CN107610223A - Power tower three-dimensional rebuilding method based on LiDAR point cloud - Google Patents
Power tower three-dimensional rebuilding method based on LiDAR point cloud Download PDFInfo
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Abstract
The invention belongs to laser radar point cloud data information extraction technology field, is related to a kind of power tower three-dimensional rebuilding method based on LiDAR point cloud.Comprise the following steps：S1. the decomposition of power tower, by carrying out statistical analysis to density and width, power tower is decomposed into tower body and tower head two parts；S2. tower body is rebuild based on data-driven, angle steel joint is extracted and split, and the progress 3 d-line fitting of RANSAC algorithms is based on to four main wheel profiles of tower body；S3. tower head is rebuild based on model-driven, predefine a model library for including tower head fundamental type, then tower head fundamental type is identified using Shape context algorithm, then Metropolis Hastings algorithms is combined to the optimized parameter of estimation tower head model with simulated annealing；S4. the position and direction in S2, S3 step acquired results are combined, and obtain complete three-dimensional power tower model.The present invention can effectively, accurately rebuild power tower, meet power transmission line three-dimensional visual, digitized demand.
Description
Technical field
The invention belongs to laser radar point cloud data information extraction technology field, more specifically, is related to one kind and is based on
The power tower three-dimensional rebuilding method of LiDAR point cloud.
Background technology
The power tower infrastructure important as ultra-high-tension power transmission line, it inhales in terms of being reconstituted in power transmission line three-dimensional visual
Increasing concern is drawn.In the past few years, power tower models the most frequently used method and mainly carried out with CAD or 3dmax
Manually model, this method expends substantial amounts of human and material resources and reconstruction precision is relatively low.One kind is badly in need of certainly in administration of power networks department
Dynamicization, high efficiency, high-precision power tower method for reconstructing meet the visualization of modern transmission line of electricity, digitized demand.With
The rapid development of Airborne LiDAR Technology, accurately high density, 3D point cloud provide an effective solution to the demand.
At present, the scholar for having many has done substantial amounts of research in terms of the reconstruction based on LiDAR point cloud objective, and
Natural feature on a map and man-made features are rebuild and achieve larger progress.However, complexity and species due to electric power tower structure
Diversity, to power tower rebuild research it is also very rare.Li Qingquan etc. proposes a kind of power tower based on model-driven
Power tower is decomposed into tower head, tower body and column foot three parts by method for reconstructing, this method first, then to tower body part with four masters
Plane is rebuild, and tower head part is classified using SVM classifier.This method is carried out according only to density feature to power tower
Decompose, lack applicability, other common type power towers (such as cat-head transmission tower, wineglass tower, hexagonal tower) often go out when being decomposed
Existing error result；Further, since this method, which introduces, manually establishes tower head model so that automaticity reduces, and lacks
Practicality.Guo Bo etc. proposes a kind of power tower method for reconstructing based on random geometry, and this method is by RJMCMC samplers and mould
Intend annealing to be combined, automatically solve the problems, such as power tower type and model parameter, but this method is inefficient, it is necessary to expend a large amount of
Iterations judge power tower type, and in power tower model parameter estimation process, the geometry not accounted between parameter closes
System, causes substantial amounts of nuisance parameter needs to be estimated.
At present, the method that building is rebuild is broadly divided into following two strategies：Based on data-driven strategy and based on mould
The strategy of type driving.Strategy based on data-driven is using method for reconstructing from top to bottom.Most of building is rebuild treated
Journey generally comprises two committed steps：The extraction at building roof edge and the reconstruction of topological relation.Miscellaneous roof structure or height
The cloud data of density, method extraction edge of the generally use based on patch division, is such as increased based on edge or region, is three-dimensional
RANSAC algorithms, classification or feature clustering etc..Reconstructed results based on data-driven are not influenceed by model library integrality, theoretical
On can rebuild any shape.When data than it is more complete when, the method for data-driven is provided simple target and accurately described；
However, when shortage of data or during comprising more noise, this method can produce reconstruction error or even failure.
For strategy based on model-driven using method for reconstructing from bottom to up, this method is based on pre-defined model library
's.This method mainly includes two committed steps：Optimum Matching and corresponding model parameter are carried out with the model in model library most
Optimization Solution.In many based in the method for reconstructing of model-driven, most basic hypothesis is the set that building is many metopes.
Many methods such as RJMCMC is introduced for certainly model parameter problem, and shows larger development potentiality.Driven based on model
Dynamic method has robustness to the quality of data and is applied to large scene, because topological relation defines in a model in advance, because
This, this method can play its superiority when a cloud density is smaller and ensure correct topological relation.However, the reconstruction of this method
As a result influenceed by model library, when when modulus shape parameter is more, this method is less efficient.
