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 PDF

Info

Publication number
CN107341825A
CN107341825A CN201710545946.XA CN201710545946A CN107341825A CN 107341825 A CN107341825 A CN 107341825A CN 201710545946 A CN201710545946 A CN 201710545946A CN 107341825 A CN107341825 A CN 107341825A
Authority
CN
China
Prior art keywords
point
data
point cloud
data point
cloud
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201710545946.XA
Other languages
Chinese (zh)
Inventor
陈永辉
张春峰
吴亚东
毕国堂
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Southwest University of Science and Technology
Original Assignee
Southwest University of Science and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Southwest University of Science and Technology filed Critical Southwest University of Science and Technology
Priority to CN201710545946.XA priority Critical patent/CN107341825A/en
Publication of CN107341825A publication Critical patent/CN107341825A/en
Pending legal-status Critical Current

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/20Finite element generation, e.g. wire-frame surface description, tesselation
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/50Depth or shape recovery
    • G06T7/521Depth or shape recovery from laser ranging, e.g. using interferometry; from the projection of structured light
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10028Range image; Depth image; 3D point clouds

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Graphics (AREA)
  • Geometry (AREA)
  • Software Systems (AREA)
  • Optics & Photonics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Length Measuring Devices By Optical Means (AREA)

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

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A simplified method for high-precision 3D laser measurement point cloud data in large scenes

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.

Claims (1)

1. a kind of technical scheme of method for simplifying for large scene high-precision three-dimensional laser measurement cloud data is as follows:
(1)Space division is carried out to dispersion point cloud using uniform cube-algorithm first, establishes grid index corresponding to cloud data;With Data point in point cloud surrounds ball for centre of sphere structure, and all data points surrounded in ball in addition to the centre of sphere are recorded with linear linked list; For each data point, K nearest data point of range points is found out using surrounding in ball, establishes K fields;
(2)For each data point, according to the normal vector of K fields calculating point, the K that the curved surface variation at defining point p is point p is adjacent In domain between data point and point p section apart from sum, take the curved surface variation average value of uniform sampling method estimation point cloud;When Data point of the neighbor data point less than K in ball is surrounded, is calculated with actual neighbors points;
(3)For each data point of a cloud, the projection residual errors value in the K fields of the point is calculated, point p is used as using maximum therein Residual values, using the average value of the residual values of all data points as the judgment threshold of characteristic point, if the projection residual errors of data point Value is more than threshold value, then judges that data point is characterized a little;All data points are sorted from small to large by curved surface variation value, taken out successively Data point, new zoning is established, the curved surface variation differential seat angle of data point and data point in K fields is calculated, if differential seat angle is small In threshold value, then data point is added current zoning;If differential seat angle is less than threshold value, data point is added seed set;When Seed set is space-time, starts new zoning, and the region having built up is added in area queue;At all data When reason finishes, the region division for putting cloud is completed;
(4)All cloud datas are labeled as reserved state;
(5)Take out the cloud data in region successively from area queue, randomly choose characteristic point, the curved surface calculated between characteristic point becomes Divide difference, if the average value of curved surface variation difference is less than threshold value, plane treatment is pressed in the region, and letter is carried out using uniform sampling method Change and be labeled as deleting by legacy data point;Otherwise concentrated from area data point and take out data point successively, calculate the K necks of data point The curved surface variation difference of domain point and data point, determine to retain or delete;When all zonings are disposed, deletion is labeled as The data point of deletion, you can the cloud data after being simplified.
CN201710545946.XA 2017-07-06 2017-07-06 A kind of method for simplifying for large scene high-precision three-dimensional laser measurement cloud data Pending CN107341825A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710545946.XA CN107341825A (en) 2017-07-06 2017-07-06 A kind of method for simplifying for large scene high-precision three-dimensional laser measurement cloud data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710545946.XA CN107341825A (en) 2017-07-06 2017-07-06 A kind of method for simplifying for large scene high-precision three-dimensional laser measurement cloud data

Publications (1)

Publication Number Publication Date
CN107341825A true CN107341825A (en) 2017-11-10

Family

ID=60218454

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710545946.XA Pending CN107341825A (en) 2017-07-06 2017-07-06 A kind of method for simplifying for large scene high-precision three-dimensional laser measurement cloud data

Country Status (1)

Country Link
CN (1) CN107341825A (en)

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108830931A (en) * 2018-05-23 2018-11-16 上海电力学院 A kind of laser point cloud compressing method based on dynamic grid k neighborhood search
CN109410342A (en) * 2018-09-28 2019-03-01 昆明理工大学 A kind of point cloud compressing method retaining boundary point
CN109961512A (en) * 2019-03-19 2019-07-02 汪俊 The airborne data reduction method and device of landform
CN110361017A (en) * 2019-07-19 2019-10-22 西南科技大学 A kind of full traverse path planing method of sweeping robot based on Grid Method
CN110457499A (en) * 2019-07-19 2019-11-15 广州启量信息科技有限公司 Indexing means, device, terminal device and the medium of a kind of pair large-scale point cloud data
WO2021016751A1 (en) * 2019-07-26 2021-02-04 深圳市大疆创新科技有限公司 Method for extracting point cloud feature points, point cloud sensing system, and mobile platform
CN112613528A (en) * 2020-12-31 2021-04-06 广东工业大学 Point cloud simplification method and device based on significance variation and storage medium
CN112884903A (en) * 2021-03-22 2021-06-01 浙江浙能兴源节能科技有限公司 Driving three-dimensional modeling system and method thereof
CN112923916A (en) * 2019-12-06 2021-06-08 杭州海康机器人技术有限公司 Map simplifying method and device, electronic equipment and machine-readable storage medium
CN113137919A (en) * 2021-04-29 2021-07-20 中国工程物理研究院应用电子学研究所 Laser point cloud rasterization method
CN113689326A (en) * 2021-08-06 2021-11-23 西南科技大学 Three-dimensional positioning method based on two-dimensional image segmentation guidance
CN114299039A (en) * 2021-12-30 2022-04-08 广西大学 Robot and collision detection device and method thereof
CN114359204A (en) * 2021-12-29 2022-04-15 广州极飞科技股份有限公司 Method, device and electronic device for detecting void in point cloud
US12106526B2 (en) 2018-07-11 2024-10-01 Interdigital Vc Holdings, Inc. Processing a point cloud

