This paper proposes a method based on 3D version to recognize free-form objects in the complicated environment, which could be used in field of robot picking. The point cloud data is generated by binocular camera system. The depth map is employed to recover 3D point cloud of the scenario with the calibration method from the binocular camera. Segmentation algorithm is used to detect the object. Recognition is performed by using software libraries integrated with custom-developed segmentation algorithm and model database created by the same binocular camera system. Experiments are designed to verify the performance of the method by randomly placing different types of experimental objects in manipulator workspace. The preliminary results demonstrate the excellent ability of the system to perform object recognition and picking.
Three-dimensional contour imaging is used to reconstruct the surface of complex contour. Line-structured light is characterized by fast measurement, large amount of data and nondestructive to contour surface and is widely used in 3-D imaging. Therefore, a series of calibration methods for line-structured light are also produced, such as cross-ratio invariance, triangulation method, polynomial and so on. However, the traditional calibration methods are complex and take a long time, so a simplified method is proposed. This method omits the complicated process of calculating the cross-ratio and obtains the equation of light plane by accurately calculating the external parameters between the target and the camera and get the 3-D points of the line-structured light by the pinhole camera model. What’s more, RANSAC is applied to get the more precious line-structured light plane by eliminating the wrong points. Moreover, errors are measured and analyzed during the process of structure light calibration.
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