Due to the complexity of object construction and the diversity of shape, some way of only voucher one is difficult to meet Modern Reconstruction
Requirement.Therefore, it is suggested with reference to the combination drive strategy of data-driven and model-driven in recent years.This method is in target
Phase of regeneration will build regular (such as collimation, coplanarity, symmetry) introducing and carry out Optimized model.It is tactful different from single,
The strategy of combination drive blends the advantage of two methods, has both remained the flexibility of data-driven, it may have model-driven
Robustness.
The content of the invention
The defects of it is an object of the invention to overcome prior art, there is provided a kind of power tower based on LiDAR point cloud is three-dimensional
Method for reconstructing, can effectively, accurately rebuild power tower, meet power transmission line three-dimensional visual, digitized demand.
To solve the above problems, technical scheme provided by the invention is：A kind of power tower Three-dimensional Gravity based on LiDAR point cloud
Construction method, wherein, comprise the following steps：
S1. the decomposition of power tower, the LiDAR point cloud data of power tower are obtained, passes through the density and width to power tower point cloud
Degree carries out statistical analysis, and power tower is decomposed into tower body and tower head two parts；
S2. tower body is rebuild based on data-driven, angle steel joint is extracted and split, four main wheel profiles of tower body are based on
RANSAC algorithms carry out 3 d-line fitting；
S3. tower head is rebuild based on model-driven, a model library for including tower head fundamental type is predefined, then using shape
Shape contextual algorithms identify tower head fundamental type, then Metropolis-Hastings algorithms are combined into estimation with simulated annealing
The optimized parameter of tower head model；
S4. the position and direction in S2, S3 step acquired results are combined, and obtain complete three-dimensional power tower mould
Type.
In the present invention, it is contemplated that the architectural feature of power tower, will first by carrying out statistical analysis to density and width
Power tower is decomposed into tower body and tower head two parts；Then data-driven is based on to tower body and carries out three-dimensional reconstruction, to four of tower body
Main wheel profile is based on RANSAC algorithms and carries out 3 d-line fitting；Three-dimensional reconstruction is carried out being based on model-driven to tower head, first
The tower head model library of a predefined parametrization is needed, model library identification tower head is then based on using shape context algorithms
The tower head type of point cloud, corresponding column is estimated with the method that simulated annealing is combined using Metropolis-Hastings samplers
The parameter of head model, finally it is combined according to two-part position and direction, obtains complete three-dimensional power tower model.
Power tower is that its tower body structure is simple, type is single with certain artificial atural object for building rule, tower head species compared with
It is more and complicated.In view of the architectural feature of power tower, power tower can be decomposed into two parts：Tower body and tower head；It is although electric
The type of power tower has a variety of, but characteristic face all includes two obvious features：(1) local density is maximum, and (2) part width is most
It is small.Wherein, density is defined as the point cloud number of tower cross section, and width is defined as each section to the ultimate range of kernel of section；
Therefore, the present invention to power tower using the method for statistical analysis come location feature face.It is straight in order to obtain the statistics of density and width
Fang Tu, if power tower is divided into dried layer, by counting each layer of density and width, choose simultaneously have density local maxima and
Position of the minimum layer of width as characteristic face.
Further, density and width the progress statistical analysis in described S1 steps to power tower point cloud include following step
Suddenly：
If power tower point cloud is S101. divided into dried layer according to Δ h at equal intervals；
S102. calculate each layer of density and width, and established respectively as X-axis, using the number of plies as Y-axis using density and width close
Spend histogram and width histogram；
S103. one is defined centered on current layer number, and size is the moving window of 2L*1 sizes respectively in density and width
Spend and very dense value and width minimum are found in histogram, and layer where maximum and minimum is marked；
S104. power tower select first from bottom to up and meanwhile meet this layer of density local maxima and it is upper and lower two layers in the range of
Position of the number of plies as characteristic face of width Local Minimum be present, decomposed by boundary line of characteristic face, portion more than characteristic face
It is decomposed into tower head, the decomposed below characteristic face is tower body.
Distinguished point based extracts and the method for segmentation is a kind of method commonly used in data-driven strategy.In order to improve segmentation
Accuracy, the present invention only requires that angle steel joint is extracted；In order to correctly extract the angle point of tower body, tower body is first according to elevation
If it is divided into dried layer at equal intervals.Each layer can be regarded as with concentric rectangle.To each layer, extracted using algorithm of convex hull
Its profile point, not all profile point is all located at four angles of rectangle, therefore, it is necessary to is based on pipe to the profile point after extraction
Road algorithm is simplified.
