自定位实物数字化技术研究
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摘要
实物数字化是几何量测量技术中的重要研究领域,为了克服传统数字化手段数据获取欠灵活、存在扫描死角、范围受限等缺陷,本文对基于自定位的实物数字化技术进行了深入系统的研究。
首先,针对最广泛应用的圆形非编码标记点,提出了一种鲁棒的提取方法。利用Snake方法对边缘进行全局最优搜索,并以Zernike算子对边缘进行亚像素定位,最后,采用鲁棒的椭圆拟合算法迭代定位椭圆中心。由于Zernike算子的积分性质和鲁棒的拟合方法的联合作用,椭圆中心定位误差小于0.02个像素。
为了能够对系统进行方便、准确的标定,充分利用系统的结构特点,提出一种基于立体视觉的线结构光参数标定方法。算法在标定光平面时不需要标靶的辅助,而仅需将激光投射到任意物体表面即可获得大量的标定点并准确标定光平面方程,提高了其在工业现场进行标定的可靠性。
研究了扫描测头自定位问题。利用极线约束和测头运动一致性解决了标记点立体匹配问题,利用标记点空间拓扑不变性解决了不同视角下同一标记点对应问题和数据对齐问题。最后,在扫描结束后对数据进行了整体优化对齐。
研究了彩色点云的获取和色彩平滑问题。利用扫描测头相邻位姿间的变换关系,顺次将扫描结果投影到前序扫描图像中,并将投影点的颜色作为点云的颜色信息。提出了色彩变化度的概念,从而自适应地选择平滑邻域,滤除色彩噪声。
研究了点云数据后处理问题：提出了基于区域增长和局部离群系数的离群点滤除算法,有效滤除了杂乱点云；提出了一种逐层外扩的曲面重建方法。首先构建原始点云的近似网格曲面,然后再根据曲面变化对初始三角形进行二次剖分。剖分速度快,且基于层次B样条顶点优化提高了其处理噪声点云的能力。
Digitalization is an important research area in the geometric measurement technology. In order to overcome defects of traditional digitalization means, such as lack of flexible, existing scanning dead ends and limitation in object size, this paper does a deep research on self-reference based digitization.
An algorithm to extract the center of uncoded circle marks is proposed. Firstly, subpilxel edges are got by combine using of the Snake and the Zernike operator. Then accurate centers are found through robust ellipse fitting method. Exp eriments demonstrate that center detection error is less than0.02pixels.
In order to calibrate system parameters accurately and conveniently, a laser plane calibrate method base on stereo vision is developed which utilizes system structure fully. Compared with other popular methods, the main contribution is that the proposed algorithm needs no calibration target. Dense calibration points can be got by projecting structured light to any object, which widen its application scope.
Algorithm of self-reference is studied. Firstly, a novel stereo match algorithm using both the eliline constraint and the instrument motion consistency is presented. Then, corresponding marks from two views are got by their topological invariant in different coordinate systems. At last scanning data from two views are registered by found corresponding marks. To improve scanning accuracy, data from all views are refined by global optimization after scanning.
Colored point cloud acquisition and its color smoothing are discussed. The color of points reconstructed from current frame is restored by projected to former images in real time. To filter noise color introduced during scanning, an index of color variation is introduced and an offline adaptive smoothing algorithm based on it is presented, which can smooth color while keep feature perfectly.
At last, necessary data post processing is studied. Outliers are filtered by combine using of region growing and local outlier factor, and remained clean points are triangulated by a new developed surface reconstruction algorithm. The algorithm presented consists of two steps:an initial triangle mesh is constructed by repeating a simple advancing front rule. Then, initial triangles are subdivided according to surface various and triangle vertexes are refined by means of Multilevel B-spline fitting.
首先,针对最广泛应用的圆形非编码标记点,提出了一种鲁棒的提取方法。利用Snake方法对边缘进行全局最优搜索,并以Zernike算子对边缘进行亚像素定位,最后,采用鲁棒的椭圆拟合算法迭代定位椭圆中心。由于Zernike算子的积分性质和鲁棒的拟合方法的联合作用,椭圆中心定位误差小于0.02个像素。
为了能够对系统进行方便、准确的标定,充分利用系统的结构特点,提出一种基于立体视觉的线结构光参数标定方法。算法在标定光平面时不需要标靶的辅助,而仅需将激光投射到任意物体表面即可获得大量的标定点并准确标定光平面方程,提高了其在工业现场进行标定的可靠性。
研究了扫描测头自定位问题。利用极线约束和测头运动一致性解决了标记点立体匹配问题,利用标记点空间拓扑不变性解决了不同视角下同一标记点对应问题和数据对齐问题。最后,在扫描结束后对数据进行了整体优化对齐。
研究了彩色点云的获取和色彩平滑问题。利用扫描测头相邻位姿间的变换关系,顺次将扫描结果投影到前序扫描图像中,并将投影点的颜色作为点云的颜色信息。提出了色彩变化度的概念,从而自适应地选择平滑邻域,滤除色彩噪声。
研究了点云数据后处理问题：提出了基于区域增长和局部离群系数的离群点滤除算法,有效滤除了杂乱点云；提出了一种逐层外扩的曲面重建方法。首先构建原始点云的近似网格曲面,然后再根据曲面变化对初始三角形进行二次剖分。剖分速度快,且基于层次B样条顶点优化提高了其处理噪声点云的能力。
Digitalization is an important research area in the geometric measurement technology. In order to overcome defects of traditional digitalization means, such as lack of flexible, existing scanning dead ends and limitation in object size, this paper does a deep research on self-reference based digitization.
An algorithm to extract the center of uncoded circle marks is proposed. Firstly, subpilxel edges are got by combine using of the Snake and the Zernike operator. Then accurate centers are found through robust ellipse fitting method. Exp eriments demonstrate that center detection error is less than0.02pixels.
In order to calibrate system parameters accurately and conveniently, a laser plane calibrate method base on stereo vision is developed which utilizes system structure fully. Compared with other popular methods, the main contribution is that the proposed algorithm needs no calibration target. Dense calibration points can be got by projecting structured light to any object, which widen its application scope.
Algorithm of self-reference is studied. Firstly, a novel stereo match algorithm using both the eliline constraint and the instrument motion consistency is presented. Then, corresponding marks from two views are got by their topological invariant in different coordinate systems. At last scanning data from two views are registered by found corresponding marks. To improve scanning accuracy, data from all views are refined by global optimization after scanning.
Colored point cloud acquisition and its color smoothing are discussed. The color of points reconstructed from current frame is restored by projected to former images in real time. To filter noise color introduced during scanning, an index of color variation is introduced and an offline adaptive smoothing algorithm based on it is presented, which can smooth color while keep feature perfectly.
At last, necessary data post processing is studied. Outliers are filtered by combine using of region growing and local outlier factor, and remained clean points are triangulated by a new developed surface reconstruction algorithm. The algorithm presented consists of two steps:an initial triangle mesh is constructed by repeating a simple advancing front rule. Then, initial triangles are subdivided according to surface various and triangle vertexes are refined by means of Multilevel B-spline fitting.
引文
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