Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Properties of Straight Homogeneous Generalized Cylinders and Their Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determination of the Attitude of 3D Objects from a Single Perspective View
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing solid objects by alignment with an image
International Journal of Computer Vision
Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Affine Structure Matching for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Journal of Global Optimization
3-D Pose from 3 Points Using Weak-Perspective
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In industrial fields, precise pose of a 3D workpiece can guide operations like grasping and assembly tasks, thus precise estimation of pose of a 3D workpiece has received intensive attention over the last decades. When utilizing vision system as the source of pose estimation, it is difficult to get the pose of a 3D workpiece from the 2D image data provided by the vision system. Conventional methods face the complexity of model construction and time consumption on geometric matching. To overcome these difficulties, this paper proposes a search-based method to determine appropriate model and pose of a 3D workpiece that match the 2D image data. Concretely, we formulate the above problem as an optimization problem aiming at finding appropriate model parameters and pose parameters which minimizes the error between the notional 2D image (given by the model/pose parameters being optimized) and the real 2D image (provided by the vision system). Due to the coupling of model and pose parameters and discontinuity of the objective function, the above optimization problem cannot be tackled by conventional optimization techniques. Hence, we employ an evolutionary algorithm to cope with the optimization problem, where the evolutionary algorithm utilizes our problem-specific knowledge and adopts a hierarchical coarse-to-fine style to meet the requirement of online estimation. Experimental results demonstrate that our method is effective and efficient.