Further, in described S2 steps angle steel joint carry out extraction comprise the following steps with segmentation：
S2011. current point and adjacent 2 points of angle are calculated, if angle is more than predetermined threshold df；Then retain current point, it is no
Then, reject；
S2012. simplified by pipeline algorithm, delete most of non-angle point；
S2013. the angle point simplified to each layer, split according to it in the position at minimum enclosed rectangle center, will
Angle point is divided into the subset of four direction.
After above-mentioned steps are handled, the subset of four tower body angle points is obtained.Due to not all letter in each subset
The angle point of change is all at four angles, and therefore, the present invention is using the 3 d-line fitting algorithm based on RANSAC to each angle point subset
It is fitted.
Further, the contour line fitting based on RANSAC comprises the following steps in described S2 steps：
S2021. two point P are randomly selected from the subset obtained by S1 steps1(x1,y1,z1) and P2(x2,y2,z2), and calculate
3 d-line equation L where it；
S2022. for the every bit in subset, calculate it and arrive straight line L distance, if the distance of current point to straight line is small
In distance threshold Td, then it is available point, otherwise, is defined as Null Spot；And the ratio inner_ratio of available point is counted,
Wherein, inner_ratio is that available point accounts for the ratio always counted；
S2023. stochastical sampling is repeated, until meeting that P is less than default fiducial probability TP or sampling number M and is more than default maximum
Sampling number TN, choose parameter of the one group of minimum parameter of regression criterion as the subset 3 d-line equation；Wherein, P is to adopt
The probability of appearance collection；
S2024. after four subsets are fitted, symmetry is added, coplanarity restrictive condition optimizes to model.
Preferably, described predetermined threshold df is 30 °.The angle point at four angles is only needed in the present invention, therefore df=is set
30°；When df set it is bigger when, profile point is fewer, but Tf should be less than 90 °.
Further, described 3 d-line equation L calculation is：
Further, the probability P of described sampling subset meets following relationship：
P=1- (1-inner_ratiom)M,
Wherein, m is to build the minimum parameters number required for a judgment criteria.
Further, identify that tower head fundamental type comprises the following steps in described S3 steps：
S3011. it is two-value image by all tower head model conversations in tower head point cloud and model library to be identified；
S3012. the outline of all images is extracted, contour edge is sampled to obtain n sampled point point set P={ p1, p2,
p3... ..pn}；
S3013. the Shape context h of each sampled point is calculated according to Shape context algorithmi(k), calculation formula such as formula
(1), k={ 1,2 ..K }, K=M*N；
hi(k)={ q ≠ pi:(q-pi∈ bin (k) }, (1)
S3014. calculate between the shape histogram of image to be matched and the shape histogram each pair point of each phantom images
Matching power flow, calculation formula such as formula (2), wherein, hi(k) the point p for being target PiShape histogram；hj(k) for target Q's
Point qiShape histogram,
S3015. Point matching operation is carried out based on the cost matrix C being calculated, chooses the mould with minimum shape distance
Type image is best matching result, and the model is that tower head point cloud corresponds to model.
Further, parameter optimization comprises the following steps in described S3 steps：
S3021.Gibbs energy definitions, it is (3) that Gibbs energy, which is expressed as formula,：
Using alpha shape algorithms come the key point of extraction model and point cloud, distance um(xi) it is each model key point
To the distance of nearest tower head point cloud key point, distance up(xi) for each tower head point cloud key point to nearest model key point away from
From, wherein,
Wherein,Average distance us of the degree of similarity u (x) by model to point cloudm(xi) and put cloud to model
Average distance up(xi) composition, and u (x)=aum(x)+bup(x), a+b=1, a and b are respectively the weights of two distances；N is mould
Type key point number, m are tower head point cloud key point number；
S3022.Metropolis-Hastings is sampled and simulated annealing, first, according to suggestion distribution q (x*| x) searching
A current parameter value x candidate parameter value x is provided in rope space X*, then, candidate parameter value is calculated according to calculation formula (6)
Receptance A (x, x*)；If receptance A (x, x*) be more than default acceptance threshold Ta, then by candidate value x*It is no instead of currency x
Then, currency x is retained；
Further, described acceptance threshold Ta=0.9+0.1*random (0,1), random (0,1) are 0~1
In the range of carry out random sampling result.
