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Optimizing plane detection in point clouds through line sampling.

José María Martínez-Otzeta1, Jon Azpiazu2,3, Iñigo Mendialdua4

  • 1Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Manuel Lardizabal 1, 20018, Donostia-San Sebastián, Spain. josemaria.martinezo@ehu.eus.

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Summary
This summary is machine-generated.

This study introduces a novel line-based method for plane detection in 3D point clouds, improving robotic navigation. The new approach enhances accuracy and efficiency compared to traditional Random Sample Consensus (RANSAC) methods.

Keywords:
Plane detectionPoint cloud segmentationRandom sample consensusRobotics

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Area of Science:

  • Robotics and Computer Vision
  • Geometric Data Processing

Background:

  • Plane detection is crucial for mobile robots to navigate and interact with environments.
  • Traditional methods like Random Sample Consensus (RANSAC) have limitations in efficiency and accuracy for complex point clouds.

Purpose of the Study:

  • To develop a more efficient and accurate plane detection algorithm for 3D point clouds in robotics.
  • To improve upon existing RANSAC-based plane detection techniques.

Main Methods:

  • A novel approach focusing on detecting lines by sampling two points at a time from point clouds.
  • Estimating planes by fitting them to pairs of detected lines.
  • Comparative experiments on public and private datasets.

Main Results:

  • The proposed line-based method outperforms traditional RANSAC in plane detection accuracy.
  • The new approach requires fewer iterations than RANSAC for comparable or better results.
  • Demonstrated effectiveness on multiple datasets.

Conclusions:

  • The line-centric plane detection method offers a significant improvement for robotics applications.
  • This technique provides a faster and more robust alternative to RANSAC for environmental interpretation.
  • The developed code is publicly available for further research and application.