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Related Experiment Video

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Photorealistic Learned Landscapes for Augmented Reality
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Object recognition and localization from 3D point clouds by maximum-likelihood estimation.

Harshana G Dantanarayana1, Jonathan M Huntley1

  • 1Loughborough University, Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough, Leicestershire, LE11 3TU, UK.

Royal Society Open Science
|September 8, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel maximum-likelihood algorithm for object recognition and pose estimation using 3D point clouds. The method efficiently utilizes surface features, offering a unified approach for both initial estimation and refinement with high accuracy.

Keywords:
fringe projection 3D scanningindustrial inspectionobject recognitionpose estimation

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Area of Science:

  • Computer Vision
  • Robotics
  • Geometric Modeling

Background:

  • Object recognition and pose estimation are crucial in robotics and computer vision.
  • Existing methods often rely on interest points or separate algorithms for initial estimation and refinement.
  • Surface segmentation from depth images offers rich feature information often overlooked by other techniques.

Purpose of the Study:

  • To develop a unified, efficient algorithm for automated object recognition and pose estimation from 3D point clouds.
  • To leverage surface segmentation features for improved performance compared to interest point-based methods.
  • To provide a single approach for both initial pose estimation and subsequent pose refinement.

Main Methods:

  • A maximum-likelihood analysis algorithm is proposed.
  • Surfaces segmented from depth images are used as primary features.
  • The algorithm is demonstrated with a 2 degrees of freedom (d.f.) example and a full 6 d.f. analysis.

Main Results:

  • The algorithm exhibits negligible memory requirements compared to the 6D Hough transform.
  • It is computationally efficient, outperforming iterative closest point algorithms.
  • Achieved an RMS alignment error as low as 0.3 mm in a cluttered scene analysis.

Conclusions:

  • The presented maximum-likelihood algorithm offers a unified and efficient solution for object recognition and pose estimation.
  • Utilizing surface features provides a robust alternative to interest point-based methods.
  • The method's applicability spans from initial recognition to precise pose refinement with high accuracy.