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Multi-View-Based Pose Estimation and Its Applications on Intelligent Manufacturing.

Haiwei Yang¹, Peilin Jiang¹, Fei Wang¹

¹School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Sensors (Basel, Switzerland)

|September 10, 2020

Summary

This study introduces a multi-view pose estimation method for accurately determining the six-degree-of-freedom pose of large-scale targets. This approach enhances stability and efficiency in industrial applications.

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Area of Science:

Computer Vision
Robotics
Industrial Automation

Background:

Pose estimation is crucial for image comparison and fusion.
Existing algorithms face challenges in efficiency, complexity, and accuracy for diverse targets and conditions.
Accurate pose estimation is vital for industrial applications and manufacturing.

Purpose of the Study:

To propose a novel multi-view-based pose estimation method.
To address the limitations of current methods for large-scale targets.
To improve accuracy, stability, and efficiency in pose estimation.

Main Methods:

A multi-view approach utilizing different positions and angles to observe features of large-size parts.
Estimating the six-degree-of-freedom pose of the entire large-size parts by integrating information from multiple views.

Keywords:

intelligent manufacturing multiple views pose estimation

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Developing a new visual guidance system for intelligent manufacturing based on the proposed method.

Main Results:

The proposed method effectively solves pose estimation for large-scale targets.
Achieved good performance in accuracy and stability compared to existing methods.
Experimental results confirm accurate six-degree-of-freedom pose estimation for various targets.

Conclusions:

The multi-view pose estimation method is effective for large-scale targets, offering high accuracy and stability.
The developed visual guidance system is successfully applied in intelligent manufacturing, particularly in automobile production.
The system demonstrates high accuracy, efficiency, and low cost, making it valuable for industrial lines.