KEYWORDS: Clouds, 3D scanning, Image registration, 3D modeling, Calibration, 3D acquisition, Machine vision, Photogrammetry, Remote sensing, 3D image processing
In this paper, we propose a method of real-time point cloud registration for flexible hand-held 3D scanning. In this study, The problem of point cloud registration to be solved can be divided into refined registration and coarse registration with eight small or large overlap. The fine registration problem is solved by point-to-projection algorithm to ensure high efficiency. In addition, we solve the two types of coarse registration by exhaustive screening with different sampling means. To employ sampling screening algorithm, first we establish multiple matching relationships between two range image by using sampling point pairs, which are derived from the sampling sets of the respective 3D point clouds. Then we propose pose evaluation algorithm(PEA) inspired by ICP to screen out the most optimal matching relationship as the coarse registration result. In this case, we design PEA as a separate kernel function combined with GPU parallel technology to realize real-time computing. Back-projection calibration technology that robust for system distance error solve the problem of pose rejection criteria. The algorithm is highly versatile and robust, since the feature information of the 3D point cloud has never been utilized and extracted. The proposed method has been applied to our hand-held 3D scanners and has been tested on extensive real measured data to demonstrate the effectiveness.
The hand-eye system calibration, aiming to achieve the relationship between the robot hand and vision sensor mounted on it, is an important technique in the robot applications, involving automatic 3D measurement, visual serving, sensor placement planning, etc. Generally, the key issue of hand-eye calibration is equivalent to solving the homogeneous transformation matrix X from the equation of the form AX=XB. In this paper, we develop an accurate hand-eye calibration method by establishing a global objective function, in which the errors of camera calibration and robot movements have been considered. It is constructed based on the minimizing the projection error from the target benchmarks to the camera retina plane at all robot motions. The experimental results prove that the proposed algorithm can accurately solve the hand-eye calibration problem. Meanwhile, we set up an automatic 3D measurement system based on a robot and a rotary table, and developed a calibration scheme for the system to achieve the multi-view and fully automatic 3D data acquisition by using a fringe projection 3D sensor.
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