CN101246595A - Multi-view point cloud data combination method in optical 3D scanning system - Google Patents

Multi-view point cloud data combination method in optical 3D scanning system Download PDF

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CN101246595A
CN101246595A CNA200810065320XA CN200810065320A CN101246595A CN 101246595 A CN101246595 A CN 101246595A CN A200810065320X A CNA200810065320X A CN A200810065320XA CN 200810065320 A CN200810065320 A CN 200810065320A CN 101246595 A CN101246595 A CN 101246595A
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车向前
程俊廷
赵灿
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Heilongjiang University of Science and Technology
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Abstract

光学三维扫描系统中多视点云数据拼合方法。该方法是在光学扫描成像系统支持下实现的,包括以下步骤:①采用中间白色实心圆和黑色外围组成的贴片作为参考单元,②在所扫描的对象上粘贴上至少三个参考单元,③直接信息提取,获得参考单元圆心的数据,④获得的参考单元圆心数据转换成三维的参考单元的形心位置信息,⑤借助解析法求出与数字摄像机数目相同的距离矩阵,⑥确定为两个点云集中的对应公共参考点,⑦采用至少三对对应公共参考点的对应坐标参数找出关于两个坐标系转换的旋转矩阵R和平移矩阵T,再根据转换公式:P=R*P′+T直接完成对点云坐标数据的改造,完成对两个点云数据的拼合。本发明的优点是,数据拼合简单,准确。

Figure 200810065320

A method for combining multi-view point cloud data in an optical 3D scanning system. The method is realized with the support of an optical scanning imaging system, and includes the following steps: ① Use a patch composed of a white solid circle in the middle and a black periphery as a reference unit; ② Paste at least three reference units on the scanned object; ③ Direct information extraction, obtain the data of the center of the reference unit, ④ convert the obtained reference unit center data into the centroid position information of the three-dimensional reference unit, ⑤ calculate the distance matrix with the same number as the number of digital cameras by means of the analytical method, ⑥ determined as two For the corresponding public reference points in the point cloud set, ⑦Use at least three pairs of corresponding coordinate parameters corresponding to the public reference points to find out the rotation matrix R and translation matrix T about the conversion of the two coordinate systems, and then according to the conversion formula: P=R*P′ +T directly completes the transformation of the point cloud coordinate data, and completes the combination of two point cloud data. The advantage of the present invention is that the data combination is simple and accurate.

Figure 200810065320

Description

光学三维扫描系统中多视点云数据拼合方法 Multi-view point cloud data combination method in optical 3D scanning system

技术领域technical field

本方法属于计算机数字图像处理技术,具体的说是一种光学三维扫描系统中多视点云数据拼合方法。The method belongs to the computer digital image processing technology, specifically a multi-viewpoint cloud data combination method in an optical three-dimensional scanning system.

背景技术Background technique

三维扫描系统在实际应用中得到了不断改进和发展,可以通过借助于对于扫描对象的直接拍摄而获得能再现对象图像的完整地点云数据集。但是在对大型件进行测绘时很难通过一次测量就可以获得对象完整的点云数据,一个摄像头往往无法扫描到全景,往往需要转换不同的角度进行测量;或采用至少两个以上的数字摄像头来同时进行扫描测量才能得到的能反映物体完整面貌的点云数据集。每个或每次只能得到某个视角内的物体的相关数据,只有得到完整的点云数据,需要对不同视角的点云数据进行拼合。The 3D scanning system has been continuously improved and developed in practical applications, and a complete location cloud data set that can reproduce the image of the object can be obtained by means of direct shooting of the scanned object. However, it is difficult to obtain the complete point cloud data of the object through one measurement when surveying and mapping large-scale objects. A camera often cannot scan the panorama, and it is often necessary to convert different angles for measurement; or use at least two or more digital cameras. The point cloud data set that can reflect the complete appearance of the object can only be obtained by scanning and measuring at the same time. Only the relevant data of objects in a certain viewing angle can be obtained each or every time, and only the complete point cloud data can be obtained, and the point cloud data of different viewing angles need to be stitched together.

