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Large-scale three-dimensional object measurement: a practical coordinate mapping and image data-patching method

机译:大规模三维物体测量:实用的坐标映射和图像数据修补方法

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摘要

In a practical three-dimensional (3-D) sensing system, the measurement of a large-scale object cannot be completed in only one operation. A relieflike object is generally divided into several subregions, an optical sensor positioned at each of these locations, and the shape of the whole object obtained by patching together all the 3-D data of the subregions. It is important to have accurate 3-D coordinates (x, y, z) for each subregion. We propose a new phase-to-height mapping algorithm and an accurate lateral coordinate calibration method with which to obtain the 3-D coordinates. After all the subregions are measured, it is necessary to transform the local coordinates into global world coordinates; here we present a new image data-patching method based on a flood algorithm. This method provides the optimal path along which to patch all the subregions into the shape of the entire object. We have measured and successfully patched a large sandy pool (9 m×5 m), and the reliability and feasibility of our method have been demonstrated by experiment.
机译:在实际的三维(3-D)传感系统中,仅通过一项操作就无法完成大型物体的测量。通常将浮雕状的物体分为几个子区域,将光学传感器放置在这些位置中的每一个上,并通过将子区域的所有3D数据拼凑在一起来获得整个物体的形状。每个子区域都有准确的3-D坐标(x,y,z),这一点很重要。我们提出了一种新的相位到高度的映射算法和一种精确的横向坐标校准方法,可以用来获取3-D坐标。在测量了所有子区域之后,有必要将局部坐标转换为全局世界坐标;这里我们提出一种基于泛洪算法的图像数据修补新方法。此方法提供了一条最佳路径,沿着该路径可以将所有子区域修补为整个对象的形状。我们已经测量并成功修补了一个大型沙池(9 m×5 m),并通过实验证明了我们方法的可靠性和可行性。

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