CN109858571A - Laser radar point cloud power line classification method based on normal distribution and cluster - Google Patents
Laser radar point cloud power line classification method based on normal distribution and cluster Download PDFInfo
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Abstract
The laser radar point cloud power line classification method based on normal distribution and cluster that the invention discloses a kind of, comprising the following steps: (1) original point cloud data is pre-processed, establish digital terrain model, filtered using elevation and carry out the coarse extraction of power line candidate point；(2) for power line candidate's point data after coarse extraction, extraction is advanced optimized based on what normal distribution transform algorithm realized power line candidate point on three-dimensional space；(3) power line candidate's point data after extracting for optimization realizes the accurate extraction of power line point using means clustering algorithm.The present invention can realize that power line is classified in the laser radar point cloud data of the various complicateds such as city forest zone, and provide accurate electric power line drawing result, greatly improve point cloud data classification effectiveness, new approaches are provided for the classification of a variety of point cloud datas, provide accurate comprehensive analysis data for work such as electric power line inspections.
Description
Technical field
The present invention relates to technical field of data processing, in particular to a kind of laser radar point based on normal distribution and cluster
Cloud power line classification method.
Background technique
A kind of space exploration means of the laser radar technique as novel and high-efficiency, being capable of quick obtaining target in a short time
Scene largely has the point cloud data of accurate three-dimensional space coordinate, but compared to laser radar system hardware performance and index side
The software processing of the progress that face obtains, laser radar point cloud data is also in initial phase, obtains in face of laser radar hardware system
The mass cloud data taken, how effectively to carry out utilizing to it is that present laser radar points cloud data processing field is faced
One main problem.Meanwhile with the rapid development of each field economy in China, all trades and professions are very fast to the demand growth of electric power
Suddenly.In face of large-scale electric power networks and corresponding powerline network, in new power grid Bus stop planning, power circuit optimization, electricity
In all various practical problems such as net safety management and maintenance, the safety management of power optical fiber cable system and maintenance, height is needed
Reliable measuring technique is imitated to meet its economic technology requirement.
Using data along the power grid that lidar measurement technology acquires and handles, electric wire and electric power optical cable can be restored
Practical geological information, and then can be with the distance of automatic measurement electric wire to ground and adjacent wire spacing, and correlation can be passed through
It calculates and obtains electric wire, the whip degree of electric power communication optical cable, span etc., and can be realized by the above geometric parameter to electric wire, electricity
The derivation of the directly security parameter such as power optical cable tension or pulling force.As it can be seen that inventing a kind of electricity by utilizing laser radar point cloud data
The extraction method of the line of force can quick obtaining correlation space data and be the fining of electrical reticulation design and management, scientific, high
The offers support such as effectization.
Summary of the invention
In order to solve the above technical problem, the present invention provides a kind of classification effectiveness height, extraction accuracy are high based on normal state point
The laser radar point cloud power line classification method of cloth and cluster.
Technical proposal that the invention solves the above-mentioned problems is: a kind of based on the laser radar point cloud of normal distribution and cluster electricity
Line of force classification method, comprising the following steps:
(1) original point cloud data is pre-processed, establishes digital terrain model, filtered using elevation and carry out power line time
Reconnaissance coarse extraction；
(2) it for power line candidate's point data after coarse extraction, is realized on three-dimensional space based on normal distribution transform algorithm
Power line candidate point advanced optimizes extraction；
(3) power line candidate's point data after extracting for optimization realizes the essence of power line point using means clustering algorithm
Really extract.
It is above-mentioned based on the laser radar point cloud power line of normal distribution and cluster classify extracting method, the step (1)
Specific steps are as follows:
1-1) the strobe utility based on original point cloud data, significant non-electrical line of force point carries out data preprocessing；
Scene 1-2) is described according to original point cloud data quality, and designs 0.5 meter of ground seed point spacing；
Point cloud data 1-3) is subjected to piecemeal processing, several pockets is obtained, selects one in each pocket
A ground seed point；
Ground fitting 1-4) is carried out using obtained seed point, digital terrain model is obtained, is denoted as DTM；
DTM 1-5), which is subtracted, on the basis of original point cloud level journey obtains a cloud relative altitude information；
1-6) extracting relative altitude is the point of 4m and 4m or more as power line candidate point data set one.
