CN108061901A - The method that 3D electric power line models are rebuild based on airborne laser radar point cloud data - Google Patents
The method that 3D electric power line models are rebuild based on airborne laser radar point cloud data Download PDFInfo
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- G—PHYSICS
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
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Abstract
The present invention relates to a kind of methods for rebuilding 3D electric power line models, belong to technical field of electric power, are specifically related to a kind of method that 3D electric power line models are rebuild based on airborne laser radar point cloud data.This method is based on airborne laser radar point cloud data, the engineering survey mode of field line patrol need not be utilized, time and the human input of field operation exploration are reduced, especially suitable for a little with a varied topography or even dangerous areas, it is difficult to carry out the airborne laser radar data of line walking working region；And this method improves airborne laser radar power line data positioning accuracy in a manner that segmented model increases (PMG) and data-driven.
Description
Technical field
The present invention relates to a kind of methods for rebuilding 3D electric power line models, belong to technical field of electric power, are specifically related to one kind
The method that 3D electric power line models are rebuild based on airborne laser radar point cloud data.
Background technology
Airborne laser radar (Airborne Light Detection And Ranging, LiDAR) is a kind of active
Air remote sensing earth observation systems are the emerging remote sensing that starts to grow up and put into commercial applications the early 1990s
Technology, its integrated laser technology, global positioning system (GPS) and computer technology are.The technology is in three-dimensional spatial information
Real-time acquisition in terms of generate important breakthrough, the geospatial information to obtain high-spatial and temporal resolution provides a kind of brand-new
Technological means.
At present, since LiDAR can obtain high-precision earth's surface terrain data, compared to traditional imaging measurement mode and
Manually measuring has incomparable advantage, therefore starts to be widely used among various Surveying Engineering, including road mapping, electricity
Power power grid, three-dimension tidal current etc..It, can be extensive using data along the power grid that airborne laser radar measurement technology gathers and handles
The actual geological information of telegram in reply line and electric power optical cable, and then can be between the distance and adjacent wire of automatic measurement electric wire to ground
Away from, and electric wire, the whip degree of electric power communication optical cable, span etc. can be obtained by correlation computations, and can be by above several
What parameter realizes the derivation to the directly security parameter such as electric wire, electric power optical cable tension or pulling force.As it can be seen that by using airborne Li
DAR measurement data can accurately obtain power grid and power optical fiber actual physical state, realize that power line is automatically extracted for electric power
Line walking work has extremely important realistic meaning.
The content of the invention
The present invention mainly solve the prior art present in field operation exploration time and human input it is big, subregion because
The technical issues of being difficult to line walking with a varied topography, provides a kind of based on airborne laser radar point cloud data reconstruction 3D power lines
The method of model.This method is based on airborne laser radar point cloud data, need not utilize the engineering survey mode of field line patrol, subtract
The time of small field operation exploration and human input, especially suitable for a little with a varied topography or even dangerous areas, it is difficult to carry out patrolman
Make the airborne laser radar data in region.
The power line data positioning accuracy that the present invention is mainly solved present in the prior art is low, it is difficult to accurately obtain power grid
And the problem of power optical fiber actual physical state, it provides a kind of based on airborne laser radar point cloud data reconstruction 3D power line moulds
The method of type.This method improves airborne laser radar power line in a manner that segmented model increases (PMG) and data-driven
Data positioning accuracy.
The above-mentioned technical problem of the present invention is mainly what is be addressed by following technical proposals：
A kind of method that 3D electric power line models are rebuild based on airborne laser radar point cloud data, including：
Feature extraction step, using the LiDAR system acquisition numbers being equipped on the winged carrier of boat, from the airborne laser collected
Extraction point cloud feature in radar data；
Point cloud classifications step is reappeared based on described cloud feature and splits described cloud spatial distribution, delimits target zone
And classify to a cloud；
Models fitting step obtains the power line point in data, the level based on the power line point based on classification point cloud
Straight line and vertical catenary curve fitting electric power line model.
