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Binocular laser scanning imaging-based disordered object recognition method.

Jican Tian, Liming Zhao, Wenlong Zhou

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Summary

This summary is machine-generated.

This study introduces a novel binocular laser 3D scanning method for industrial robot object recognition, improving 3D reconstruction and pose estimation for cluttered scenes. The new approach enhances grasping success rates in complex environments.

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Area of Science:

Robotics
Computer Vision
3D Imaging

Background:

Industrial robot grasping of disordered objects presents challenges due to complex structures and occlusion.
Existing 3D reconstruction methods suffer from incomplete data caused by self-obscuration and mutual occlusion in stacked scenes.

Purpose of the Study:

To develop an object recognition method using binocular laser 3D scanning for improved grasping of disordered objects.
To enhance 3D reconstruction completeness and object pose estimation accuracy in cluttered environments.

Main Methods:

A binocular laser scanning 3D imaging method with rapid multi-source point cloud fusion for complete 3D reconstruction.
An improved point pair feature (PPF) based pose estimation algorithm incorporating hidden point removal (HPR) and farthest point sampling (FPS).
Iterative closest point (ICP) algorithm for pose optimization.

Main Results:

Significantly improved object information reconstruction completeness compared to original binocular vision and monocular laser vision systems.
Reduced recognition time and average error compared to the original PPF algorithm.
Achieved a 100% matching success rate in 20 cluttered scenes and a 93.3% grasping success rate in actual experiments.

Conclusions:

The proposed binocular laser 3D scanning and improved PPF method effectively addresses challenges in disordered object grasping.
The method demonstrates high accuracy and robustness in cluttered and occluded scenarios, validating its practical applicability in industrial robotics.