It is above-mentioned based on the laser radar point cloud power line of normal distribution and cluster classify extracting method, the step (2)
Specific steps are as follows:
2-1) all power line candidate point coordinates are x, y, z in power line candidate point data set one, calculate separately power line
The difference of the maxima and minima of three axis of candidate point coordinate x, y, z takes three corresponding smallest positive integral difference not less than difference
For Ax、Ay、Az；
2-2) building size is Ax*Ay*AzCube, the minimum value of cube X-direction are the minimum of candidate point coordinate x
Value, the maximum value of cube X-direction are not less than a smallest positive integral for the maximum value of coordinate x；The minimum of cube Y direction
Value is the minimum value of point coordinate y, and the maximum value of cube Y direction is not less than a smallest positive integral for the maximum value of coordinate y；It is vertical
The minimum value of cube Z-direction is the minimum value of point coordinate z, and the maximum value of cube Z-direction is most not less than coordinate z
The smallest positive integral being worth greatly；
The cube of building 2-3) is divided into the cubic units of n 1m*1m*1m；
Power line candidate point 2-4) is separately dispensed into the cubic units for meeting grid bearing according to three-dimensional point coordinate value
In lattice；
2-5) count the point number in each cubic units, if point quantity less than 3, by the cubic units and cube
Point in body unit is rejected from data set one, to obtain optimizing updated power line candidate point data set one and updated
A cubic units of n '；
2-6) from a cubic units of data set one and its n ' that upper step obtains, point in each cubic units is calculated
The covariance matrix of coordinate, calculation formula are as follows:
In formula: cov indicates that covariance calculates operator；X, y, z is then the X-axis, Y-axis of point set, Z axis in each cubic units
Coordinate value；It is then the average value of the X-axis, Y-axis of point set in each cubic units, Z axis coordinate value；M is corresponding vertical
The intracorporal points in side；R is the three-dimensional covariance matrix of point set in corresponding cubic units；
2-7) according to 2-6) the covariance matrix R of cubic units that is calculated, according to conventional matrix algorithm meter
Calculation obtains three eigenvalue λs1、λ2And λ3, and set:
λ1≤λ2≤λ3(5)；
Threshold value t=0.02 2-8) is set, if λ2/λ3The cubic units are then labeled as linear cubic units by≤t, no
It is then non-linear shape cubic units；
Linear cubic units and its interior point data 2-9) are extracted as power line candidate point data set two.
It is above-mentioned based on the laser radar point cloud power line of normal distribution and cluster classify extracting method, the step (3)
Specific steps are as follows:
3-1) feature vector corresponding to maximum eigenvalue is used as in each cubic units in preservation data set two
It is worth the sample data of clustering algorithm, is denoted as data set three；
K 3-2) are visually chosen in data set three belongs to the feature vector of power line candidate point cubic units as kind
Subvector；
3-3) ergodic data collection three calculates angle α of each feature vector respectively between k Seeding vectorpq；Wherein p
Represent the corresponding feature vector of maximum eigenvalue of p-th of cubic units in data set three；Q represents q in k Seeding vector
A feature vector；αpqRepresent the angle in data set three between p-th of feature vector and q-th of Seeding vector；
Angle threshold value Δ=0.5 degree 3-4) is set, is judged in data set three between each feature vector and k Seeding vector
Angle αpqWith the size relation between angle threshold value Δ, if αpq< Δ, then αpqP-th of cube list in corresponding data set three
First feature vector belongs to q-th in k Seeding vector of Seeding vector group, k Seeding vector group is always obtained, if αpq> Δ, then
P-th of cubic units feature vector is not belonging to any Seeding vector group；
The normal vector for 3-5) calculating each feature vector group, using this k normal vector as new Seeding vector；
3-6) repeat step 3-3)-step 3-5), until normal vector is consistent with Seeding vector, end loop；
The cubic units and its interior point data being unsatisfactory for where the feature vector of angle threshold value 3-7) are rejected, then data
Remaining point data is final power line point in collection three, and the points in the cubic units where k feature vector group
According to the power line for belonging to k different directions.
