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
The present invention relates to a kind of method for simplifying for large scene high-precision three-dimensional laser measurement cloud data, the quick simplification of large scene, high-precision three-dimensional Point Cloud of Laser Scanner can be supported, key feature in cloud data is kept simultaneously, belongs to dimensional Modeling Technology field.The present invention carries out space uniform division using uniform cube-algorithm to dispersion point cloud, establishes grid index corresponding to cloud data, the K neighborhoods of the quick searching data point in utilization space position；Point cloud characteristic point is extracted according to projection residual errors value, region division is carried out to cloud data using curved surface variation value；Curved surface variation value using region division and data point simplifies to original point cloud, finally gives the cloud data after simplifying.The method of the present invention can quickly be simplified to high-precision scan data of the data volume more than 100,000,000 points, and execution speed is fast, while data capacity is effectively reduced, can keep the key feature points in scene, be advantageous to the work such as the three-dimensional modeling in later stage development.
Description
Technical field
The present invention relates to a kind of method for simplifying for large scene high-precision three-dimensional laser measurement cloud data, it would be preferable to support
The quick simplification of large scene, high-precision three-dimensional Point Cloud of Laser Scanner, while key feature in cloud data is kept, belong to three
Tie up modeling technique field.
Background technology
Improved constantly with the measurement accuracy of spatial digitizer, the point collected milks up, and contains abundant
Detailed information.But huge cloud data brings many inconvenience to subsequent treatment and storage, display, transmission.If
Directly it is handled, substantial amounts of hardware resource and time certainly will be taken, and not all data point is required for using
In subsequent treatment, overstocked cloud data can influence the quality of three-dimensional body reconstruct in visualization process.Existing processing method
Mainly there are the point cloud simplification method based on space, the point cloud simplification method based on normal direction, the point cloud simplification based on curved surface change degree
Method and mixing method for simplifying.Point cloud simplification method based on space, space is carried out to a cloud using Octree or space lattice
Division, the point in space after subdivision is replaced with a point, and execution speed is fast, but it is more serious to put cloud feature missing；It is based on
The point cloud simplification method of normal error, it is contemplated that put the local geometric features of cloud, but easily produce hole；Changed based on curved surface
The point cloud simplification method of degree, it is capable of the distribution of effectively control point cloud and data point, but it is slower to perform speed；Mix method for simplifying,
Octree division points cloud space is used according to points and curved surface change degree, only retains number nearest apart from point set center of gravity in leaf node
Strong point, this method performs speed, it can be difficult to being sufficiently reserved the geometric properties in a cloud.
The content of the invention
Huge for large scene, high accuracy three-dimensional Point Cloud of Laser Scanner, directly storage space-consuming is big, Wu Fazhi
Connect be transmitted, three-dimensional modeling, display the shortcomings of, the present invention provide a kind of simplification side for three-dimensional laser measurement pointcloud data
Method, it would be preferable to support the quick simplification of large scene, high-precision three-dimensional Point Cloud of Laser Scanner, effectively reduce data redundancy, simultaneously
Keep key feature and details in cloud data.
A kind of technical scheme of method for simplifying for three-dimensional laser measurement pointcloud data is as follows.
1）Data point field is searched for
（1）The division of point cloud space
Space division is carried out to dispersion point cloud using uniform cube-algorithm.Scattered point cloud data is read first, is obtained data point set and is existed
X, the maximum and minimum value in Y, Z reference axis, establish the cuboid comprising all data points parallel with reference axis and surround
Box.Bounding box is divided into uniform lattice, establishes grid index corresponding to cloud data, a grid can include multiple data
Point.
（2）Structure surrounds ball
Built using the data point in a cloud as the centre of sphere and surround ball, and all numbers surrounded in ball in addition to the centre of sphere are recorded with linear linked list
Strong point.Ball is surrounded using the spatial relation rapid build between grid, according to where being calculated point p in point p coordinate value
Cubic lattice call number and adjacent 26 cubic lattices, respectively between each data point in computation grid and point p
Distance, if distance is less than or equal to the radius R for surrounding ball, the point is appended to and surrounded in linear linked list corresponding to ball.Repeat with
Upper step, until the corresponding encirclement ball of each data point in a cloud.