目前现有拼合方法主要有:1)基于曲面拓扑特征即曲面的曲率这一前提进行拼合的,即搜索点云数据中曲率较高的点,利用这些特征信息作为基准点来拼合局部点云。这就使得那些曲面平滑,表面特征信息不明显,没有过高曲率的点云数据很难得到较好的拼合效果。并且求解点云数据中每个点的曲率是一个较费时的工作,邻域点数的选取直接影响该点的曲率计算结果,不定因素很多;②人机交互的方法。比较典型的程序步骤是首先人工选定若干对对应点,先完成整体拼合,也就是粗拼合工作,然后利用ICP算法对共公点云数据集中的各个元素进行迭代运算,使拼合结果达到最佳。这种方法的缺点是对操作人员的要求较高,要求所选取的对应点准确,并且ICP算法对初始拼合结果较敏感,如果粗拼合偏差较大,会为精确拼合带来较大的计算量。At present, the existing stitching methods mainly include: 1) stitching based on the premise of the topological feature of the surface, that is, the curvature of the surface, that is, searching for points with higher curvature in the point cloud data, and using these feature information as a reference point to stitch the local point cloud. This makes those surfaces smooth, surface feature information is not obvious, and point cloud data without excessive curvature is difficult to get a better stitching effect. And it is a time-consuming task to calculate the curvature of each point in the point cloud data. The selection of the number of neighborhood points directly affects the calculation result of the curvature of the point, and there are many uncertain factors; ②The method of human-computer interaction. The typical program steps are to manually select several pairs of corresponding points, first to complete the overall merging, that is, the rough merging work, and then use the ICP algorithm to perform iterative operations on each element in the public point cloud data set to achieve the best merging result. . The disadvantage of this method is that it has higher requirements on the operator, and the selected corresponding points are required to be accurate, and the ICP algorithm is more sensitive to the initial merging results. If the deviation of the rough merging is large, it will bring a large amount of calculation for the precise merging .

发明内容Contents of the invention

本发明的目的是针对多视点扫描成像系统中出现的分别独立的点云数据,找到一个快速、准确地实现数据拼合的方法,实现对不同视角下获取的三维点云数据进行规范,寻求一个精度高,速度快、适用广泛的方法作指导,以实现对不同视角的点云数据进行拼合。本发明的设计基础基于在图像的三维扫描系统进行点云数据提取的过程中,所得出的是由在线参照系坐标信息确立的坐标数据。进行点云数据拼合的过程,实际上是两个参照系坐标变换的过程。因此,本发明的方法就是找出两个坐标系变换公式的待定系数的操作过程。The purpose of the present invention is to find a method for quickly and accurately realizing data stitching for the separately independent point cloud data appearing in the multi-viewpoint scanning imaging system, realize the standardization of three-dimensional point cloud data acquired under different viewing angles, and seek a precision High, fast, and widely applicable methods are used as guidance to realize the stitching of point cloud data from different perspectives. The design basis of the present invention is based on the coordinate data established by the coordinate information of the online reference system obtained during the point cloud data extraction process of the image three-dimensional scanning system. The process of merging point cloud data is actually a process of coordinate transformation between two reference systems. Therefore, the method of the present invention is the operation process of finding out the undetermined coefficients of the two coordinate system transformation formulas.

本方法是在计算机、存有光学扫描成像系统管理软件的配套存储器、和具有至少两个数字摄像机的支持下实现完成的,关键在于本方法包括以下步骤:This method is realized with the support of a computer, a supporting memory with optical scanning imaging system management software, and at least two digital cameras. The key point is that this method includes the following steps:

①采用中间白色实心圆和圆外黑色封闭区域构成的贴片作为参考单元,①A patch consisting of a white solid circle in the middle and a black closed area outside the circle is used as a reference unit,

②在处于不同视点的相关数字摄像机均可以同时扫描到的范围内在所扫描的对象上粘贴上至少三个参考单元,②Paste at least three reference units on the scanned object within the range that the relevant digital cameras at different viewpoints can scan at the same time,

③借助于在线运行的系统对扫描的对象上粘贴上的参考单元进行直接信息提取,获得参考单元圆心的数据信息,再采用区域标识法对参考点的黑、白过渡边界提取后,利用最小二乘相结合的方法获取参考单元的圆心位置数据,③ With the help of the online operating system, the reference unit pasted on the scanned object is directly extracted to obtain the data information of the center of the reference unit, and then the black and white transition boundary of the reference point is extracted by using the area identification method, and the least squares is used to extract the reference unit. The method of combining multiplication and multiplication to obtain the center position data of the reference unit,