The beneficial effects of the present invention are: the invention proposes a kind of laser radar point cloud based on normal distribution and cluster
The method of middle power line, first with digital terrain model and the progress power line coarse extraction of elevation threshold value is generated, followed by just
State distribution transformation algorithm realizes the further extraction of power line candidate point, finally realizes electricity using improved means clustering algorithm
The accurate extraction of line of force point.It can be in the laser radar point cloud number of the various complicateds such as city forest zone using method of the invention
Classify according to middle realization power line, and provide accurate electric power line drawing as a result, greatly improving point cloud data classification effectiveness, is
A variety of point cloud data classification provide new approaches, provide accurate comprehensive analysis data for work such as electric power line inspections.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Fig. 2 is normal vector and the consistent schematic diagram of Seeding vector in step of the present invention (3).
Specific embodiment
The present invention is further illustrated with reference to the accompanying drawings and examples.
As shown in Figure 1, a kind of laser radar point cloud power line classification method based on normal distribution and cluster, including it is following
Step:
(1) original point cloud data is pre-processed, establishes digital terrain model, filtered using elevation and carry out power line time
Reconnaissance coarse extraction.The specific steps of step (1) are as follows:
1-1) the filtering based on original point cloud data, conventional traditional significant non-electrical line of force point (noise spot, mistakes and omissions point etc.)
Mechanism carries out data preprocessing；
Scene 1-2) is described according to original point cloud data quality, and designs 0.5 meter of ground seed point spacing；
Point cloud data 1-3) is subjected to piecemeal processing, several pockets is obtained, selects one in each pocket
A ground seed point；
Ground fitting 1-4) is carried out using obtained seed point, digital terrain model is obtained, is denoted as DTM；
DTM 1-5), which is subtracted, on the basis of original point cloud level journey obtains a cloud relative altitude information；
1-6) extracting relative altitude is the point of 4m and 4m or more as power line candidate point data set one.
(2) it for power line candidate's point data after coarse extraction, is realized on three-dimensional space based on normal distribution transform algorithm
Power line candidate point advanced optimizes extraction.The specific steps of step (2) are as follows:
2-1) all power line candidate point coordinates are x, y, z in power line candidate point data set one, calculate separately power line
The difference of the maxima and minima of three axis of candidate point coordinate x, y, z takes three corresponding smallest positive integral difference not less than difference
For Ax、Ay、Az；
2-2) building size is Ax*Ay*AzCube, the minimum value of cube X-direction are the minimum of candidate point coordinate x
Value, the maximum value of cube X-direction are not less than a smallest positive integral for the maximum value of coordinate x；The minimum of cube Y direction
Value is the minimum value of point coordinate y, and the maximum value of cube Y direction is not less than a smallest positive integral for the maximum value of coordinate y；It is vertical
The minimum value of cube Z-direction is the minimum value of point coordinate z, and the maximum value of cube Z-direction is most not less than coordinate z
The smallest positive integral being worth greatly；
The cube of building 2-3) is divided into the cubic units of n 1m*1m*1m；
Power line candidate point 2-4) is separately dispensed into the cubic units for meeting grid bearing according to three-dimensional point coordinate value
In lattice；
2-5) count the point number in each cubic units, if point quantity less than 3, by the cubic units and cube
Point in body unit is rejected from data set one, to obtain optimizing updated power line candidate point data set one and updated
A cubic units of n '；
2-6) from a cubic units of data set one and its n ' that upper step obtains, point in each cubic units is calculated
The covariance matrix of coordinate, calculation formula are as follows:
In formula: cov indicates that covariance calculates operator；X, y, z is then the X-axis, Y-axis of point set, Z axis in each cubic units
Coordinate value；It is then the average value of the X-axis, Y-axis of point set in each cubic units, Z axis coordinate value；M is corresponding vertical
The intracorporal points in side；R is the three-dimensional covariance matrix of point set in corresponding cubic units；
2-7) according to 2-6) the covariance matrix R of cubic units that is calculated, according to conventional matrix algorithm meter
Calculation obtains three eigenvalue λs1、λ2And λ3, and set:
λ1≤λ2≤λ3(5)；
Threshold value t=0.02 2-8) is set, if λ2/λ3The cubic units are then labeled as linear cubic units by≤t, no
It is then non-linear shape cubic units；
Linear cubic units and its interior point data 2-9) are extracted as power line candidate point data set two.
