6-DOF Displacement Measurement For Moing Objects Based On Continuous Images

Intelligent manufacturing has been widely concerned because of Made in China 2025.The 6-DOF(six degree of freedom)displacement measurement of moving objects have became one of the most improtant research in intelligent manufacturing.Visual measurement is one of the best method for 6-DOF measurement.The algorithm of visual measurement has some limitations because that the requirements of the present precision is improving.Therefore,this paper studies the 6-DOF displacement measurement method of moving object based on dynamic image information.The key points of researches is the displacement measurement method based on image feature points and image gray values.The feature points of the image contain image information,which is the reason why it has been selected to measure the 6-DOF displacement.The chessboard corner point has been choosed by simple construction,convenient fabrication and small error.The mathematical model between the image coordinates of the feature points and the displacement of 6-DOF is established according to the imaging model of the camera and the coordinate transformation relation.The mathematical model is linearized with high precision based on the continuity of motion,and the 6-DOF displacement can be solved.The displacement measurement algorithm based on the feature points is simulated and verified from single DOF,3-DOF and 6-DOF.The experimental platform is built to verify the measurement system.The experimental results show that the 6-DOF displacement measurement system based on image feature points is accurate and feasible.Image feature points only account for a small part of the image,lots of image information is wasted.The advantage of establishing the measuremengt model according to grat value is that each image point can establish the corresponding measurement equation.This paper presents a displacement measurement method based on image gray value.The algorithm based on image gray value is simulated.The result shows the accuracy and effectiveness of the algorithm.