Metropolis-Hastings (MH) is sampled as the most popular MCMC method of samplings, and it is widely used
In the approximation of appraising model parameter.MH sampling core concept be by statistical sampling, it is near with a simple question
Like the combinatorial problem of complexity.It is related to two distributions in MH samplings：Target distribution p (x) and suggestion distribution q (x*|x).First, root
It is suggested that distribution q (x*| a current parameter value x candidate parameter value x x) is provided in the X of search space*.Then, according to formula
(10) receptance A (x, the x of candidate parameter value are calculated*)；If receptance A (x, x*) be more than default acceptance threshold Ta, then by candidate
Value x*Instead of currency x, otherwise, retain currency x.Pass through the repeated sampling of statistics, candidate value x*Finally tend towards stability.For
Sampling is prevented to be absorbed in local optimum, acceptance threshold Ta=0.9+0.1*random (0,1), random (0,1) are the model 0~1
Enclose interior progress random sampling result.Although MH sampling algorithm principle very simples, realizing for it are relatively difficult.This be mainly because
For the selection of object function and suggestion function.Different selection results can cause sampled result different.The present invention is selected Gauss
Distribution is as distribution is suggested, using Gibbs energy as target distribution.Due to suggesting the symmetry of distribution, q (x*| x)=q (x |
x*), therefore, receptance can be reduced to formula：Because stochastical sampling is seldom in the attached of actual value
Closely, therefore, MH sampling efficiencies are than relatively low.It expends the more non-interest region of time search.In order that sampling is more efficiently, mould
Intend annealing to be used in the present invention.Simulated annealing formulates an Annealing Strategy first since a higher initial temperature,
With the decline of temperature, target distribution progressively tends towards stability, and finally reaches global optimum with higher receptance.In simulated annealing
In, the selection of Annealing Strategy is an important influence factor, and it is by simulating a nonhomogeneous Markov chain come appraising model
Optimized parameter.Unlike MH samplings, target distributionIt instead of p (x).Wherein, temperature when Ti is iterations i
Degree, and meet limi→∞Ti=0.
Compared with prior art, beneficial effect is：A kind of power tower Three-dimensional Gravity based on LiDAR point cloud provided by the invention
Construction method, the construction feature of power tower is taken into full account, to strategy corresponding to different electric power tower section uses；Rebuild in tower head
During, geometrical relationship is taken full advantage of between tower body reconstruction information, original point cloud information and parameter to reduce parameter as far as possible
Number and parameter search space；The present invention can effectively, accurately rebuild power tower, meet power transmission line three-dimensional visual, number
The demand of word.
Brief description of the drawings
Fig. 1 is power tower structural representation of the present invention.
Fig. 2 is angle point grid of the present invention with being layered schematic diagram in segmentation.
Fig. 3 is angle point grid of the present invention and angle point grid schematic diagram in segmentation.
Fig. 4 is angle point grid of the present invention and angle point rough schematic view in segmentation.
Fig. 5 is that angle point grid of the present invention splits schematic diagram with angle point in segmentation.
Fig. 6 is some subset schematic diagram of angle point during tower body of the present invention is rebuild.
Fig. 7 is angle point fitting result schematic diagram during tower body of the present invention is rebuild.
Fig. 8 is angle point tower body optimum results schematic diagram during tower body of the present invention is rebuild.
Fig. 9 is angle point optimum results top view illustration during tower body of the present invention is rebuild.
Figure 10 is the structural representations of tower head model library Model 1 that the present invention parameterizes.
Figure 11 is the structural representations of tower head model library Model 2 that the present invention parameterizes.
Figure 12 is the structural representations of tower head model library Model 3 that the present invention parameterizes.
Figure 13 is the structural representations of tower head model library Model 4 that the present invention parameterizes.
Figure 14 is Shape context algorithm point cloud configuration sampling point schematic diagram of the present invention.
Figure 15 is Shape context algorithm model configuration sampling point schematic diagram of the present invention.
Figure 16 is Shape context algorithm Shape context schematic diagram of the present invention.
Wherein, 1 is tower head, and 2 be tower body.
Embodiment
Accompanying drawing being given for example only property explanation, it is impossible to be interpreted as the limitation to this patent；It is attached in order to more preferably illustrate the present embodiment
Scheme some parts to have omission, zoom in or out, do not represent the size of actual product；To those skilled in the art,
Some known features and its explanation may be omitted and will be understood by accompanying drawing.Being given for example only property of position relationship described in accompanying drawing
Explanation, it is impossible to be interpreted as the limitation to this patent.
A kind of power tower three-dimensional rebuilding method based on LiDAR point cloud, wherein, comprise the following steps：
Step 1：The decomposition of power tower, the LiDAR point cloud data of power tower are obtained, pass through the density to power tower point cloud
Statistical analysis is carried out with width, power tower is decomposed into tower body and tower head two parts.
