:

  • The proposed filtering technique is highly effective for removing outliers from organized 3D point clouds in robotic bin-picking.
  • This advancement can enhance the reliability and efficiency of autonomous robot operations in manufacturing.
  • The method offers a specialized solution for perception challenges in cluttered industrial environments.