CN107341825A - A kind of method for simplifying for large scene high-precision three-dimensional laser measurement cloud data - Google Patents
A kind of method for simplifying for large scene high-precision three-dimensional laser measurement cloud data Download PDFInfo
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Abstract
本发明涉及一种用于大场景高精度三维激光测量点云数据的简化方法,能够支持大场景、高精度三维激光扫描点云数据的快速简化,同时保持点云数据中关键特征,属于三维建模技术领域。本发明采用均匀栅格法对散乱点云进行空间均匀划分,建立点云数据对应的栅格索引,利用空间位置快速查找数据点的K邻域;根据投影残差值提取点云特征点,利用曲面变分值对点云数据进行区域划分;利用区域划分和数据点的曲面变分值对原始点云进行简化,最终得到简化后的点云数据。本发明的方法可对数据量大于1亿点的高精度扫描数据进行快速简化,执行速度快,在有效降低数据容量的同时,能够保持场景中的关键特征点,有利于后期的三维建模等工作开展。The invention relates to a method for simplifying point cloud data of high-precision three-dimensional laser measurement in large scenes, which can support rapid simplification of point cloud data in large scenes and high-precision three-dimensional laser scanning, while maintaining key features in point cloud data, and belongs to three-dimensional construction field of mold technology. The invention uses the uniform grid method to evenly space the scattered point cloud, establishes the grid index corresponding to the point cloud data, uses the spatial position to quickly find the K neighborhood of the data point; extracts the point cloud feature point according to the projection residual value, and uses The surface variation value divides the point cloud data into regions; the original point cloud is simplified by using the region division and the surface variation value of data points, and finally the simplified point cloud data is obtained. The method of the present invention can quickly simplify the high-precision scanning data with a data volume greater than 100 million points, and the execution speed is fast. While effectively reducing the data capacity, it can maintain key feature points in the scene, which is beneficial to later three-dimensional modeling, etc. work carried out.
Description
技术领域technical field
本发明涉及一种用于大场景高精度三维激光测量点云数据的简化方法,能够支持大场景、高精度三维激光扫描点云数据的快速简化,同时保持点云数据中关键特征,属于三维建模技术领域。The invention relates to a method for simplifying point cloud data of high-precision three-dimensional laser measurement in large scenes, which can support rapid simplification of point cloud data in large scenes and high-precision three-dimensional laser scanning, while maintaining key features in point cloud data, and belongs to three-dimensional construction field of mold technology.
背景技术Background technique
随着三维扫描仪的测量精度不断提高,采集到的点云越来越密,而且包含了丰富的细节信息。但是,庞大的点云数据对后续处理以及存储、显示、传输带来了许多不便。如果直接对其进行处理,势必占用大量的硬件资源和时间,而且并不是所有的数据点都需要用于后续处理,过密的点云数据在可视化过程中会影响三维物体重构的质量。现有处理方法主要有基于空间的点云简化方法、基于法向的点云简化方法、基于曲面变化度的点云简化方法和混合简化方法。基于空间的点云简化方法,利用八叉树或空间栅格对点云进行空间划分,将细分后空间中的点用一个点进行代替,执行速度快,但点云特征缺失较严重;基于法向误差的点云简化方法,考虑了点云的局部几何特征,但是容易产生孔洞;基于曲面变化度的点云简化方法,能够有效控制点云和数据点的分布,但执行速度较慢;混合简化方法,根据点数和曲面变化度采用八叉树划分点云空间,只保留叶结点中距离点集重心最近的数据点,该方法执行速度较快,但是难以充分保留点云中的几何特征。With the continuous improvement of measurement accuracy of 3D scanners, the collected point clouds are becoming denser and contain rich details. However, the huge point cloud data brings a lot of inconvenience to subsequent processing, storage, display and transmission. If it is processed directly, it will inevitably take up a lot of hardware resources and time, and not all data points need to be used for subsequent processing. Too dense point cloud data will affect the quality of 3D object reconstruction during the visualization process. The existing processing methods mainly include space-based point cloud simplification methods, normal-based point cloud simplification methods, surface change-based point cloud simplification methods and hybrid simplification methods. The space-based point cloud simplification method uses octree or spatial grid to divide the point cloud space, and replaces the points in the subdivided space with one point. The execution speed is fast, but the point cloud features are seriously missing; based on The point cloud simplification method based on normal error takes into account the local geometric features of the point cloud, but it is prone to holes; the point cloud simplification method based on the degree of surface change can effectively control the distribution of point cloud and data points, but the execution speed is slow; The hybrid simplification method uses an octree to divide the point cloud space according to the number of points and the degree of surface change, and only retains the data points closest to the center of gravity of the point set in the leaf nodes. This method executes faster, but it is difficult to fully preserve the geometry in the point cloud feature.
