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HoPE: Horizontal Plane Extractor for Cluttered 3D Scenes.

Zhipeng Dong1, Yi Gao2, Jinfeng Zhang3

  • 1School of Mechanical Engineering and Automation, Northeastern University, NO. 3-11, Wenhua Road, Heping District, Shenyang 110819, China. zhipengdongneu@gmail.com.

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|September 26, 2018
PubMed
Summary
This summary is machine-generated.

HoPE, a Horizontal Plane Extractor, efficiently identifies multiple horizontal planes in cluttered 3D scenes using organized and unorganized point clouds. This method enhances robotic applications by improving robustness, accuracy, and efficiency.

Keywords:
3D data segmentation3D imaging sensor3D point cloudhorizontal plane extractionplane segmentation.

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Area of Science:

  • Robotics
  • Computer Vision
  • Computational Geometry

Background:

  • Extracting horizontal planes is crucial for robotic applications.
  • Existing methods struggle with heavily cluttered 3D scenes.

Purpose of the Study:

  • Introduce HoPE (Horizontal Plane Extractor) for robust horizontal plane extraction.
  • Address limitations of general plane segmentation in cluttered environments.

Main Methods:

  • Transform point clouds to a reference frame using sensor orientation.
  • Employ Z clustering (region growing) and PCA for point clustering and refinement.
  • Utilize nearest neighbor plane matching (NNPM) for plane identity preservation.

Main Results:

  • HoPE effectively extracts multiple horizontal planes from cluttered 3D point clouds.
  • Demonstrated superior performance over state-of-the-art methods in challenging scenarios.
  • Achieved high robustness, accuracy, and efficiency in evaluations.

Conclusions:

  • HoPE offers a significant advancement for horizontal plane extraction in robotics.
  • The publicly available toolbox facilitates practical implementation and further research.