Preferably, a kind of above-mentioned method that 3D electric power line models are rebuild based on airborne laser radar point cloud data, it is described
In feature extraction step, based on following sub-step gathered data：
Platform building sub-step flies to carry a set of laser radar system on carrier in boat, including Inertial Measurement Unit
(IMU), differential GPS (DGPS), laser scanning and ranging system and imaging device；
Area's aerial survey sub-step is surveyed, according to the flight scenario made, is flown to surveying area and navigate；
Data generate sub-step, and airborne laser radar point cloud number is obtained according to airborne laser radar data generative theory model
According to.
Preferably, a kind of above-mentioned method that 3D electric power line models are rebuild based on airborne laser radar point cloud data, it is described
In feature extraction step, the point cloud feature of extraction includes：The space coordinates of point cloud, strength information, echo information, feature based are empty
Between feature, the feature based on echo, the feature based on density.
Preferably, a kind of above-mentioned method that 3D electric power line models are rebuild based on airborne laser radar point cloud data, it is described
Point cloud classifications step specifically includes：
Using property parameters as input value, judgement ground is filtered using TIN Encryption Algorithm for target discrimination sub-step
Millet cake and non-ground points；
Space reappears sub-step, and the spatial distribution of airborne LiDAR point cloud is reappeared using data processing software；
Interaction classification sub-step, using the method for man-machine interactively, is manually split cloud data, delimit power line,
Tower bar, the scope of atural object, obtains classification results.
Preferably, a kind of above-mentioned method that 3D electric power line models are rebuild based on airborne laser radar point cloud data, it is described
In models fitting step, the horizontal linear of power line point is obtained based on following sub-step：
ρ=xcos θ+ysin θ
In formula, ρ is the distance between straight line and (0,0), and θ is the vectorial angle with X-axis,
Wherein, ξ1, ξ2, ξ3It is three parameter vectors of ξ, and is constrained by following formula：
min:J (ξ)=ξTATAξ
In formula,
Preferably, a kind of above-mentioned method that 3D electric power line models are rebuild based on airborne laser radar point cloud data, it is described
In models fitting step, fitting a straight line precision is evaluated based on following formula：
In formula, n is sample data quantity.
Preferably, a kind of above-mentioned method that 3D electric power line models are rebuild based on airborne laser radar point cloud data, it is described
In models fitting step, the structure of the vertical catenary curve comprises the following steps：
Coordinate converts sub-step, the floor coordinate system XOY for establishing point cloud and the part in the span randomly selected
Coordinate system XsOsYsConversion：
Wherein (x0,y0) it is coordinate system XsOsYsOrigin under XOY, and the inclination angle that α is span,θ is
Vector and the angle of X-axis；
Curve builds sub-step, and equations of the catenary curve C (a, b, c) in XZ planes, wherein a and b are determined based on following formula
It is the parameter of change of origin, c is a zoom factor, is expressed as the tension and weight ratio of the catenary curve of per unit length：
Preferably, a kind of above-mentioned method that 3D electric power line models are rebuild based on airborne laser radar point cloud data, it is described
In curve structure sub-step：
Given initial value (a0,b0,c0) be, after being linearized to formula (14)：
In formula,
When there are during m >=3, by minimizing residual sum of squares (RSS) progress parametric solution, the correction of parameter is as follows：
δ=- (GTG)GTf (16)
Wherein,Upper right footmark (i) represents corresponding i points
Value, iteration undated parameter value, until parameter corrected value is sufficiently small.