（3）K fields are searched for
For data point p, K nearest data point of range points p can be found out from the encirclement ball using point p as the centre of sphere.For
Boundary point and isolated noise point, neighbor data point in ball may be surrounded at it and, less than K, this kind of data point is marked,
Specially treated is needed in curved surface variation calculation procedure, if actual neighbors number of data points is less than the 20% of K, it is believed that the data point
For noise point, subsequent arithmetic will be no longer participate in by being marked.
2）Curved surface variation calculates
For data point p, according to point p K closest data point, the real number characteristic value of point p covariance matrix is calculated,
Characteristic vector corresponding to minimal eigenvalue is point p normal vector.In the K neighborhoods that curved surface variation at point p is point p data point and
Between point p section apart from sum.
The present invention takes the curved surface variation average value of uniform sampling method estimation point cloud, is uniformly taken out in a grid of cloud division
20% grid is taken, the curved surface variation each put in computation grid respectively, then calculates average value, obtains point cloud surface variation
Estimate.For surrounding data point of the neighbor data point less than K in ball, calculated with actual neighbors points.
3）Point cloud region division
（1）Extract characteristic point
Calculate the projection residual errors value of data point p all spectra points, the residual values using maximum therein as point p.Calculate all
The residual values of data point, the judgment threshold t to average as characteristic point, if data point p projection residual errors value is more than threshold value t,
Then think that the point is characterized a little.
（2）Region division
All data points are sorted from small to large by curved surface variation value, take out data point successively, establish new zoning, are found
The K fields of data point, subset 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 0.25, then data point is added current zoning；If differential seat angle is less than threshold value 0.3, data point is added seed
Set.When seed set is space-time, starts new zoning, the region having built up is added in area queue.Work as institute
When having the data processing to finish, the region division for putting cloud is completed.
4）Point cloud simplification
All cloud datas are labeled as reserved state.Take out the cloud data in region successively from area queue, randomly choose
2000 characteristic points, the curved surface variation difference between characteristic point is calculated, if the average value of curved surface variation difference is less than 0.12, Ze Gai areas
Plane treatment is pressed in domain, using uniform sampling method, is calculated the bounding box of cloud data and is evenly dividing, the data point in each grid
Collection g is replaced with central point, and legacy data point is labeled as deleting；Otherwise concentrated from area data point and take out data point p successively.If
Point p is non-characteristic point and is labeled as retaining, then point p curved surface variation difference of the field point with point p is calculated, if being more than threshold value 0.6
And the point is labeled as retaining, then by the point labeled as deletion.When all zonings are disposed, deletion is labeled as the number deleted
Strong point, you can the cloud data after being simplified.
The method of the present invention is applied to large scene, high-precision three-dimensional Point Cloud of Laser Scanner.Compared with prior art, originally
The beneficial effect of invention is：High-precision scan data of the data volume more than 100,000,000 points can quickly be simplified, execution speed is fast,
While data capacity is effectively reduced, the key feature points in scene can be kept, are advantageous to the works such as the three-dimensional modeling in later stage
Work is carried out.
Embodiment
1）Data point field is searched for
（1）The division of point cloud space
Space division is carried out to dispersion point cloud using uniform cube-algorithm.Scattered point cloud data is read first, is obtained data point set and is existed
X, the maximum and minimum value in Y, Z reference axis, establish the cuboid comprising all data points parallel with reference axis and surround
Box, the bounding box length of side are、、.Bounding box is divided
ForThe individual length of side isUniform lattice, illustrate by taking M as an example,.Establish cloud data
Corresponding grid index, the coordinate value pretended to be a little are（x,y,z）, its corresponding grid index number（i,j,k）For：、、.One grid can include multiple data points.
（2）Structure surrounds ball
Built using the data point in a cloud as the centre of sphere and surround ball, surround radius of a ball R=0.15, and recorded with linear linked list and surround ball
In all data points in addition to the centre of sphere.Ball is surrounded using the spatial relation rapid build between grid, according to point p coordinate
The cubic lattice call number where point p is calculated in value, is not only located at using the point p data points included by the encirclement ball of the centre of sphere
In corresponding grid, it is also possible in 26 cubic lattices adjacent with cubic lattice where point p, it is assumed that point p institutes
It is in the call number of grid（I, j, k）, then the call number of adjacent cells is respectively, calculate respectively
The distance between each data point and point p in grid, if distance is less than or equal to the radius R for surrounding ball, the point is appended to
Surround in linear linked list corresponding to ball.Above step is repeated, until the corresponding encirclement ball of each data point in a cloud.
