Research On 3D Reconstruction Of The Task Space For Telerobotic Welding

Welding technology would be widely used in maintenance of nuclear plants, construction and repairing operations of underwater structures and outer space aircrafts. Telerobotic welding has become the best choice to perform welding tasks in those hazardous or extreme environments instead of human operator. When performing the weld task in unstructured environment, automatic robot functions can be employed by the recontruction of robot's task space to avoid depending too much on human operator and to improve the efficiency and security. Furthermore, according to the development need of the completely autonomous welding, the ability to perceive envirionment of weld robot should be improved. The thesis investigates 3D reconstruction of the welding robot's task space base on stereo vision, and 3D modeling of untextured weld environment is implemented.Stereo vision system for 3D reconstruction of telerobotic's task space is set up. The system is calibrated and epipolar rectification is applied to the image pair. By system calibration, the mapping between the point in the robot coordinate and the pixel in the image is built. Epipolar rectification resamples the image and transforms epipolar line to image scanline, so that the complex of stereo matching algorithm is reduced.Lack of texture in weld scene makes stereo matching harder. Active illumination is applied to add artificial texture into the scene, and both feature matching of singe image pair and image sequence matching of spacetime stereo are investigated to accomplish stereo matching.In single image pair matching, features of corners and lines are used. Subpixel corner detection algorithm is used to detect the corner features. Man-machine interaction and autonomously corner matching are used for image pairs of peripheral equipment and simple workpiece respectively. A new line feature detection algorithm is presented to accomplish stereo matching of weld seam. The image is preprocessed by bottom hat transformation of morphology to enhance weld seam section and initial closed pixel level edge points of the weld seam are obtained by Canny detector and close operation. According to the continuity and limited width of weld seam, these edge points are filtered to eliminate wrong data. Then the filtered data is fitted to cubic smooth spline and the subpixel weld edge features are obtained. These subpixel feature points of weld seam are matched by epipolar constraint.A multi-level dual"saw-tooth"stripe gray light patterns are designed to add artificial texture into scene and a normalized SSSD matching algorithm is presented to revise spacetime stereo to accomplish the matching of images for the untextured weld environment. The normalized SSSD algorithm improves precision by normalizing the SSD of image pairs at different times. Subpixel disparity is calculated by fitting a second-degree curve to the SSSD values. By intensity tracking of the image sequence and left right consistant check, wrong regions are removed in the disparity map and anisotropy diffusion filtering is introduced to smooth disparity map while preserving depth discontinuity.The problem of creating 3D model of task space from disparity map or point clouds is also studied. The scene's triangulation mesh model can be directly created from disparity map. Besides, standard geometry model of weld workpiece can be obtained by disparity map segmentation and surface fitting. The USF range image plane segmentation algorithm is revised to segment disparity map containing cylinder surface via an additional step of region merging. The segmented point clouds, which belong to different surfaces, are then fitted to different surface models. The fitting algorithms for plane, general quadric and cylinder are investigated. Eigenvector method is used for plane fitting and regulation least squares method is used for quadric fitting. For cylinder fitting, a new algorithm of collaboration of principal component analysis and nonlinear least-squares algorithm is presented. For weld seam, NURBS curve fitting is used to create model from point cloud of weld seamFor typical weld task space, the reconstruction experiments for peripheral equipment, weld workpiece and weld seam are carried out. The errors of system calibration and reconstruction are analyzed. The mean of absolute value of the calibration's absolute error along X,Y,Z axes are [0.26 0.18 0.74]mm, respectively. Mean absolute position error for corner of peripheral equipment is 6.53mm. The fitting error for plane workpiece is 0.92mm, and the mean error of the boundary is 3.43mm. For saddle-shape workpiece, the mean fitting error is 1.35mm, and mean error for cylinders'heights and radius is 2.25mm. In reconstruction results of weld seam, the mean distance of fitted curve to the measured true datas for S-weld seam is 1.10mm, and the mean distance for cylinder butt joint weld seam is 1.33mm. The reconstruction results and errors show that the 3D reconstruction algorithm based on stereo vision can overcome the problem of lack of texture in welding scene, relative high precision can be obtained, and the algorithm can satisfy the need of creating model of task space for telerobotic welding.