④将以上获得的参考单元的形心位置数据,利用计算机视觉理论,进行立体匹配,即将平面的参考单元圆心数据转换成三维的参考单元的形心位置信息,④ Use the computer vision theory to perform stereo matching on the centroid position data of the reference unit obtained above, that is, convert the center data of the plane reference unit into the centroid position information of the three-dimensional reference unit,

⑤根据参考单元的形心位置信息,参照数字摄像机的扫描测绘所得出点云集中各个点的数据信息,借助解析法求出点云集中选定的参考点与参考单元的形心位置的距离参数形成与所采用的数字摄像机数目相同的距离矩阵,⑤According to the centroid position information of the reference unit, refer to the data information of each point in the point cloud set obtained by scanning and mapping of the digital camera, and use the analytical method to obtain the distance parameter between the selected reference point in the point cloud set and the centroid position of the reference unit form the same number of distance matrices as the number of digital cameras employed,

⑥通过对比两个矩阵中元素值找出它们的最大同构子集,从而找出具有相同拓扑特征的参考点,确定为两个点云集中的对应公共参考点,⑥ By comparing the element values in the two matrices to find their largest isomorphic subset, so as to find the reference point with the same topological characteristics, and determine it as the corresponding common reference point in the two point cloud sets,

⑦采用至少三对对应公共参考点的对应坐标参数找出关于两个坐标系转换的旋转矩阵R和平移矩阵T,再根据转换公式:P=R*P′+T直接完成对点云坐标数据的改造,完成对两个点云数⑦ Use at least three pairs of corresponding coordinate parameters corresponding to the common reference points to find out the rotation matrix R and translation matrix T about the conversion of the two coordinate systems, and then directly complete the point cloud coordinate data according to the conversion formula: P=R*P′+T The transformation is completed on the two point clouds

本方法实际上是借助于设计好的参考贴片,将被扫描测绘的大型物体人为设置分界和标示,通过对两次不同视角所采集到的点云数据集中的贴片信息处理,找到了足够的具有参考意义的公共对应点。所有的对应点针对同一位置但具有完全不同的坐标信息,从而给找出两个坐标系对应位置点之间转换公式中的待定系数提供了必要的条件。将不同坐标系点云中的每一个点按照公式可以轻而易举地完成转换,也就是轻松准确地完成了数据拼合任务。在现有系统和管理软件的协助下现有技术中复杂而且艰难的工作在本方法的指导和计算机系统的支持下变得如此简单和准确,这正是本发明的积极效果。In fact, this method uses the designed reference patches to artificially set boundaries and labels for large objects to be scanned and mapped. By processing the patch information in point cloud datasets collected from two different viewing angles, a sufficient The public corresponding points with reference significance. All corresponding points point to the same position but have completely different coordinate information, thus providing the necessary conditions for finding out the undetermined coefficients in the conversion formula between the corresponding position points of the two coordinate systems. Converting each point in the point cloud of different coordinate systems according to the formula can be easily completed, that is, the task of data stitching is easily and accurately completed. With the assistance of the existing system and management software, the complex and difficult work in the prior art becomes so simple and accurate under the guidance of the method and the support of the computer system, which is the positive effect of the present invention.

附图说明Description of drawings

图1是以圆形为参考单元的贴片。Figure 1 is a patch with a circle as the reference unit.

图2是以矩形为参考单元的贴片。Figure 2 is a patch with a rectangle as the reference unit.

图3是处理过程中的等价标记归并流程图。Fig. 3 is a flow chart of merging equivalence marks during processing.

具体实施方式Detailed ways

光学三维扫描系统中多视点云数据拼合方法,该方法是在计算机、存有光学扫描成像系统管理软件的配套存储器、和具有至少两个数字摄像机的支持下实现完成的,该方法包括以下步骤:A method for combining multi-view point cloud data in an optical three-dimensional scanning system. The method is implemented with the support of a computer, a supporting memory with optical scanning imaging system management software, and at least two digital cameras. The method includes the following steps:

①采用中间白色实心圆和圆外黑色封闭区域构成的贴片作为参考单元;①A patch consisting of a white solid circle in the middle and a black closed area outside the circle is used as the reference unit;

②在处于不同视点的相关数字摄像机均可以同时扫描到的范围内在所扫描的对象上粘贴上至少三个参考单元;②Paste at least three reference units on the scanned object within the range that relevant digital cameras at different viewpoints can scan simultaneously;