Preferably, a kind of above-mentioned method that 3D electric power line models are rebuild based on airborne laser radar point cloud data, is used
Increase the modeling method of the power circuit of (PMG) based on segmented model, the segmented model increases building for the power circuit of (PMG)
In mould step：
Power line model hypothesis generate and optimal selection sub-step, give power line point D, and Mi is made to represent i-th of segmentation mould
The electric power line model and its model parameter vector wi of type growth.A null hypothesis is done since Mi, then the replacement of a m-sequence
Model is
The position b of catenary curve after assuming can be determined using formula (3) and formula (14).Therefore in a sequence not
The position of same catenary curve is
bj={ b+dbj, j=0,1 ..., m | dbj=Δconst×j} (18)
Due to D for estimating all parametric assumptions, the b of the estimation procedure on variationjIt cannot be according to simple
Generate different hypothesis least square adjustments.We employ the least-squares estimation with random constraints be used for consider parameter with
Weight in linear function is as additional observation equation.In this case, adjusting parameter each assumes that entirety should be met
Geometry deformation.We are by adding in a parameterEach hypothesis is made to meet entire change.We are by increasing by two
Curve modification catenary curve equation.By parameter (a1,c1) be added in formula (14), formula is revised as
Finally, optimal electric power line model is selectedUsing hypothesis testing process, the residual error between D and Mi is measured,
And the hypothesis for selecting to meet best fit degree criterion is as the optimal models in current propagation step；
It is segmented electric power line model and increases sub-step, PMG models are then by generating multiple hypothesized models and selecting optimal PL
Model realizes that this process is until Mi is selected as optimal models to provide best fit result.When Mi is chosen as most
Excellent model, then based on the assumption that examine propagation terminate, then electric power line model by be based on Mi Direct Acquisitions power line point without
Increase by hypothesis verification.
Therefore, the invention has the advantages that：
1. the present invention is based on airborne laser radar point cloud data, the engineering survey mode of field line patrol need not be utilized, because
This reduces the time of field operation exploration and human input, especially suitable for a little with a varied topography or even dangerous areas, it is difficult to be patrolled
The airborne laser radar data of line working region；
2. the present invention carries out precision evaluation after carrying out power line classification and rebuilding, using the reference data of manual sort,
Improve airborne laser radar power line data positioning accuracy.Minimal error, the worst error in X-direction are obtained using the present invention
It is respectively 2.34cm, 8.45cm, 4.48cm with root-mean-square error, and in Y-direction is respectively 2.08cm, 9.34cm 4.63cm, Z
It is respectively 4.63cm, 16.73cm, 8.34cm on direction.Its fitting precision is slightly different in different directions, on X, Y-direction
Its precision is similar, and fitting precision is slightly poor in Z-direction.
Description of the drawings
Fig. 1 a：The schematic diagram (XOY) of cloud data two-dimensional transform；
Fig. 1 b：Schematic diagram (the X of cloud data two-dimensional transformsOsYs)；
Fig. 2：Catenary curve instance graph in the vertical projection of power line；
Fig. 3：The flow chart of 3D electric power line models is rebuild based on airborne laser radar point cloud data；
Fig. 4：The present invention is applied to airborne laser radar data power line fitting precision schematic diagram；
Fig. 5：The present invention is applied to airborne laser radar data and rebuilds 3D power line model schematics.
Specific embodiment
Below with reference to the embodiments and with reference to the accompanying drawing the technical solutions of the present invention will be further described.
Embodiment：
As shown in figure 3, the method for 3D electric power line models is rebuild based on airborne laser radar point cloud data, including following step
Suddenly：
Step 1, fly to carry a set of airborne laser LiDAR systems on carrier in boat, including Inertial Measurement Unit (IMU), poor
Divide GPS (DGPS), laser scanning and ranging system and imaging device；
Step 2, data acquisition is carried out using the airborne laser radar in step 1, obtains airborne laser radar data；
Step 3, according to the data obtained in step 2, the feature (single-point feature and neighborhood characteristics) of LiDAR data is extracted,
Include space coordinates, strength information, echo information (echo position and echo strength) and feature based value and the spy of point cloud respectively
Levy feature, the feature based on echo, feature based on density of vector etc.；
Step 4, to obtaining the corresponding attributive character of data in step 3, classify to data；
Step 5, using the grouped data obtained in step 4, the power line point data in data is obtained；
Step 6, the power line point data obtained to step 5 obtains hanging down on the horizontal level and XZ planes on X/Y plane
Straight catenary curve position.
