Pose estimation in industrial machine vision systems under sensing dynamics: A statistical learning approach

Keyphrases

• Computer Vision System 100%
• Learning Approaches 100%
• Statistical Learning 100%
• Pose Estimation 100%
• Industrial Vision 100%
• Sensing Dynamics 100%
• Target Dynamics 100%
• Target Motion 50%
• Visual Servoing 50%
• Dynamic Model 25%
• Measurement Capability 25%
• Expectation-maximization Algorithm 25%
• Extended Kalman Filter 25%
• Estimation Problem 25%
• Modelling Problems 25%
• Convex Optimization Method 25%
• Block Coordinate Descent 25%
• Motion Characteristics 25%
• Slow Sensors 25%
• Sensor Dynamics 25%
• 6-DOF Industrial Robot 25%
• Limited Measurements 25%
• Target Pose 25%

Computer Science

• Machine Vision 100%
• Learning Approach 100%
• Pose Estimation 100%
• Convex Optimization 33%
• Industrial Robot 33%
• Extended Kalman Filter 33%
• Motion Characteristic 33%

Engineering

• Learning Approach 100%
• Pose Estimation 100%
• Tasks 33%
• Optimization Method 33%
• Moving Target 33%
• Industrial Robot 33%
• Expectation Maximization Algorithm 33%
• Extended Kalman Filter 33%
• Target Pose 33%