发明内容Contents of the invention
针对大场景、高精度的三维激光扫描点云数据庞大,直接存储占用空间大,无法直接进行传输、三维建模、显示等缺点,本发明提供一种用于三维激光测量点云数据的简化方法,能够支持大场景、高精度三维激光扫描点云数据的快速简化,有效减少数据冗余,同时保持点云数据中关键特征和细节。Aiming at the large scene, high-precision 3D laser scanning point cloud data is huge, direct storage takes up a lot of space, and cannot be directly transmitted, 3D modeling, display and other shortcomings, the present invention provides a simplified method for 3D laser measurement point cloud data , can support the rapid simplification of large-scene, high-precision 3D laser scanning point cloud data, effectively reduce data redundancy, while maintaining key features and details in point cloud data.
一种用于三维激光测量点云数据的简化方法的技术方案如下。 A technical scheme of a simplified method for three-dimensional laser measurement point cloud data is as follows.
1)数据点领域搜索1) Data point field search
(1)点云空间划分(1) Point cloud space division
采用均匀栅格法对散乱点云进行空间划分。首先读取散乱点云数据,获得数据点集在X、Y、Z 坐标轴上的最大值和最小值,建立与坐标轴平行的包含所有数据点的长方体包围盒。将包围盒等分成均匀栅格,建立点云数据对应的栅格索引,一个栅格可以包含多个数据点。The uniform grid method is used to spatially partition the scattered point cloud. First read the scattered point cloud data, obtain the maximum and minimum values of the data point set on the X, Y, and Z coordinate axes, and establish a cuboid bounding box containing all data points parallel to the coordinate axes. Divide the bounding box into uniform grids, and establish the grid index corresponding to the point cloud data. A grid can contain multiple data points.
(2)构建包围球 (2) Construct the enclosing sphere
以点云中的数据点为球心构建包围球,并用线性链表记录包围球中除球心外的所有数据点。利用栅格间的空间位置关系快速构建包围球,根据点p的坐标值计算得到点p所在的立方体栅格索引号及相邻的26 个立方体栅格,分别计算栅格中的每个数据点与点p 间的距离,若距离小于等于包围球的半径R,则将该点追加到包围球对应的线性链表中。重复以上步骤,直到点云中的每个数据点都对应一个包围球。Construct the enclosing sphere with the data points in the point cloud as the center of the sphere, and use a linear linked list to record all the data points in the enclosing sphere except the center of the sphere. Use the spatial position relationship between the grids to quickly construct the enclosing sphere, calculate the cube grid index number where the point p is located and the 26 adjacent cube grids according to the coordinate value of the point p, and calculate each data point in the grid separately The distance from point p, if the distance is less than or equal to the radius R of the enclosing sphere, then add this point to the linear linked list corresponding to the enclosing sphere. Repeat the above steps until each data point in the point cloud corresponds to a bounding sphere.
(3)K领域搜索(3) K field search
对于数据点p,可从以点p为球心的包围球中找出距离点p 最近的K个数据点即可。对于边界点和孤立噪声点,可能在其包围球内邻居数据点少于K个,将这类数据点进行标记,在曲面变分计算步骤中需要特殊处理,若实际邻居数据点数量小于K的20%,可认为该数据点为噪音点,进行标记将不再参与后续运算。For a data point p, it is sufficient to find the K data points closest to the point p from the enclosing sphere with the point p as the center. For boundary points and isolated noise points, there may be less than K neighbor data points in the enclosing sphere. Marking such data points requires special treatment in the surface variation calculation step. If the actual number of neighbor data points is less than K 20%, the data point can be considered as a noise point, and it will no longer participate in subsequent calculations after marking.
2)曲面变分计算2) Calculation of surface variation
对于数据点p,根据点p的距离最近的K个数据点,计算点p的协方差矩阵的实数特征值,最小特征值对应的特征向量为点p的法向量。点p处的曲面变分为点p 的K邻域中数据点与点p的切平面间的距离之和。For data point p, calculate the real eigenvalues of the covariance matrix of point p according to the K data points closest to point p, and the eigenvector corresponding to the smallest eigenvalue is the normal vector of point p. The surface change at point p is the sum of the distances between the data points in the K neighborhood of point p and the tangent plane of point p.