Step 7, the horizontal level using step 6 acquisition and vertical catenary curve position use segmented model growth method electricity
Line of force road models.
In step 4 as described above, the step of classifying to data, is：
Step 4.1, using the property parameters obtained in step 3 as input value, to this two sets of airborne laser radar datas into
Row step 4.2 --- 4.4 processing；
Step 4.2, it is filtered using TIN Encryption Algorithm and judges ground point and non-ground points；
Step 4.3, the spatial distribution of airborne LiDAR point cloud is reappeared using data processing software；
Step 4.4, using the method for man-machine interactively, manually cloud data is split, delimit power line, tower bar, ground
The scope of object, obtains classification results；
In step 6 as described above, the method that horizontal and vertical position obtains is：
Step 6.1, power line point data will be obtained in step 5 as input, and carries out step 5.2 --- 5.3 processing；
Step 6.2, straight line is generally defined as on X/Y plane：
Y=mx+b (1)
Wherein parameter m is slope, and b is intercept.Therefore, straight line can also be defined as the points of m-b spatially on X/Y plane
(m,b)：
B=-xm+y (2)
Since two-dimentional straight line tool is there are two degree of freedom, can use two parameter models or one have more than two parameters with
The model of additional constraint represents, therefore straight line can be expressed as using polar form：
ρ=xcos θ+ysin θ (3)
Wherein, ρ is the distance between straight line and (0,0), and θ is vector and the angle of X-axis.
If (xi,yi) it is the point randomly selected, then the distance put to line is represented by：
Assuming that there are a points of N (N >=2), to make the quadratic sum of all the points to the distance of line minimum, that is, cause function (XX) most
It is small：
When providing the approximation of parameter, solved by lienarized equation (XX) and using traditional least square method.
And it is herein, it is directly solved, and need not be linearized using total least square.Ignore the phase between parameter
Guan Xing, the general type of equation (5) are：
xiξ1+yiξ2+ξ3=0 (6)
Wherein ξ is the parameter vector of a 3*1.Therefore, equation (6) is represented by：
min:J (ξ)=ξTATAξ (7)
Wherein
Equation (8) is a uniform optimization problem (sensation should be a Global Optimal Problem), it is necessary to which use restraint ability
It solves.Assuming that a unit vector is ξTξ=1, by introducing lagrangian multiplier, Euler-Lagrange condition generates
2ATA ξ -2 λ ξ=0 (8)
It is reduced to：
S ξ=λ ξ (9)
Wherein S=ATA is known as collision matrix, and equation (9) shows that ξ is the feature vector of matrix S.
In order to seek the minimum value of equation (8), ξ corresponds to the feature vector of minimal eigenvalue.Once ξ is determined, by asking
(1) and (7) are solved equation, ρ and θ can be also determined：
After ρ and θ are solved, it is possible to fit the line on X/Y plane.The root-mean-square error of residual error is used herein
(RMSE) precision of fitting a straight line is evaluated.
Step 6.3, the vertical catenary curve fitting in XZ planes
It is fitted after the power line on X/Y plane, is exactly catenary curve fitting on vertical plane.Therefore, two-dimensional coordinate system needs
It to be converted on X/Y plane, make transformed X-direction consistent with fitting power line section direction, power line fitting at this time is bent
Line is projected in XZ planes as curve in new coordinate system in itself, can be directly in the XZ planes of new coordinate system to power line
It is fitted.Fig. 1 a- Fig. 1 b illustrate the principle of two-dimensional transform, and wherein XOY is the floor coordinate system of a cloud, is random
The X-axis Local coordinate system consistent with fitting power line direction in the span of selection.Wherein, origin is (logical for the starting point of span
It is often the tie point of shaft tower), X-axis is consistent with fitting power line.The 2D coordinate system transformation systems are：
Wherein (x0,y0)) it is coordinate system XSOSYSOrigin under XOY, and the inclination angle that α is span.It can be seen that in Fig. 1
Equations of the catenary curve C (a, b, c) in XZ planes is represented that wherein a and b are the ginsengs of change of origin by formula (14)
Number, c is a zoom factor, is expressed as the tension and weight ratio of the catenary curve of per unit length.
