Research On Mono-vision Pose Measurement Based On Features Of Target

Mono-vision measuring system is an essential consisting part of visionmeasuring region. Mono-vision measuring system is widely adopted by RVD(Rendezvous and Docking), auto detection machine for its simplicity, highprecision. In order to extend the application of mono-vision system to robotic arm,robotic hand, RVD, the research is carried out based on both cooperative target andnon-cooperative target.Measuring system based on cooperative target is consisted by camera,cooperative target, feature extraction algorithm, position and orientation resolvingalgorithm, laser system and control system, among all of which, feature extractionand pose resolving algorithms are the most important parts. Image contains target issubtracted by background image without the target when feature points areextracted during RVD is in operation. Pose resolving algorithm by three featurepoints is regularly adopted by cooperative target measuring system. Precision ofthree points algorithm is influenced mainly by6aspects, such as measurementdistant, feature points character, and precision of feature point extraction.There is no cooperative target in many working conditions, for example, Hubblespace telescope maintenance on orbit, size measurement of workpiece, and licenseplate number detection. In order to extend pose measuring system to thoseconditions that pose measurement system based on non-cooperative target isdeveloped. The measuring system is consisted by camera, non-cooperative target,feature extraction algorithm, and pose resolving algorithm. Dimensionalinformation of target could not be reconstructed by mono-vision system, so thatdimensions of target should be known information. While precision of featureextraction is the most influencing factor, extraction of feature points onnon-cooperative is considerably tougher and less precise than cooperative target.Pose resolving algorithm is another intractable problem for that feature type is notconsistent. Feature points, feature lines, curves, and regions are used for posecalculation, so that the pose resolving algorithms should be capable to get poseinformation from those features. Usually, the pose resolving algorithm is varying indifferent conditions.Precision analysis and accuracy distribution are two important parts after measuring system is developed. Camera calibration, which is a preliminaryprocedure for precision analysis and accuracy distribution, is consisted by intrinsicand extrinsic parameters calibration. Intrinsic parameter is composed by focallength, center position of image, size of pixel on CCD or CMOS, and extrinsicparameter is composed by position and orientation relationship between cameracoordinate and reference coordinate. Total station is adopted for the cameracalibration and precision analysis, three corner cubes are imposed on both the targetand camera frame as the target of total station. Position accuracy of cooperativetarget is2.6‰when the measurement distance is about2.4meters, and the angleaccuracy is better than0.2°. Position accuracy of non-cooperative targetmeasurement is6.0‰when the measurement distance is about2.8meters, andangle accuracy is better than0.3°.Camera model, camera calibration model are included as preliminaryprocedures for pose measurement. Pose calculation algorithms by three featurepoints, four points, and N points are deduced, and feature points extractionmechanism for cooperative target is presented, and image processing procedure arealso analyzed. At the same time, feature extraction methods for points, lines, andcurves, and methods for finding correspondence between features extracted fromimages and on the model are presented for non-cooperative target. Methods to poseresolving for non-cooperative target by varying feature types are developed. Totalstation is adopted for camera calibration and accuracy analysis. The frame theoriesof mono-vision measurement are deduced, experiments are carried out, andprecision analysis is presented.