（3）Field is searched for
For data point p, K nearest data point of range points p can be found out from the encirclement ball using point p as the centre of sphere.For
Boundary point and isolated noise point, neighbor data point in ball may be surrounded at it and, less than K, this kind of data point is marked,
Specially treated is needed in curved surface variation calculation procedure, if actual neighbors number of data points is less than the 20% of K, it is believed that the data point
For noise point, subsequent arithmetic will be no longer participate in by being marked.
2）Curved surface variation calculates
For data point p, according to point p K closest data point, formula 1 is shown in point p covariance matrix definition.
（Formula 1）
Wherein.C is symmetric positive semidefinite matrix, 3 real number characteristic values and satisfaction be present,Corresponding characteristic vector is, i.e. point p normal vector.Curved surface variation at point p is calculated by formula 2.
（Formula 2）
Represent the distance between j-th of data point and point p section in point p neighborhood.
The present invention takes the curved surface variation average value of uniform sampling method estimation point cloud, is uniformly taken out in a grid of cloud division
20% grid is taken, the curved surface variation each put in computation grid respectively, then calculates average value, obtains point cloud surface variation
Estimate, n is the data point number included in 20% grid.For surrounding neighbours' number in ball
Data point of the strong point less than K, calculated with actual neighbors points.
3）Point cloud region division
（1）Extract characteristic point
Calculate the projection residual errors value of data point p all spectra points, the residual values using maximum therein as point p.Calculate all
The residual values of data point, the judgment threshold t to average as characteristic point, if data point p projection residual errors value is more than threshold value t,
Then think that the point is characterized a little.
（2）Region division
Step 1：All data points are ranked up by curved surface variation value by order from small to large, are recorded in data set A.
Step 2：Choose the minimum data point p of data set A mean cambers change score value and be put into seed set s progress computings.Establish
Regional ensemble Ri, it is arranged to empty.
Step 3：When seed set is not space-time, the seed q in seed set is taken out, finds seed q neighbours' point set N
(q)。
Step 4：Calculate the curved surface variation differential seat angle between neighbours point qi and seed point qIt is if current adjacent
Occupy the unmarked and curved surface variation differential seat angle of point and be less than threshold value 0.25, then current neighbours point is put into set of regions Ri and marks the data
Point removes to have been added to zone state from data set A.
Step 5：If the curved surface variation value of current neighbours point is less than threshold value 0.3, current neighbours point is added to seed
Set s.
Step 6：If neighbours point set N (q) is not space-time, returns and perform step 4.
Step 7：When seed set s is not space-time, return performs step 3.
Step 8：The region Ri newly divided recorded in region division queue.
Step 9：When there is data point in data point set A, return and perform step 2, and seed set is emptied.
4）Point cloud simplification
Step 1：All data point markers are concentrated to retain by.
Step 2：Take out the cloud data in a region successively from area queue, randomly choose 2000 characteristic points, meter
Calculate the curved surface variation difference between characteristic point.
Step 3：If the average value of curved surface variation difference is less than 0.12, plane treatment is pressed in the region, using uniform sampling
Method, the bounding box of cloud data is calculated, by the length of sideBounding box is divided, the data point set g in each grid
Replaced with new central point, i.e.,, legacy data point is labeled as deleting.Otherwise step 4 is performed.
Step 4：Concentrated from area data point and take out data point p successively.As fruit dot p be non-characteristic point and labeled as retain,
Point p curved surface variation difference of the field point with point p is then calculated, if being labeled as retaining more than threshold value 0.6 and the point, the point is marked
It is designated as deleting.
Step 5：If there is data in region point cloud, step 4 is performed.
Step 6：If there is region in area queue, step 2 is performed.
Step 7：Delete labeled as the data point deleted, the cloud data after being simplified.
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.
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Cited By (12)
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| 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 |
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| 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 |
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| 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 |
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| 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 |
| CN108830931B (en) * | 2018-05-23 | 2022-07-01 | 上海电力学院 | Laser point cloud simplification 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 |
| CN110361017B (en) * | 2019-07-19 | 2022-02-11 | 西南科技大学 | Grid method based full-traversal path planning method for sweeping robot |
| 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 |
| 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 |
| CN112613528B (en) * | 2020-12-31 | 2023-11-03 | 广东工业大学 | Point cloud simplifying 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 |
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Application publication date: 20171110 |