(3) power line candidate's point data after extracting for optimization realizes the essence of power line point using means clustering algorithm
Really extract.The specific steps of step (3) are as follows:
3-1) feature vector corresponding to maximum eigenvalue is used as in each cubic units in preservation data set two
It is worth the sample data of clustering algorithm, is denoted as data set three；
K 3-2) are visually chosen in data set three belongs to the feature vector of power line candidate point cubic units as kind
Subvector；
3-3) ergodic data collection three calculates angle α of each feature vector respectively between k Seeding vectorpq；Wherein p
Represent the corresponding feature vector of maximum eigenvalue of p-th of cubic units in data set three；Q represents q in k Seeding vector
A feature vector；αpqRepresent the angle in data set three between p-th of feature vector and q-th of Seeding vector；
Angle threshold value Δ=0.5 degree 3-4) is set, is judged in data set three between each feature vector and k Seeding vector
Angle αpqWith the size relation between angle threshold value Δ, if αpq< Δ, then αpqP-th of cube list in corresponding data set three
First feature vector belongs to q-th in k Seeding vector of Seeding vector group, k Seeding vector group is always obtained, if αpq> Δ, then
P-th of cubic units feature vector is not belonging to any Seeding vector group；
The normal vector for 3-5) calculating each feature vector group, using this k normal vector as new Seeding vector；
3-6) repeat step 3-3)-step 3-5), until normal vector is consistent with Seeding vector, end loop is (such as Fig. 2 institute
Show, until all point values are assigned to some class cluster in k normal vector group)；
The cubic units and its interior point data being unsatisfactory for where the feature vector of angle threshold value 3-7) are rejected, then data
Remaining point data is final power line point in collection three, and the points in the cubic units where k feature vector group
According to the power line for belonging to k different directions.
Claims (4)
1. a kind of laser radar point cloud power line classification method based on normal distribution and cluster, comprising the following steps:
(1) original point cloud data is pre-processed, establishes digital terrain model, filtered using elevation and carry out power line candidate point
Coarse extraction；
(2) for power line candidate's point data after coarse extraction, electric power on three-dimensional space is realized based on normal distribution transform algorithm
Line candidate point advanced optimizes extraction；
(3) power line candidate's point data after extracting for optimization realizes accurately mentioning for power line point using means clustering algorithm
It takes.
2. it is according to claim 1 based on the laser radar point cloud power line of normal distribution and cluster classify extracting method,
It is characterized by: the specific steps of the step (1) are as follows:
1-1) the strobe utility based on original point cloud data, significant non-electrical line of force point carries out data preprocessing；
Scene 1-2) is described according to original point cloud data quality, and designs 0.5 meter of ground seed point spacing；
Point cloud data 1-3) is subjected to piecemeal processing, obtains several pockets, a ground is selected in each pocket
Face seed point；
Ground fitting 1-4) is carried out using obtained seed point, digital terrain model is obtained, is denoted as DTM；
DTM 1-5), which is subtracted, on the basis of original point cloud level journey obtains a cloud relative altitude information；
1-6) extracting relative altitude is the point of 4m and 4m or more as power line candidate point data set one.