③借助于在线运行的系统对扫描的对象上粘贴上的参考单元进行直接信息提取,获得参考单元圆心的数据信息,再采用区域标识法对参考点的黑、白过渡边界提取后,利用最小二乘相结合的方法获取参考单元的圆心位置数据;③ With the help of the online operating system, the reference unit pasted on the scanned object is directly extracted to obtain the data information of the center of the reference unit, and then the black and white transition boundary of the reference point is extracted by using the area identification method, and the least squares is used to extract the reference unit. The method of combining multiplication to obtain the center position data of the reference unit;

④将以上获得的参考单元的形心位置数据,利用计算机视觉理论,进行立体匹配,即将平面的参考单元圆心数据转换成三维的参考单元的形心位置信息;④ Use the computer vision theory to perform stereo matching on the centroid position data of the reference unit obtained above, that is, convert the center data of the plane reference unit into the centroid position information of the three-dimensional reference unit;

⑤根据参考单元的形心位置信息,参照数字摄像机的扫描测绘所得出点云集中各个点的数据信息,借助解析法求出点云集中选定的参考点与参考单元的形心位置的距离参数形成与所采用的数字摄像机数目相同的距离矩阵;⑤According to the centroid position information of the reference unit, refer to the data information of each point in the point cloud set obtained by scanning and mapping of the digital camera, and use the analytical method to obtain the distance parameter between the selected reference point in the point cloud set and the centroid position of the reference unit Form the same distance matrix as the number of digital cameras used;

⑥通过对比两个矩阵中元素值找出它们的最大同构子集,从而找出具有相同拓扑特征的参考点,确定为两个点云集中的对应公共参考点;⑥ Find the largest isomorphic subset by comparing the element values in the two matrices, so as to find the reference point with the same topological characteristics, and determine it as the corresponding common reference point in the two point cloud sets;

⑦采用至少三对对应公共参考点的对应坐标参数找出关于两个坐标系转换的旋转矩阵R和平移矩阵T,再根据转换公式:P=R*P′+T直接完成对点云坐标数据的改造,完成对两个点云数据的拼合。⑦ Use at least three pairs of corresponding coordinate parameters corresponding to the common reference points to find out the rotation matrix R and translation matrix T about the conversion of the two coordinate systems, and then directly complete the point cloud coordinate data according to the conversion formula: P=R*P′+T Transformation to complete the combination of two point cloud data.

具体实施时步骤①中用的参考单元是圆形或矩形中间为白色实心圆的黑色贴片。During specific implementation, the reference unit used in step ① is a black patch with a white solid circle in the middle of a circle or a rectangle.

以上所说的步骤②中所说的参考单元均匀贴在两个数字摄像机所能扫描到公共区域。The reference unit mentioned in step ② mentioned above is evenly attached to the common area that can be scanned by the two digital cameras.

以上步骤③借助于在线运行的系统对扫描的对象上粘贴上的参考单元进行直接信息提取,是采用区域标识与最小二乘相结合的方法获取参考点的形心位置,首先采用序贯算法扫描图像,将待测物体图像做区域标识及等价对划分,将属于同一类的标记点归并,并赋予相同的等价标记;然后利用边界点识别方法获得参考点的边界,即对于边界内部的点,则此点周围8个方向上的点全部为黑色;反之,边界上的点就不具有这样的性质;或根据点的4邻域来做边界点的判断工作借助最小二乘法拟合出参考点的形心位置及相关的数据信息,将以上相关信息存入中间寄存器中待用。The above step ③ uses the online operating system to directly extract the reference unit pasted on the scanned object. It uses the method of combining area identification and least squares to obtain the centroid position of the reference point. First, the sequential algorithm is used to scan Image, the image of the object to be tested is identified and divided into equivalent pairs, the marked points belonging to the same class are merged, and the same equivalent mark is given; then the boundary of the reference point is obtained by using the boundary point recognition method, that is, for the boundary inside point, the points in the eight directions around this point are all black; otherwise, the points on the boundary do not have such properties; The centroid position of the reference point and related data information are stored in the intermediate register for use.

