通过匹配多个三维轮廓图实现光条系统的自校准

O. Jokinen
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引用次数: 53

摘要

提出了一种改进光条系统标定的新方法,该方法包括相机成像平面与激光片平面之间的投影变换,以及扫描方向相对于激光片平面的投影变换。通过加权最小二乘匹配从不同视点获得的多个轮廓图,并使用近似校准进行注册,从而获得精细化。使用合成的轮廓图进行测试表明,如果目标的几何形状合适,并且准确地知道配准参数和系统的内在参数,则用于校准的地图中的平均噪声水平从0.3降至零像素,则相对于场景尺寸的校准精度可以达到0.003…0.00003%。也可以同时调整多个校准。配准和校准参数可以同时进行细化,但当平均噪声水平为0.03像素时,要达到0.03%的精度,需要接近的初始估计和相当复杂的目标几何形状。在配准和校准任务中,通过插值在地图的参数域上确定相应的点比在3D中最接近点的切面垂直投影具有更高的精度。当插值误差在重叠区域内尽可能相等时,可以获得最高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Self-calibration of a light striping system by matching multiple 3-D profile maps
A novel method is proposed for refining the calibration of a light striping system including a projective transformation between the image plane of the camera and the plane of the laser sheet, and also the direction of the scanning with respect to the plane of the laser sheet. The refinement is obtained through weighted least squares matching of multiple profile maps acquired from different viewpoints and registered previously using an approximate calibration. Testing with synthetically generated profile maps shows that if the geometry of the object is appropriate and the registration parameters and the intrinsic parameters of the system are known exactly, then a calibration accuracy of 0.003...0.00003% relative to the scene dimensions can be achieved as the average noise level in the maps used for the calibration decreases from 0.3 down to zero pixels. It is also possible to adjust several calibrations at the same time. The registration and calibration parameters can be refined simultaneously, but a close initial estimate and rather complex object geometry are needed for an accuracy of 0.03% when the average noise level is 0.03 pixels. Determining the corresponding points by interpolation on the parametric domains of the maps yields higher accuracy than perpendicular projection to the tangent planes at the closest points in 3D in both registration and calibration tasks. The highest accuracy is achieved when the interpolation errors are as equal as possible within the overlapping areas.
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