The purpose that catenary curve is rebuild finds suitable parameter a, b, c by giving a series of point.When parameter gives
Initial value (a0,b0,c0) it is to be obtained after formula (14) linearisation：
Wherein：
When there are a points of m (m >=3), m is the points of power line point cloud, can be joined by minimizing residual sum of squares (RSS)
Number solves, and the correction of parameter is as follows：
δ=- (GTG)GTf (16)
Wherein,Upper right footmark (i) represents corresponding i points
Value.Iteration undated parameter value, until parameter corrected value is sufficiently small.In order to ensure convergence, the choosing of initial parameter value
It takes with regard to needing to be correct.It can be by choosing the starting point of each span, terminal and midpoint are near to obtain appropriate parameter with this
Like value, fitting precision is still evaluated using RMSE.
In step 7 as described above, the method for segmented model growth method power circuit modeling is：
Step 7.1, the generation of power line model hypothesis and optimal selection
Given power line point D makes Mi represent the electric power line model and its model parameter vector of i-th of segmented model growth
wi.A null hypothesis is done since Mi, then the alternative model of a m-sequence is
The position b of catenary curve after assuming can be determined using formula (3) and formula (14).Therefore in a sequence not
The position of same catenary curve is
bj={ b+dbj, j=0,1 ..., m | dbj=Δconst×j} (18)
Due to D for estimating all parametric assumptions, the b of the estimation procedure on variationjIt cannot be according to simple
Generate different hypothesis least square adjustments.We employ the least-squares estimation with random constraints be used for consider parameter with
Weight in linear function is as additional observation equation.In this case, adjusting parameter each assumes that entirety should be met
Geometry deformation.We are by adding in a parameterEach hypothesis is made to meet entire change.We are by increasing by two
Curve modification catenary curve equation.By parameter (a1,c1) be added in formula (14), formula is revised as
Finally, optimal electric power line model is selectedUsing hypothesis testing process, the residual error between D and Mi is measured,
And the hypothesis for selecting to meet best fit degree criterion is as the optimal models in current propagation step.
Step 7.2, electric power line model is segmented to increase
PMG models then provide best fit result come real by generating multiple hypothesized models and selecting optimal PL models
Existing, this process is until Mi is selected as optimal models.When Mi is chosen as optimal models, then based on the assumption that the propagation examined
It terminates, then electric power line model is increased by being based on Mi Direct Acquisitions power line point without hypothesis verification.
Specific embodiment described herein is only to spirit explanation for example of the invention.Technology belonging to the present invention is led
The technical staff in domain can do various modifications or additions to described specific embodiment or replace in a similar way
Generation, but without departing from spirit of the invention or beyond the scope of the appended claims.
Claims (9)
- A kind of 1. method that 3D electric power line models are rebuild based on airborne laser radar point cloud data, which is characterized in that including：Feature extraction step, using the LiDAR system acquisition numbers being equipped on the winged carrier of boat, from the airborne laser radar collected Extracting data point cloud feature；Point cloud classifications step is reappeared based on described cloud feature and splits described cloud spatial distribution, and delimitation target zone is simultaneously right Point cloud is classified；Models fitting step obtains the power line point in data, the horizontal linear based on the power line point based on classification point cloud Electric power line model is fitted with vertical catenary curve.
- 2. a kind of method that 3D electric power line models are rebuild based on airborne laser radar point cloud data according to claim 1, It is characterized in that, in the feature extraction step, based on following sub-step gathered data：Platform building sub-step flies to carry a set of laser radar system on carrier in boat, including Inertial Measurement Unit (IMU), poor Divide GPS (DGPS), laser scanning and ranging system and imaging device；Area's aerial survey sub-step is surveyed, according to the flight scenario made, is flown to surveying area and navigate；Data generate sub-step, and airborne laser radar point cloud data is obtained according to airborne laser radar data generative theory model.