更具体的操作过程如下:首先对采集的包含参考单元的图像进行中值滤波处理,从而实现去除图像中的噪点。然后采用动态阈值分割方法来实现图像的二值化,白色记为1,黑色记为0。对图像从左到右、从上到下进行扫描过程中,如果当前像素的灰度值为1,检查它左、左上、上、右上这4个相邻像素。如果上述4个相邻像素的灰度值都为0,就给当前像素一个新的位置标记值。如果4个相邻像素中只有一个像素的灰度值为1,就把该像素的位置标记值赋给当前像素。如果4个相邻像素中有m(1<m<=4)个像素的灰度值为1,则按照左、左上、上、右上的优先顺序,确定当前像素的位置标记值。然后对这m个像素所拥有的标记值记做等价对,并将其归入一个等价对链表中。图3表示计算O点位置序号时相关的邻域点。可见,O点的标记值与A、B、C、D的标记值有关。如果A、B、C、D在二值图像中有1值,O的标记值取其中标记值的最小值,否则将当前最大标记值加1赋给O点。若A、B、C、D中都有标记值,则将优先级高的标记值标记O点,将其余不同的标记值做等价处理,这样就建立了一个对应于二值图像的序号矩阵M和等价对链表EList。将EList中的等价对进行整理,去除其中的重复项,并将所有等价像素做同一标记。尽管EList中包含了所有的等价对,但其中可能存在同一标记值对应不同的标记值的情况,如经过序贯连接,可能会出现4和5等价且4和3等价的情况,这样需要做处理,将3、4、5标识成同一标记值。然后构造一双层链表SList,将等价对链表EList中的头节点先存入SList中,对EList中的其余每个节点做如下处理:对于EList当前节点中的两个标记值a和b,遍历SList,如果找到a,则将含有a的内链表指针记录在pa中,否则pa为空;如果找到b,将包含b的内链表指针记录在pb中,否则pb为空。根据pa与pb的值,做如图3所示的处理。经过以上处理后,将所有的等价标记链表存放到SList中,将每一组等价像素标记换为等价标记链中的最小标记值,使所有的连通区域就都被标记上了新的相同标记。A more specific operation process is as follows: firstly, median filter processing is performed on the collected image including the reference unit, so as to remove the noise in the image. Then, the dynamic threshold segmentation method is used to realize the binarization of the image, and the white is recorded as 1, and the black is recorded as 0. In the process of scanning the image from left to right and from top to bottom, if the gray value of the current pixel is 1, check its 4 adjacent pixels on the left, top left, top, and top right. If the gray values of the above four adjacent pixels are all 0, give the current pixel a new position marker value. If only one of the four adjacent pixels has a gray value of 1, assign the position marker value of this pixel to the current pixel. If the gray value of m (1<m<=4) pixels among the 4 adjacent pixels is 1, then determine the position mark value of the current pixel according to the priority order of left, upper left, upper, upper right. Then record the equivalence pairs for the tag values owned by these m pixels, and put them into an equivalence pair linked list. Fig. 3 shows the relevant neighbor points when calculating the position number of point O. It can be seen that the marked value of point O is related to the marked values of A, B, C, and D. If A, B, C, D have a value of 1 in the binary image, the mark value of O takes the minimum value of the mark value, otherwise, add 1 to the current maximum mark value and assign it to point O. If there are marked values in A, B, C, and D, then mark the marked value with high priority at point O, and treat the remaining different marked values as equivalent, thus establishing a serial number matrix corresponding to the binary image M and the equivalent pair linked list EList. Arrange the equivalent pairs in the EList, remove the duplicates, and mark all the equivalent pixels with the same mark. Although EList contains all equivalence pairs, there may be cases where the same tag value corresponds to different tag values. For example, after sequential connection, there may be cases where 4 and 5 are equivalent and 4 and 3 are equivalent. In this way It needs to be processed to mark 3, 4, and 5 as the same tag value. Then construct a double-layer linked list SList, first store the head node in the equivalent pair linked list EList in the SList, and do the following processing for each other node in the EList: For the two tag values a and b in the current node of the EList, Traversing the SList, if a is found, record the pointer of the internal linked list containing a in pa, otherwise pa is empty; if b is found, record the pointer of the internal linked list containing b in pb, otherwise pb is empty. According to the values of pa and pb, do the processing as shown in Figure 3. After the above processing, store all the equivalent tag linked lists in the SList, and replace each group of equivalent pixel tags with the minimum tag value in the equivalent tag chain, so that all connected regions are marked with new Same mark.

