Research On Machine Vision Calibration And Object Detection Tracking Methods & Application

With the developing of science and technology of modern society, the demand is increasely improving on product quality and production efficiency and working conditions and the environment. Intelligence, robotics and automation is the inevitable trend of development for civil and military applications in the field from big production lines, automatic wash of buildings window, and cleaning work in bad environment, to defense weaponry and equipment manufacturing. It has a very important theoretical and economic significance to research machine vision theory method and its key technology. This paper has investigated vision calibration and visual object detection and tracking, and its application in the large condenser cleaning robots. Main results and contributions of this dissertation are as following.The background of the subject is analyzed, the vital theory and technique in the vision field are reviewed, and machine vision applications are presented, then the difficulty problems to be handled is discussed. For the sake of further research projective geometry, the imaging model and perspective, fundamental vision geometry are introduced briefly.An approach to self-calibration is proposed based on extended imaging model. Extended imaging models are described and three different homography between space plane and image plane are obtained from one image simultaneously under different directions perspective projection, further, constraints equation in intrinsic parameters are established. So a single image completed the process of calibration. Comparison with traditional method, the precision of calibration is improved without the step of matching image points in multi-view.Besides the calibration of camera imaging model, A self-calibration approach to hand-eye relation of manipulator is proposed based on a single point in the scene. The motions of manipulator are accurately controlled and read, then camera is required to observe one point in the scene at five (or more) pure translational motions and two (or more) pure rotational motions. The motions of camera are estimated from the disparity and depth value of the point. Thus, constrained equations are set up between the manipulator and the camera coordination. The five elements of intrinsic parameters of camera and hand-eye relation are determined linearly, and depth value of scene point is also solved. It is characteristic by conveniently controlling motions of manipulator and succinct implement of algorithm due to the utility of a single point in the scene, requiring neither matching, nor orthogonal motions.An algorithm is proposed for solving linearly the P5P problem with an un-calibrated camera based on vector difference. Vector difference is set up with five control points. Constraint equations in camera pose and intrinsic parameters are set up according orthogonal relation of rotation R. the analytic solutions of P5P with un-calibrated camera is determined in terms of linear theory.Motion Estimation from Image Sequences feature is investigated here, a linear algorithm for motion estimation is proposed based on parallel line segments (PLS) correspondences. Under the framework of structure from motion (SFM), Line segment is represented by two elements, point and line. The space line segment structure is reconstructed gradually by image lines under the help of parallelism. Then the two elements of space line segment based the motion parameters equations are established according to screw theory and solved using quaternion. Further, the motion parameters are optimized by PSO optimization algorithm. It is characteristic by linear constraint equations and analytic solutions.The method for visual object detection and tracking is discussed based on mean shift iteration. They are complementary, but the classical mean shift tracking algorithm has poor robustness in represent of color feature and complex iterations in matching. So the detection algorithm is proposed based on mean shift cluster and object model is represented by clustered modal points. Then a hierarchical mean shift(HMS)iteration for object tracking is proposed. The tracking match between object reference model and candidate model is performed at two levels, first in the clustered blocks, then in pixels within blocks. Finally, the centroid of tracking object is got layer by layer in the consecutive frames. Relatively, single object tracking is simpler and better performance is obtained using deterministic gradient algorithm of modified mean shift, however, multi-object visual tracking is done with probabilistic reasoning due to factors of unknown number of the object, and inter-acting each other. So a new approach to multi-object visual tracking is proposed based on Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampling. The tracking problem is formulated as computing the MAP (maximum a posteriori) estimation given image observation Four types of reversible and jump moves are designed for Markov Chains dynamics, and the prior proposal distribution of objects is developed with the aid of association match matrix to improves the confidence of sampling and perform the iteration effectively. The joint likelihood distribution measurement is presented at two levels of clustered blocks subsets (CBS) and pixels. Comparisons with other two MS algorithms demonstrate the validity, robustness, and performance of hierarchical mean shift(HMS) algorithm used for single and multi-object.For the application in mobile robot for cleaning condenser, the paper research vision system of robot and key vision technique to implement autonomous movement of robot and online cleaning of the condenser.Therefore, we construct vision system, including the subsystem of guiding robot navigation positioning, and the subsystem of guiding blowtorch to position the condenser pipes. Altogether four vision signals are transmitted to control cabinet via image acquisition card. Then decision is made to control robot after the process of visual information.A visual SLAM for robot is proposed based on 3D camera sensor, so that robot autonomously moves to the current place to be cleaned in the environment of condenser. SwissRanger SR3000 Camera used for sensing 3D natural environment provides mobile robot with image and 3dimension data. Theses data are regarded as two property of environment observation. Observation at time k is matched with observation at time k-1 under the constraint of the coupled BA and ICP, and the estimation of movement is performed. A solution to SLAM is obtained with the respect of visual theory, containing implementation of the 6 DOF location of mobile robot and 3demension mapping of landmarks. The solution is easier than traditional kinematics Kalman or particle filter without prediction process. The robot location and mapping is solved simultaneously. Comparison with laser 3d data match, the proposed algorithm carries out match process by using 2Dimage to guide 3D data so that search range is reduced and the computation efficiency of 3D SLAM is improved.An approach is presented based on vision to help robot with the positioning of condenser tubes. The work place is partitioned manually into blocks off-line according to the size of the area and view filed of camera. Course position is performed firstly by counting blocks, then precise position of each tube in current block is conducted by mechanical arm visual system when robot move to a certain course position. With the help of visual theory, the captured image is converted into tubes spatial position via tube detecting, circle fitting, the center calculating.