- 3. a kind of method that 3D electric power line models are rebuild based on airborne laser radar point cloud data according to claim 1, It is characterized in that, in the feature extraction step, the point cloud feature of extraction includes：The space coordinates of point cloud, strength information, echo Information, the feature in feature based space, the feature based on echo, the feature based on density.
- 4. a kind of method that 3D electric power line models are rebuild based on airborne laser radar point cloud data according to claim 1, It is characterized in that, the point cloud classifications step specifically includes：Using property parameters as input value, judgement ground point is filtered using TIN Encryption Algorithm for target discrimination sub-step With non-ground points；Space reappears sub-step, and the spatial distribution of airborne LiDAR point cloud is reappeared using data processing software；Interaction classification sub-step, using the method for man-machine interactively, is manually split cloud data, delimitation power line, tower bar, The scope of atural object, obtains classification results.
- 5. a kind of method that 3D electric power line models are rebuild based on airborne laser radar point cloud data according to claim 1, It is characterized in that, in the models fitting step, the horizontal linear of power line point is obtained based on following sub-step：ρ=x cos θ+y sin θsIn formula, ρ is the distance between straight line and (0,0), and θ is the vectorial angle with X-axis,<mrow> <mi>&theta;</mi> <mo>=</mo> <mi>arccos</mi> <mrow> <mo>(</mo> <mfrac> <msub> <mi>&xi;</mi> <mn>1</mn> </msub> <msqrt> <mrow> <msubsup> <mi>&xi;</mi> <mn>1</mn> <mn>2</mn> </msubsup> <mo>+</mo> <msubsup> <mi>&xi;</mi> <mn>2</mn> <mn>2</mn> </msubsup> </mrow> </msqrt> </mfrac> <mo>)</mo> </mrow> </mrow><mrow> <mi>&rho;</mi> <mo>=</mo> <mo>-</mo> <mfrac> <msub> <mi>&xi;</mi> <mn>3</mn> </msub> <msqrt> <mrow> <msubsup> <mi>&xi;</mi> <mn>1</mn> <mn>2</mn> </msubsup> <mo>+</mo> <msubsup> <mi>&xi;</mi> <mn>2</mn> <mn>2</mn> </msubsup> </mrow> </msqrt> </mfrac> </mrow>Wherein, ξ1, ξ2, ξ3It is three parameter vectors of ξ, and is constrained by following formula：min:J (ξ)=ξTATAξIn formula,
- 6. a kind of method that 3D electric power line models are rebuild based on airborne laser radar point cloud data according to claim 5, It is characterized in that, in the models fitting step, fitting a straight line precision is evaluated based on following formula：<mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> <mo>=</mo> <msqrt> <mfrac> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mi>c</mi> <mi>o</mi> <mi>s</mi> <mi>&theta;</mi> <mo>+</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> <mi>s</mi> <mi>i</mi> <mi>n</mi> <mi>&theta;</mi> <mo>-</mo> <mi>&rho;</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> <mi>n</mi> </mfrac> </msqrt> </mrow>In formula, n is sample data quantity.