Cited By (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108830931B (en) * 2018-05-23 2022-07-01 上海电力学院 A laser point cloud reduction method based on dynamic grid k-neighbor search
CN108830931A (en) * 2018-05-23 2018-11-16 上海电力学院 A kind of laser point cloud compressing method based on dynamic grid k neighborhood search
US12106526B2 (en) 2018-07-11 2024-10-01 Interdigital Vc Holdings, Inc. Processing a point cloud
CN109410342A (en) * 2018-09-28 2019-03-01 昆明理工大学 A kind of point cloud compressing method retaining boundary point
CN109961512A (en) * 2019-03-19 2019-07-02 汪俊 The airborne data reduction method and device of landform
CN110361017B (en) * 2019-07-19 2022-02-11 西南科技大学 A full traversal path planning method for sweeping robot based on grid method
CN110361017A (en) * 2019-07-19 2019-10-22 西南科技大学 A kind of full traverse path planing method of sweeping robot based on Grid Method
CN110457499A (en) * 2019-07-19 2019-11-15 广州启量信息科技有限公司 Indexing means, device, terminal device and the medium of a kind of pair large-scale point cloud data
WO2021016751A1 (en) * 2019-07-26 2021-02-04 深圳市大疆创新科技有限公司 Method for extracting point cloud feature points, point cloud sensing system, and mobile platform
CN112923916A (en) * 2019-12-06 2021-06-08 杭州海康机器人技术有限公司 Map simplifying method and device, electronic equipment and machine-readable storage medium
CN112613528B (en) * 2020-12-31 2023-11-03 广东工业大学 A point cloud simplification method, device and storage medium based on saliency variation
CN112613528A (en) * 2020-12-31 2021-04-06 广东工业大学 Point cloud simplification method and device based on significance variation and storage medium
CN112884903A (en) * 2021-03-22 2021-06-01 浙江浙能兴源节能科技有限公司 Driving three-dimensional modeling system and method thereof
CN113137919A (en) * 2021-04-29 2021-07-20 中国工程物理研究院应用电子学研究所 Laser point cloud rasterization method
CN113137919B (en) * 2021-04-29 2022-10-28 中国工程物理研究院应用电子学研究所 Laser point cloud rasterization method
CN113689326A (en) * 2021-08-06 2021-11-23 西南科技大学 Three-dimensional positioning method based on two-dimensional image segmentation guidance
CN113689326B (en) * 2021-08-06 2023-08-04 西南科技大学 A 3D Positioning Method Based on 2D Image Segmentation Guidance
CN114359204A (en) * 2021-12-29 2022-04-15 广州极飞科技股份有限公司 Method, device and electronic device for detecting void in point cloud
CN114299039A (en) * 2021-12-30 2022-04-08 广西大学 Robot and collision detection device and method thereof

Similar Documents

Publication Publication Date Title
CN107341825A (en) A kind of method for simplifying for large scene high-precision three-dimensional laser measurement cloud data
CN111932688B (en) Indoor plane element extraction method, system and equipment based on three-dimensional point cloud
CN108830931B (en) A laser point cloud reduction method based on dynamic grid k-neighbor search
CN107223268B (en) Three-dimensional point cloud model reconstruction method and device
CN107123164A (en) Keep the three-dimensional rebuilding method and system of sharp features
CN104616349B (en) Scattered point cloud data based on local surface changed factor simplifies processing method
WO2019019680A1 (en) Point cloud attribute compression method based on kd tree and optimized graph transformation
CN102890828B (en) Point cloud data compacting method based on normal included angle
CN107392875A (en) A kind of cloud data denoising method based on the division of k neighbours domain
CN113379898B (en) A 3D Indoor Scene Reconstruction Method Based on Semantic Segmentation
CN107122396A (en) Three-dimensional model searching algorithm based on depth convolutional neural networks
CN103310481B (en) A kind of point cloud compressing method based on fuzzy entropy iteration
CN102750730B (en) Characteristic-maintained point cloud data compacting method
CN112634457B (en) Point cloud simplification method based on local entropy of Hausdorff distance and average projection distance
CN101650838A (en) Point cloud simplification processing method based on resampling method and affine clustering algorithm
CN101853485A (en) A Simplified Processing Method for Non-uniform Point Clouds Based on Neighbor Propagation Clustering
CN112614216B (en) Variable-curvature self-adaptive point cloud data down-sampling method
CN111340723A (en) A terrain-adaptive thin-plate spline interpolation filtering method for airborne LiDAR point cloud regularization
CN104183020B (en) Atural object mesh simplification method based on the local secondary error measure with penalty term
CN102855661B (en) Large-scale forest scene quick generation method based on space similarity
CN107133496A (en) Gene expression characteristicses extracting method based on manifold learning Yu closed loop depth convolution dual network model
CN111310811B (en) Large-scene three-dimensional point cloud classification method based on multi-dimensional feature optimal combination
CN102800114A (en) Data point cloud downsizing method based on Poisson-disk sampling
Friedrich et al. Optimizing evolutionary CSG tree extraction
CN105069845A (en) Point cloud simplification method based on curved surface change

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20171110

WD01 Invention patent application deemed withdrawn after publication