本发明采取均匀采样法估计点云的曲面变分平均值,在点云划分的栅格中均匀抽取20%的栅格,分别计算栅格中每个点的曲面变分, 然后计算平均值,得到点云曲面变分的估计值。对于包围球内邻居数据点少于K个的数据点,以实际邻居点数进行计算。The present invention adopts the uniform sampling method to estimate the average value of the surface variation of the point cloud, uniformly extracts 20% of the grids from the grids divided by the point cloud, calculates the surface variation of each point in the grid respectively, and then calculates the average value, Get an estimate of the point cloud surface variation. For data points with less than K neighbor data points in the enclosing sphere, the actual number of neighbor points is used for calculation.
3)点云区域划分3) Point cloud area division
(1)提取特征点(1) Extract feature points
计算数据点p所有领域点的投影残差值,以其中的最大值作为点p的残差值。计算所有数据点的残差值,取平均值作为特征点的判断阈值t,若数据点p的投影残差值大于阈值t,则认为该点为特征点。Calculate the projection residual value of all domain points of data point p, and take the maximum value as the residual value of point p. Calculate the residual value of all data points, and take the average value as the judgment threshold t of the feature point. If the projection residual value of the data point p is greater than the threshold t, the point is considered as a feature point.
(2)区域划分(2) Regional division
对所有数据点按曲面变分值从小到大排序,依次取出数据点,建立新的划分区域,寻找数据点的K领域,建立种子集,计算数据点与K领域中数据点的曲面变分角度差,若角度差小于阈值0.25,则把数据点加入当前划分区域;若角度差小于阈值0.3,则把数据点加入种子集合。当种子集合为空时,开始新的划分区域,把已经建立的区域加入到区域队列中。当所有数据处理完毕时,点云的区域划分完成。Sort all data points according to the surface variation value from small to large, take out the data points in turn, create a new division area, find the K field of the data point, establish a seed set, and calculate the surface variation angle between the data point and the data point in the K field If the angle difference is less than the threshold 0.25, add the data point to the current divided area; if the angle difference is less than the threshold 0.3, add the data point to the seed set. When the seed set is empty, start a new division area, and add the established area to the area queue. When all the data has been processed, the region division of the point cloud is completed.
4)点云简化4) Point cloud simplification
将所有点云数据标记为保留状态。从区域队列中依次取出区域的点云数据,随机选择2000个特征点,计算特征点间的曲面变分差值,若曲面变分差值的平均值小于0.12,则该区域按平面处理,采用均匀采样法,计算点云数据的包围盒并均匀划分,每个栅格中的数据点集g用中心点代替,原有数据点标注为删除;否则从区域数据点集中依次取出数据点p。如果点p为非特征点且标记为保留,则计算点p的领域点与点p的曲面变分差值,若大于阈值0.6且该点标注为保留,则将该点标记为删除。当所有划分区域处理完毕,删除标记为删除的数据点,即可得到简化后的点云数据。Mark all point cloud data as preserved. The point cloud data of the area is sequentially taken out from the area queue, 2000 feature points are randomly selected, and the surface variation difference between the feature points is calculated. If the average value of the surface variation difference is less than 0.12, the area is treated as a plane, using The uniform sampling method calculates the bounding box of the point cloud data and divides it evenly. The data point set g in each grid is replaced by the center point, and the original data point is marked as deleted; otherwise, the data point p is sequentially taken out from the regional data point set. If point p is a non-feature point and marked as reserved, calculate the surface variation difference between the domain point of point p and point p, if it is greater than the threshold 0.6 and mark the point as reserved, then mark the point as deleted. When all the divided areas are processed, delete the data points marked as deleted, and the simplified point cloud data can be obtained.
本发明的方法适用于大场景、高精度三维激光扫描点云数据。与现有技术相比,本发明的有益效果是:可对数据量大于1亿点的高精度扫描数据进行快速简化,执行速度快,在有效降低数据容量的同时,能够保持场景中的关键特征点,有利于后期的三维建模等工作开展。The method of the present invention is suitable for large scenes and high-precision three-dimensional laser scanning point cloud data. Compared with the prior art, the beneficial effect of the present invention is that it can quickly simplify the high-precision scanning data with a data volume greater than 100 million points, the execution speed is fast, and the key features in the scene can be maintained while effectively reducing the data capacity point, which is conducive to the development of 3D modeling and other work in the later stage.