- 7. a kind of method that 3D electric power line models are rebuild based on airborne laser radar point cloud data according to claim 1, It is characterized in that, in the models fitting step, the structure of the vertical catenary curve comprises the following steps：Coordinate converts sub-step, the floor coordinate system XOY for establishing point cloud and the local coordinate in the span randomly selected System XsOsYsConversion：<mrow> <mfenced open = "(" close = ")"> <mtable> <mtr> <mtd> <msub> <mi>x</mi> <mi>s</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>y</mi> <mi>s</mi> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mfenced open = "(" close = ")"> <mtable> <mtr> <mtd> <mrow> <mi>c</mi> <mi>o</mi> <mi>s</mi> <mi>&alpha;</mi> </mrow> </mtd> <mtd> <mrow> <mo>-</mo> <mi>s</mi> <mi>i</mi> <mi>n</mi> <mi>&alpha;</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>s</mi> <mi>i</mi> <mi>n</mi> <mi>&alpha;</mi> </mrow> </mtd> <mtd> <mrow> <mi>cos</mi> <mi>&alpha;</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> <mfenced open = "(" close = ")"> <mtable> <mtr> <mtd> <mi>x</mi> <mo>-</mo> <msub> <mi>x</mi> <mn>0</mn> </msub> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>y</mi> <mo>-</mo> <msub> <mi>x</mi> <mn>0</mn> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>12</mn> <mo>)</mo> </mrow> </mrow>Wherein (x0,y0) it is coordinate system XsOsYsOrigin under XOY, and the inclination angle that α is span,θ is vector With the angle of X-axis；Curve builds sub-step, determines equations of the catenary curve C (a, b, c) in XZ planes based on following formula, wherein a and b are former The parameter of point transformation, c are a zoom factors, are expressed as the tension and weight ratio of the catenary curve of per unit length：<mrow> <mi>z</mi> <mo>=</mo> <mi>a</mi> <mo>+</mo> <mi>c</mi> <mi> </mi> <mi>cosh</mi> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <mi>b</mi> </mrow> <mi>c</mi> </mfrac> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>14</mn> <mo>)</mo> </mrow> </mrow>
- 8. a kind of method that 3D electric power line models are rebuild based on airborne laser radar point cloud data according to claim 7, It is characterized in that, in the curve structure sub-step：Given initial value (a0,b0,c0) be, after being linearized to formula (14)：gaδa+gbδb+gcδ c+f=0 (15)In formula,<mrow> <msub> <mi>g</mi> <mi>a</mi> </msub> <mo>=</mo> <mfrac> <mrow> <mo>&part;</mo> <mi>C</mi> </mrow> <mrow> <mo>&part;</mo> <mi>a</mi> </mrow> </mfrac> <msub> <mo>|</mo> <mn>0</mn> </msub> <mo>=</mo> <mn>1</mn> <mo>;</mo> <msub> <mi>g</mi> <mi>b</mi> </msub> <mo>=</mo> <mfrac> <mrow> <mo>&part;</mo> <mi>C</mi> </mrow> <mrow> <mo>&part;</mo> <mi>b</mi> </mrow> </mfrac> <msub> <mo>|</mo> <mn>0</mn> </msub> <mo>=</mo> <mo>-</mo> <mi>sinh</mi> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msub> <mi>b</mi> <mn>0</mn> </msub> </mrow> <msub> <mi>c</mi> <mn>0</mn> </msub> </mfrac> <mo>)</mo> </mrow> <mo>;</mo> </mrow><mrow> <msub> <mi>g</mi> <mi>c</mi> </msub> <mo>=</mo> <mfrac> <mrow> <mo>&part;</mo> <mi>C</mi> </mrow> <mrow> <mo>&part;</mo> <mi>c</mi> </mrow> </mfrac> <msub> <mo>|</mo> <mn>0</mn> </msub> <mo>=</mo> <mi>cosh</mi> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msub> <mi>b</mi> <mn>0</mn> </msub> </mrow> <msub> <mi>c</mi> <mn>0</mn> </msub> </mfrac> <mo>)</mo> </mrow> <mo>-</mo> <mfrac> <mn>1</mn> <msub> <mi>c</mi> <mn>0</mn> </msub> </mfrac> <mi>sinh</mi> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msub> <mi>b</mi> <mn>0</mn> </msub> </mrow> <msub> <mi>c</mi> <mn>0</mn> </msub> </mfrac> <mo>)</mo> </mrow> <mo>;</mo> </mrow><mrow> <mi>f</mi> <mo>=</mo> <msub> <mi>a</mi> <mn>0</mn> </msub> <mo>+</mo> <msub> <mi>c</mi> <mn>0</mn> </msub> <mi>cosh</mi> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msub> <mi>b</mi> <mn>0</mn> </msub> </mrow> <msub> <mi>c</mi> <mn>0</mn> </msub> </mfrac> <mo>)</mo> </mrow> <mo>-</mo> <mi>z</mi> </mrow>When there are during m >=3, by minimizing residual sum of squares (RSS) progress parametric solution, the correction of parameter is as follows：δ=- (GTG)GTf (16)Wherein,Upper right footmark (i) represents the value of corresponding i points, Iteration undated parameter value, until parameter corrected value is sufficiently small.