3. it is according to claim 2 based on the laser radar point cloud power line of normal distribution and cluster classify extracting method,
It is characterized by: the specific steps of the step (2) are as follows:
2-1) all power line candidate point coordinates are x, y, z in power line candidate point data set one, calculate separately power line candidate
The difference of the maxima and minima of point three axis of coordinate x, y, z, taking three corresponding smallest positive integrals not less than difference is respectively Ax、
Ay、Az；
2-2) building size is Ax*Ay*AzCube, the minimum value of cube X-direction are the minimum value of candidate point coordinate x, are stood
The maximum value of cube X-direction is not less than a smallest positive integral for the maximum value of coordinate x；The minimum value of cube Y direction is
The minimum value of point coordinate y, the maximum value of cube Y direction are not less than a smallest positive integral for the maximum value of coordinate y；Cube
The minimum value of Z-direction is the minimum value of point coordinate z, and the maximum value of cube Z-direction is not less than a maximum value of coordinate z
Smallest positive integral；
The cube of building 2-3) is divided into the cubic units of n 1m*1m*1m；
2-4) power line candidate point is separately dispensed into the cubic units lattice for meeting grid bearing according to three-dimensional point coordinate value；
The point number in each cubic units 2-5) is counted, if putting quantity less than 3, by the cubic units and cube list
Point in member is rejected from data set one, a to obtain optimizing updated power line candidate point data set one and updated n '
Cubic units；
2-6) from a cubic units of data set one and its n ' that upper step obtains, calculates and put coordinate in each cubic units
Covariance matrix, calculation formula are as follows:
In formula: cov indicates that covariance calculates operator；X, y, z is then the X-axis, Y-axis of point set, Z axis coordinate in each cubic units
Value；It is then the average value of the X-axis, Y-axis of point set in each cubic units, Z axis coordinate value；M is corresponding cube
Interior points；R is the three-dimensional covariance matrix of point set in corresponding cubic units；
2-7) according to 2-6) the covariance matrix R of cubic units that is calculated, it is calculated according to conventional matrix algorithm
To three eigenvalue λs1、λ2And λ3, and set:
λ12λ2≤λ3(5)；
Threshold value t=0.02 2-8) is set, if λ2/λ3The cubic units are then labeled as linear cubic units by≤t, otherwise for
Non-linear shape cubic units；
Linear cubic units and its interior point data 2-9) are extracted as power line candidate point data set two.
4. it is according to claim 3 based on the laser radar point cloud power line of normal distribution and cluster classify extracting method,
It is characterized by: the specific steps of the step (3) are as follows:
3-1) feature vector corresponding to maximum eigenvalue is poly- as mean value in each cubic units in preservation data set two
The sample data of class algorithm is denoted as data set three；
3-2) visually chosen in data set three k belong to the feature vectors of power line candidate point cubic units as seed to
Amount；
3-3) ergodic data collection three calculates angle α of each feature vector respectively between k Seeding vectorpq；Wherein p is represented
The corresponding feature vector of the maximum eigenvalue of p-th of cubic units in data set three；Q represents q-th of spy in k Seeding vector
Levy vector；αpqRepresent the angle in data set three between p-th of feature vector and q-th of Seeding vector；
Angle threshold value Δ=0.5 degree 3-4) is set, judges the folder in data set three between each feature vector and k Seeding vector
Angle αpqWith the size relation between angle threshold value Δ, if αpq< Δ, then αpqP-th of cubic units is special in corresponding data set three
Sign vector belongs to q-th in k Seeding vector of Seeding vector group, k Seeding vector group is always obtained, if αpq> Δ, then this
P cubic units feature vector is not belonging to any Seeding vector group；
The normal vector for 3-5) calculating each feature vector group, using this k normal vector as new Seeding vector；
3-6) repeat step 3-3)-step 3-5), until normal vector is consistent with Seeding vector, end loop；
The cubic units and its interior point data being unsatisfactory for where the feature vector of angle threshold value 3-7) are rejected, then data set three
In remaining point data be final power line point, and the point data category in the cubic units where k feature vector group
In the power line of k different directions.
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| CN111814666A (en) * | 2020-07-07 | 2020-10-23 | 华中农业大学 | Single tree parameter extraction method, system, medium and equipment under complex forest stand |
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