Research on pose estimation for stereo vision measurement system by an improved method: uncertainty weighted...

Area of Science:

• Computer Vision
• Robotics
• 3D Reconstruction

Background:

• Pose estimation is crucial for stereo vision systems.
• Existing methods struggle with feature point uncertainty.
• Accurate 3D measurement requires robust pose solutions.

Purpose of the Study:

• To introduce a novel algorithm for stereo vision pose estimation.
• To address the challenge of feature point direction uncertainty.
• To improve the accuracy and reliability of stereo vision measurements.

Main Methods:

• Utilized a covariance matrix to represent feature point uncertainty.
• Integrated uncertainty into pose estimation using a projection matrix.
• Employed singular value decomposition for attitude matrix calculation.
• Iteratively optimized pose parameters with fixed camera constraints.

Main Results:

• The proposed UWSPSM algorithm demonstrates rapid convergence.
• Achieved high precision and robustness in pose estimation.
• The method effectively tolerates varying degrees of error uncertainty.
• Experimental results validate theoretical convergence proofs.

Conclusions:

• UWSPSM offers a reliable solution for stereo vision pose estimation.
• The algorithm enhances measurement accuracy and efficiency.
• It shows significant potential for practical applications in 3D measurement.