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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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A Shape Reconstruction and Measurement Method for Spherical Hedges Using Binocular Vision.

Yawei Zhang1, Jin Gu1, Tao Rao1

  • 1College of Engineering, China Agricultural University, Beijing, China.

Frontiers in Plant Science
|May 23, 2022
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Summary

This study presents a binocular vision system for accurately measuring spherical hedge dimensions, crucial for automated pruning. The system achieves high precision in laboratory and outdoor settings, supporting robotic trimming advancements.

Keywords:
3D point cloudbinocular visiondimension measurementshape reconstructionspherical hedges

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Robotics

Background:

  • Automatic pruning requires precise phenotypic feature detection, specifically the center coordinate and radius of spherical hedges.
  • Existing methods may lack the accuracy and efficiency needed for real-time robotic applications.

Purpose of the Study:

  • To develop and validate a binocular vision-based system for reconstructing and measuring the 3D shape of spherical hedges.
  • To provide technical support for phenotypic feature detection in automatic trimming research.

Main Methods:

  • Utilized parallel binocular cameras for image acquisition.
  • Employed region segmentation and object extraction to obtain 2D coordinates.
  • Implemented a stereo correction algorithm and an improved semi-global block matching (SGBM) algorithm to generate a disparity map.
  • Calculated the 3D point cloud from the disparity map and camera geometry to determine hedge center coordinates and radius.

Main Results:

  • Laboratory tests showed average radius errors of 1.58 mm (0.53% relative error) and average location deviation of 15.92 mm within 2,000-2,600 mm detection range.
  • Outdoor tests yielded average radius errors of 4.02 mm (0.44% relative error) and average location deviation of 18.29 mm.
  • The system demonstrated reliable performance in both controlled and real-world environments.

Conclusions:

  • The developed binocular vision system accurately measures spherical hedge center coordinates and radius.
  • This technology offers significant potential for advancing automated pruning systems in agriculture and horticulture.
  • The system provides essential data for precise robotic manipulation and trimming operations.