具体实施方式detailed description
1)数据点领域搜索1) Data point field search
(1)点云空间划分(1) Point cloud space division
采用均匀栅格法对散乱点云进行空间划分。首先读取散乱点云数据,获得数据点集在X、Y、Z 坐标轴上的最大值和最小值,建立与坐标轴平行的包含所有数据点的长方体包围盒,包围盒边长为、、。将包围盒划分为个边长为的均匀栅格,以M为例说明,。建立点云数据对应的栅格索引,假充点的坐标值为(x,y,z),其对应的栅格索引号(i,j,k)为:、、。一个栅格可以包含多个数据点。The uniform grid method is used to spatially partition the scattered point cloud. First read the scattered point cloud data, obtain the maximum and minimum values of the data point set on the X, Y, and Z coordinate axes, and establish a rectangular parallelepiped bounding box containing all data points parallel to the coordinate axis. The side length of the bounding box is , , . Divide the bounding box into side length is A uniform grid of , taking M as an example, . Establish the grid index corresponding to the point cloud data. The coordinate value of the fake point is (x, y, z), and the corresponding grid index number (i, j, k) is: , , . A raster can contain multiple data points.
(2)构建包围球(2) Construct the enclosing sphere
以点云中的数据点为球心构建包围球,包围球半径R=0.15 ,并用线性链表记录包围球中除球心外的所有数据点。可利用栅格间的空间位置关系快速构建包围球,根据点p的坐标值计算得到点p所在的立方体栅格索引号,以点p为球心的包围球所包含的数据点不仅位于对应的栅格内,还可能位于与点p 所在立方体栅格相邻的26 个立方体栅格中,假设点p所在栅格的索引号为(i,j,k),那么相邻栅格的索引号分别为,分别计算栅格中的每个数据点与点p 间的距离,如果距离小于等于包围球的半径R,则将该点追加到包围球对应的线性链表中。重复以上步骤,直到点云中的每个数据点都对应一个包围球。Construct a surrounding sphere with the data points in the point cloud as the center of the sphere, the radius of the surrounding sphere is R=0.15, and use a linear linked list to record all data points in the surrounding sphere except the center of the sphere. The spatial position relationship between the grids can be used to quickly construct the bounding sphere, and the cube grid index number where the point p is located can be calculated according to the coordinate value of the point p. The data points contained in the bounding sphere with the point p as the center of the sphere are not only located In the grid, it may also be located in the 26 cubic grids adjacent to the cube grid where the point p is located. Assuming that the index number of the grid where the point p is located is (i, j, k), then the index number of the adjacent grid respectively , respectively calculate the distance between each data point in the grid and point p, if the distance is less than or equal to the radius R of the enclosing sphere, then add the point to the linear linked list corresponding to the enclosing sphere. Repeat the above steps until each data point in the point cloud corresponds to a bounding sphere.
(3)领域搜索(3) Field search
对于数据点p,可从以点p为球心的包围球中找出距离点p 最近的K个数据点即可。对于边界点和孤立噪声点,可能在其包围球内邻居数据点少于K个,将这类数据点进行标记,在曲面变分计算步骤中需要特殊处理,若实际邻居数据点数量小于K的20%,可认为该数据点为噪音点,进行标记将不再参与后续运算。For a data point p, it is sufficient to find the K data points closest to the point p from the enclosing sphere with the point p as the center. For boundary points and isolated noise points, there may be less than K neighbor data points in the enclosing sphere. Marking such data points requires special treatment in the surface variation calculation step. If the actual number of neighbor data points is less than K 20%, the data point can be considered as a noise point, and it will no longer participate in subsequent calculations after marking.
2)曲面变分计算2) Calculation of surface variation
对于数据点p,根据点p的距离最近的K个数据点,点p的协方差矩阵定义见公式1。For data point p, according to the K data points closest to point p, the covariance matrix of point p is defined in formula 1.
(公式1) (Formula 1)
其中。C是对称半正定矩阵, 存在3个实数特征值且满足,对应的特征向量为,即点p的法向量。点p处的曲面变分由公式2计算得到。in . C is a symmetric positive semi-definite matrix, which has 3 real eigenvalues and satisfies , The corresponding eigenvectors are , which is the normal vector of point p. The surface variation at point p is calculated by Equation 2.
(公式2) (Formula 2)
表示点p 的邻域中第j个数据点与点p的切平面间的距离。 Indicates the distance between the jth data point in the neighborhood of point p and the tangent plane of point p.
本发明采取均匀采样法估计点云的曲面变分平均值,在点云划分的栅格中均匀抽取20%的栅格,分别计算栅格中每个点的曲面变分, 然后计算平均值,得到点云曲面变分的估计值,n为20%栅格里包含的数据点个数。对于包围球内邻居数据点少于K个的数据点,以实际邻居点数进行计算。The present invention adopts the uniform sampling method to estimate the average value of the surface variation of the point cloud, uniformly extracts 20% of the grids from the grids divided by the point cloud, calculates the surface variation of each point in the grid respectively, and then calculates the average value, Get an estimate of the point cloud surface variation , n is the number of data points contained in the 20% grid. For data points with less than K neighbor data points in the enclosing sphere, the actual number of neighbor points is used for calculation.