对于同一种标记的区域进行边界提取,对于边界内部的点,此点周围8个方向上的点全部为黑色;反之,边界上的点就不具有这样的性质。利用这一方法可以得到参考点的闭合边缘点数据,将其存放到一个链表中。运用最小二乘拟合法来求取边界像素所包围区域的形心:Boundary extraction is performed on the region of the same type of mark. For the point inside the boundary, the points in the 8 directions around this point are all black; otherwise, the points on the boundary do not have such properties. By using this method, the closed edge point data of the reference point can be obtained and stored in a linked list. Use the least squares fitting method to find the centroid of the area enclosed by the boundary pixels:

设给定的样点为pi(xi,yi),i=1...n。Let the given sample point be p i ( xi , y i ), i=1...n.

可以得到:can get:

(( xx ii -- uu ^^ 11 )) 22 ++ (( ythe y ii -- uu ^^ 22 )) 22 == (( xx ii ++ 11 -- uu ^^ 11 )) 22 ++ (( ythe y ii ++ 11 -- uu ^^ 22 )) 22

通过上式可以求得几何形心的最佳无偏估计为:The best unbiased estimate of the geometric centroid can be obtained by the above formula:

uu ^^ 11 == &Sigma;&Sigma; ii == 11 mm -- 11 bb ii 22 &CenterDot;&CenterDot; &Sigma;&Sigma; ii == 11 mm -- 11 aa ii cc ii -- &Sigma;&Sigma; ii == 11 mm -- 11 bb ii cc ii &CenterDot;&Center Dot; &Sigma;&Sigma; ii == 11 mm -- 11 aa ii bb ii &Sigma;&Sigma; ii == 11 mm -- 11 aa ii 22 &CenterDot;&CenterDot; &Sigma;&Sigma; ii == 11 mm -- 11 bb ii 22 -- (( &Sigma;&Sigma; ii == 11 mm -- 11 aa ii bb ii )) 22

uu ^^ 22 == &Sigma;&Sigma; ii == 11 mm -- 11 aa ii 22 &CenterDot;&Center Dot; &Sigma;&Sigma; ii == 11 mm -- 11 bb ii cc ii -- &Sigma;&Sigma; ii == 11 mm -- 11 aa ii cc ii &CenterDot;&Center Dot; &Sigma;&Sigma; ii == 11 mm -- 11 aa ii bb ii &Sigma;&Sigma; ii == 11 mm -- 11 aa ii 22 &CenterDot;&CenterDot; &Sigma;&Sigma; ii == 11 mm -- 11 bb ii 22 -- (( &Sigma;&Sigma; ii == 11 mm -- 11 aa ii bb ii )) 22

其中:ai=2(xi+1-xi),bi=2(yi+1-yi),Among them: a i =2(x i+1 -x i ), b i =2(y i+1 -y i ),

cc ii == xx ii ++ 11 22 ++ ythe y ii ++ 11 22 -- xx ii 22 -- ythe y ii 22

考虑到实际测量中可能产生误差,所以选定构成等距离同构子集时设置比对误差值,即在以上所说的步骤⑥中确定为两个点云集对应公共参考点的具体步骤是:根据本测量系统存在的基础误差决定最小误差值δ,以比对误差值小于δ为条件由从两个矩阵中元素值找出它们的最大同构子集。进一步找出公共对应点对。Considering that there may be errors in the actual measurement, the comparison error value is set when the equidistant isomorphic subset is selected, that is, the specific steps for determining the common reference point corresponding to the two point cloud sets in the above-mentioned step ⑥ are: Determine the minimum error value δ according to the basic error of the measurement system, and find out the largest isomorphic subset from the element values in the two matrices on the condition that the comparison error value is less than δ. Further find the common corresponding point pairs.

根据所获得的对应点对(3对以上),采用奇异值分解(SVD)来完成旋转矩阵与平移向量的求解出相关的坐标变换公式以及进一步完成转换和点云数据拼合也就迎刃而解了。According to the obtained corresponding point pairs (more than 3 pairs), use Singular Value Decomposition (SVD) to complete the solution of the rotation matrix and translation vector to obtain the relevant coordinate transformation formula and further complete the conversion and point cloud data combination.

