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Related Experiment Videos

Range image segmentation into planar and quadric surfaces using an improved robust estimator and genetic algorithm.

Paulo Fabiano Urnau Gotardo1, Olga Regina Pereira Bellon, Kim L Boyer

  • 1IMAGO Research Group, Departamento de Informática, Universidade Federal do Paraná, Curitiba (PR), Brazil. gotardop@ece.osu.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|December 29, 2004
PubMed
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This study introduces a new range image segmentation method using an improved robust estimator and genetic algorithm for accurate surface extraction. This approach enhances obstacle detection in real-time applications like autonomous navigation.

Area of Science:

  • Computer Vision
  • Geometric Modeling
  • Robotics

Background:

  • Range image segmentation is crucial for understanding 3D environments.
  • Existing methods struggle with noisy data and complex structures.
  • Accurate surface extraction is needed for tasks like obstacle detection.

Purpose of the Study:

  • To develop a novel range image segmentation method for robustly detecting planar and quadric surfaces.
  • To improve the accuracy and efficiency of surface extraction from noisy range data.
  • To enable real-time applications in man-made environments.

Main Methods:

  • An improved robust estimator extending MSAC/RANSAC with local surface orientation.
  • An efficient approximation for geometric distance to quadric surfaces.

Related Experiment Videos

  • A genetic algorithm for accelerated optimization and convergence.

    • Main Results:

    • The method accurately detects and extracts planar and quadric surfaces from noisy range data.
    • Quantitative evaluation shows favorable performance against eleven other segmenters.
    • The algorithm demonstrates effectiveness on three real-world range image databases.

    Conclusions:

    • The proposed segmentation method offers enhanced accuracy and efficiency.
    • It is suitable for real-time applications, including autonomous navigation and target detection.
    • The approach can be extended to process registered point clouds.