- 9. a kind of method that 3D electric power line models are rebuild based on airborne laser radar point cloud data according to claim 8, Using the modeling method for the power circuit for increasing (PMG) based on segmented model, which is characterized in that the segmented model increases (PMG) in the modeling procedure of power circuit：Power line model hypothesis generate and optimal selection sub-step, give power line point D, and Mi is made to represent i-th of segmented model life Long electric power line model and its model parameter vector wi.A null hypothesis is done since Mi, then the alternative model of a m-sequence For<mrow> <mi>M</mi> <mi>i</mi> <mo>=</mo> <mo>{</mo> <mi>M</mi> <mi>i</mi> <mrow> <mo>(</mo> <msubsup> <mi>w</mi> <mi>i</mi> <mn>0</mn> </msubsup> <mo>)</mo> </mrow> <mo>,</mo> <mi>M</mi> <mi>i</mi> <mrow> <mo>(</mo> <msubsup> <mi>w</mi> <mi>i</mi> <mn>1</mn> </msubsup> <mo>)</mo> </mrow> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>M</mi> <mi>i</mi> <mrow> <mo>(</mo> <msubsup> <mi>w</mi> <mi>i</mi> <mi>m</mi> </msubsup> <mo>)</mo> </mrow> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>17</mn> <mo>)</mo> </mrow> </mrow>The position b of catenary curve after assuming can be determined using formula (3) and formula (14).Therefore it is different in a sequence The position of catenary curve isbj={ b+dbj, j=0,1 ..., m | dbj=Δconst×j} (18)Due to D for estimating all parametric assumptions, the b of the estimation procedure on variationjIt cannot be generated not according to simple Same hypothesis least square adjustment.We employ the least-squares estimation with random constraints and are used for considering parameter with linear letter Weight in number is as additional observation equation.In this case, adjusting parameter each assumes that whole geometry change should be met Shape.We are by adding in a parameterEach hypothesis is made to meet entire change.We are repaiied by increasing by two curves Change catenary curve equation.By parameter (a1,c1) be added in formula (14), formula is revised as<mrow> <msup> <mi>C</mi> <mo>&prime;</mo> </msup> <mrow> <mo>(</mo> <mi>a</mi> <mo>,</mo> <mi>b</mi> <mo>,</mo> <mi>c</mi> <mo>,</mo> <msub> <mi>a</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>c</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mo>:</mo> <mi>Z</mi> <mo>=</mo> <mi>a</mi> <mo>+</mo> <msub> <mi>a</mi> <mn>1</mn> </msub> <mo>+</mo> <mrow> <mo>(</mo> <mi>c</mi> <mo>+</mo> <msub> <mi>c</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mi>cosh</mi> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>X</mi> <mo>-</mo> <mi>b</mi> </mrow> <mrow> <mi>c</mi> <mo>+</mo> <msub> <mi>c</mi> <mn>1</mn> </msub> </mrow> </mfrac> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>19</mn> <mo>)</mo> </mrow> </mrow>Finally, optimal electric power line model is selectedUsing hypothesis testing process, the residual error between D and Mi is measured, and is selected The hypothesis for meeting best fit degree criterion is selected as the optimal models in current propagation step；It is segmented electric power line model and increases sub-step, PMG models are then by generating multiple hypothesized models and selecting optimal PL models It is realized to provide best fit result, this process is until Mi is selected as optimal models.When Mi is chosen as optimal mould Type, then based on the assumption that examine propagation terminate, then electric power line model by be based on Mi Direct Acquisitions power line point and without Hypothesis verification and increase.
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