进一步说明如下:Further explanation is as follows:

设点云A所对应的参考点集合为P={p1,p2…pn},点云B所对应的参考点集合为Q={q1,q2…qn},这两个参考点集中点的对应关系可以描述为qi=R×pi+T  i=1,2,…,n,n>3Suppose the set of reference points corresponding to point cloud A is P={p 1 , p 2 ...p n }, and the set of reference points corresponding to point cloud B is Q={q 1 , q 2 ...q n }, the two The corresponding relationship of points in the reference point set can be described as q i =R×p i +T i=1, 2,...,n, n>3

式中R为一个3×3的旋转矩阵,T为一个3维的平移矢量,n为参考点对的数目。建立如下误差函数In the formula, R is a 3×3 rotation matrix, T is a 3-dimensional translation vector, and n is the number of reference point pairs. Create the following error function

ff (( RR ,, TT )) == 11 nno &Sigma;&Sigma; ii == 11 nno |||| qq ii -- (( RR &times;&times; pp ii ++ TT )) |||| 22

采用SVD算法求解放置矩阵与平移矢量,令The SVD algorithm is used to solve the placement matrix and translation vector, so that

Hh 33 &times;&times; 33 == 11 nno &Sigma;&Sigma; ii == 11 nno (( pp ii -- pp &OverBar;&OverBar; )) (( qq ii -- qq &OverBar;&OverBar; )) TT

其中: p &OverBar; = 1 n &Sigma; i = 1 n p i , q &OverBar; = 1 n &Sigma; i = 1 n q i 分别为P和Q的质心。对矩阵H作奇异值分解得:H=UDVT,(D=diag(di),d1≥d2≥d3≥0),in: p &OverBar; = 1 no &Sigma; i = 1 no p i , q &OverBar; = 1 no &Sigma; i = 1 no q i are the centroids of P and Q, respectively. Singular value decomposition of matrix H is obtained: H=UDV T , (D=diag(d i ), d 1 ≥d 2 ≥d 3 ≥0),

旋转矩阵:R=UVT Rotation matrix: R=UV T

平移向量:T=p-R×qTranslation vector: T=p-R×q

求解出旋转矩阵R与平移矩阵T,即可以实现对两个点云数据的拼合,最终可以获得一个实物的完整点云数据。Solve the rotation matrix R and the translation matrix T, that is, the combination of two point cloud data can be realized, and finally a complete point cloud data of an object can be obtained.

Claims (5)