3)点云区域划分3) Point cloud area division
(1)提取特征点(1) Extract feature points
计算数据点p所有领域点的投影残差值,以其中的最大值作为点p的残差值。计算所有数据点的残差值,取平均值作为特征点的判断阈值t,若数据点p的投影残差值大于阈值t,则认为该点为特征点。Calculate the projection residual value of all domain points of data point p, and take the maximum value as the residual value of point p. Calculate the residual value of all data points, and take the average value as the judgment threshold t of the feature point. If the projection residual value of the data point p is greater than the threshold t, the point is considered as a feature point.
(2)区域划分(2) Regional division
步骤1:将所有数据点按曲面变分值按从小到大的顺序进行排序,记录在数据集A中。Step 1: Sort all data points in descending order according to the surface variation value, and record them in dataset A.
步骤2:选取数据集A中曲面变分值最小的数据点p放入种子集合s进行运算。建立区域集合Ri,设置为空。Step 2: Select the data point p with the smallest surface variation value in the data set A and put it into the seed set s for operation. Create a set of regions Ri, set to empty.
步骤3:当种子集合不为空时,取出种子集合中的种子q,寻找种子q的邻居点集N(q)。Step 3: When the seed set is not empty, take out the seed q in the seed set, and find the neighbor point set N(q) of the seed q.
步骤4:计算邻居点qi与种子点q之间的曲面变分角度差,若当前邻居点未标记且曲面变分角度差小于阈值0.25,则把当前邻居点放入区域集Ri并标记该数据点为已经加入区域状态,并从数据集A中移除。Step 4: Calculate the surface variation angle difference between the neighbor point qi and the seed point q , if the current neighbor point is not marked and the surface variation angle difference is less than the threshold 0.25, put the current neighbor point into the region set Ri and mark the data point as having joined the region state, and remove it from the data set A.
步骤5:如果当前邻居点的曲面变分值小于阈值0.3,则把当前邻居点加入到种子集合s。Step 5: If the surface variation value of the current neighbor point is less than the threshold 0.3, add the current neighbor point to the seed set s.
步骤6:如果邻居点集N(q)不为空时,返回执行步骤4。Step 6: If the neighbor point set N(q) is not empty, return to step 4.
步骤7:当种子集合s不为空时,返回执行步骤3。Step 7: When the seed set s is not empty, return to step 3.
步骤8:将新划分的区域Ri记录到区域划分队列中。Step 8: Record the newly divided region Ri into the region division queue.
步骤9:当数据点集A中还有数据点时,返回执行步骤2,并将种子集合清空。Step 9: When there are still data points in data point set A, return to step 2 and clear the seed set.
4)点云简化4) Point cloud simplification
步骤1:将点集中所有数据点标记为保留。Step 1: Mark all data points in the point set as reserved.
步骤2:从区域队列中依次取出一个区域的点云数据,随机选择2000个特征点,计算特征点间的曲面变分差值。Step 2: Take the point cloud data of an area sequentially from the area queue, randomly select 2000 feature points, and calculate the surface variation difference between feature points.
步骤3:若曲面变分差值的平均值小于0.12,则该区域按平面处理,采用均匀采样法,计算点云数据的包围盒,按边长对包围盒进行划分,每个栅格中的数据点集g用新的中心点代替,即,原有数据点标注为删除。否则执行步骤4。Step 3: If the average value of the surface variation difference is less than 0.12, the area is treated as a plane, and the uniform sampling method is used to calculate the bounding box of the point cloud data, according to the side length The bounding box is divided, and the data point set g in each grid is replaced by a new center point, namely , the original data points are marked as deleted. Otherwise, go to step 4.
步骤4:从区域数据点集中依次取出数据点p。如果点p为非特征点且标记为保留,则计算点p的领域点与点p的曲面变分差值,若大于阈值0.6且该点标注为保留,则将该点标记为删除。Step 4: Take out data point p sequentially from the regional data point set. If point p is a non-feature point and marked as reserved, calculate the surface variation difference between the domain point of point p and point p, if it is greater than the threshold 0.6 and mark the point as reserved, then mark the point as deleted.
步骤5:若区域点云中还有数据,则执行步骤4。Step 5: If there is still data in the area point cloud, go to step 4.
步骤6:若区域队列中还有区域,则执行步骤2。Step 6: If there are still zones in the zone queue, go to step 2.
步骤7:删除标记为删除的数据点,得到简化后的点云数据。Step 7: Delete the data points marked for deletion to obtain the simplified point cloud data.
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