1、光学三维扫描系统中多视点云数据拼合方法,该方法是在计算机、存有光学扫描成像系统管理软件的配套存储器、和具有至少两个数字摄像机的支持下实现完成的,其特征在于该方法包括以下步骤:1. A method for combining multi-viewpoint cloud data in an optical three-dimensional scanning system. The method is implemented with the support of a computer, a supporting memory with optical scanning imaging system management software, and at least two digital cameras. It is characterized in that The method includes the following steps: ①采用中间白色实心圆和圆外黑色封闭区域构成的贴片作为参考单元,①A patch consisting of a white solid circle in the middle and a black closed area outside the circle is used as a reference unit, ②在处于不同视点的相关数字摄像机均可以同时扫描到的范围内在所扫描的对象上粘贴上至少三个参考单元,②Paste at least three reference units on the scanned object within the range that the relevant digital cameras at different viewpoints can scan at the same time, ③借助于在线运行的系统对扫描的对象上粘贴上的参考单元进行直接信息提取,获得参考单元圆心的数据信息,再采用区域标识法对参考点的黑、白过渡边界提取后,利用最小二乘相结合的方法获取参考单元的圆心位置数据,③ With the help of the online operating system, the reference unit pasted on the scanned object is directly extracted to obtain the data information of the center of the reference unit, and then the black and white transition boundary of the reference point is extracted by using the area identification method, and the least squares is used to extract the reference unit. The method of combining multiplication and multiplication to obtain the center position data of the reference unit, ④将以上获得的参考单元的形心位置数据,利用计算机视觉理论,进行立体匹配,即将平面的参考单元圆心数据转换成三维的参考单元的形心位置信息,④ Use the computer vision theory to perform stereo matching on the centroid position data of the reference unit obtained above, that is, convert the center data of the plane reference unit into the centroid position information of the three-dimensional reference unit, ⑤根据参考单元的形心位置信息,参照数字摄像机的扫描测绘所得出点云集中各个点的数据信息,借助解析法求出点云集中选定的参考点与参考单元的形心位置的距离参数形成与所采用的数字摄像机数目相同的距离矩阵,⑤According to the centroid position information of the reference unit, refer to the data information of each point in the point cloud set obtained by scanning and mapping of the digital camera, and use the analytical method to obtain the distance parameter between the selected reference point in the point cloud set and the centroid position of the reference unit form the same number of distance matrices as the number of digital cameras employed, ⑥通过对比两个矩阵中元素值找出它们的最大同构子集,从而找出具有相同拓扑特征的参考点,确定为两个点云集中的对应公共参考点,⑥ By comparing the element values in the two matrices to find their largest isomorphic subset, so as to find the reference point with the same topological characteristics, and determine it as the corresponding common reference point in the two point cloud sets, ⑦采用至少三对对应公共参考点的对应坐标参数找出关于两个坐标系转换的旋转矩阵R和平移矩阵T,再根据转换公式:P=R*P′+T直接完成对点云坐标数据的改造,完成对两个点云数据的拼合。⑦ Use at least three pairs of corresponding coordinate parameters corresponding to the common reference points to find out the rotation matrix R and translation matrix T about the conversion of the two coordinate systems, and then directly complete the point cloud coordinate data according to the conversion formula: P=R*P′+T Transformation to complete the combination of two point cloud data. 2、根据权利要求1所说的光学三维扫描系统中多视点云数据拼合方法,其特征在于步骤①中用的参考单元是圆形或矩形中间为白色实心圆的黑色贴片。2. The method for combining multi-view point cloud data in an optical three-dimensional scanning system according to claim 1, wherein the reference unit used in step ① is a circle or a black patch with a white solid circle in the middle of a rectangle. 3、根据权利要求1所说的光学三维扫描系统中多视点云数据拼合方法,其特征在于步骤②中所说的参考单元均匀贴在两个数字摄像机所描到公共区域。3. The method for combining multi-view point cloud data in an optical three-dimensional scanning system according to claim 1, characterized in that the reference unit in step ② is evenly attached to the common area depicted by the two digital cameras. 4、根据权利要求1所说的光学三维扫描系统中多视点云数据拼合方法,其特征在于步骤③借助于在线运行的系统对扫描的对象上粘贴上的参考单元进行直接信息提取,是采用区域标识与最小二乘相结合的方法获取参考点的形心位置,首先采用序贯算法扫描图像,将待测物体图像做区域标识及等价对划分,将属于同一类的标记点归并,并赋予相同的等价标记;然后利用边界点识别方法获得参考点的边界,即对于边界内部的点,则此点周围8个方向上的点全部为黑色;反之,边界上的点就不具有这样的性质;或根据点的4邻域来做边界点的判断工作借助最小二乘法拟合出参考点的形心位置及相关的数据信息,将以上相关信息存入中间寄存器中待用。4. The multi-view point cloud data merging method in the optical three-dimensional scanning system according to claim 1, characterized in that the step ③ directly extracts information from the reference unit pasted on the scanned object by means of an online operating system, and uses the area The method of combining identification and least squares to obtain the centroid position of the reference point, first scans the image with a sequential algorithm, makes the area identification and equivalence pair division of the image of the object to be measured, merges the marking points belonging to the same class, and assigns The same equivalent mark; then use the boundary point recognition method to obtain the boundary of the reference point, that is, for the point inside the boundary, the points in the 8 directions around this point are all black; otherwise, the points on the boundary do not have such properties; or judge the boundary point according to the 4 neighbors of the point, use the least square method to fit the centroid position of the reference point and related data information, and store the above related information in the intermediate register for use. 5、根据权利要求1所说的光学三维扫描系统中多视点云数据拼合方法,其特征在于步骤⑥中确定为两个点云集对应公共参考点的具体步骤是:根据本测量系统存在的基础误差决定最小误差值δ,以比对误差值小于δ为条件由从两个矩阵中元素值找出它们的最大同构子集。5. The multi-view point cloud data merging method in the optical three-dimensional scanning system according to claim 1, characterized in that in step ⑥, the specific step of determining that the two point cloud sets correspond to the common reference points is: according to the basic error existing in the measurement system Determine the minimum error value δ, and find out the largest isomorphic subset from the element values in the two matrices on the condition that the comparison error value is less than δ.
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