Power tower is that its tower body structure is simple, type is single with certain artificial atural object for building rule, tower head species compared with
It is more and complicated, it is contemplated that the architectural feature of power tower, power tower can be decomposed into two parts：Tower body and tower head, such as Fig. 1 institutes
Show.In order to clearly distinguish tower head and tower body, lower edge or the tower cross section sharp change in elevation that cross-arm is descended defined in the present invention are spy
Sign face, as shown in Fig. 1 a, 1b, part more than characteristic face is defined as tower head, and the part below characteristic face is defined as tower body.Although
The type of power tower has a variety of, but characteristic face all includes two obvious features：(1) maximum (2) part width of local density is most
It is small.Wherein, density is defined as the point cloud number of tower cross section, and width is defined as each section to the ultimate range of kernel of section；
Therefore, herein to power tower using the method for statistical analysis come location feature face.In order to obtain the statistics Nogata of density and width
Figure, if power tower is divided into dried layer, by counting each layer of density and width, choose has density local maxima and width simultaneously
Spend position of the minimum layer as characteristic face.
Comprise the following steps that：
1. by power tower point cloud, according to elevation, if Δ h is divided into dried layer at equal intervals.Δ h value sets smaller, characteristic face positioning
Precision is higher, generally Δ h/2, but to ensure each layer all a little.In the present invention, Δ h=0.5 rice；
2. calculating each layer of density and width, and density is established as X-axis, by Y-axis of the number of plies using density and width respectively
Histogram and width histogram；
3. defining one centered on current layer number, size is straight in density and width respectively for the moving window of 2L*1 sizes
Very dense value and width minimum are found in square figure.If the density of current layer is maximum in the range of 2L*1, the layer is carried out
Mark.If the width of current layer is minimum in the range of 2L*1, the layer is marked.The size 2*L of window and electric power tower height
Spend relevant, L should be less than the minimum range of adjacent two cross section when setting.L=3 rice is set in the present invention；
4. from bottom to up select first simultaneously meet this layer of density local maxima and it is upper and lower two layers in the range of width be present
Position of the number of plies of Local Minimum as characteristic face.Decomposed by boundary line of characteristic face, decomposed more than characteristic face is
Tower head, the decomposed below characteristic face is tower body.
Step 2：Tower body is rebuild based on data-driven, angle steel joint is extracted and split, to four main wheel profiles of tower body
3 d-line fitting is carried out based on RANSAC algorithms.
Wherein, the extraction and segmentation of angle point are carried out first；As shown in Figure 2 and Figure 3, not all profile point is all located at
Four angles of rectangle, therefore, it is necessary to the profile point after extraction is simplified based on pipeline algorithm.Calculate current point and adjacent two
The angle of point, if angle is more than predetermined threshold df, retains current point, otherwise, rejects.Due to only needing four angles herein
Angle point, therefore set df=30 °.When Tf set it is bigger when, profile point is fewer, but df should be less than 90 °.As shown in figure 4, pass through
Pipeline algorithm is simplified, and can delete most of non-angle point.Finally, the angle point simplified to each layer, according to it outside minimum
The position for connecing rectangular centre is split, and angle point is divided into the subset of four direction, and final result is as shown in Figure 5.Each subset
It is approximately 3 d-line in shape.
Secondly, the contour line fitting based on RANSAC；After above-mentioned steps are handled, the son of four tower body angle points is obtained
Collection.Because the angle point of not all simplification in each subset is at four angles, therefore, the three-dimensional based on RANSAC is used herein
Algorithm of fitting a straight line is fitted to each angle point subset.
As shown in fig. 6, in order to obtain the 3 d-line equation of each subset, the point of selection two P random first1(x1,y1,
z1) and P2(x2,y2,z2), and calculate the 3 d-line equation L where it.Wherein, 2 points of 3 d-line equation L is according to formula (1)
Calculated；Calculation formula is as follows：
Then, for the every bit in subset, calculate it and arrive straight line L distance.If the distance of current point to straight line is small
In distance threshold Td, then it is available point, otherwise, is defined as Null Spot.Distance threshold Td is noise spot degrees of tolerance, and Td is set
Put it is smaller, participate in calculate noise spot it is fewer, the present invention in set Td=0.1 rice.Statistics available point accounts for total points of the subset
Ratio inner_ratio.Stochastical sampling is repeated, until meeting that P is less than default fiducial probability TP or sampling number M more than default
Maximum sampling number TN, parameter of the one group of minimum parameter of regression criterion as the subset 3 d-line equation is chosen, such as Fig. 7 institutes
Show.
Wherein, under certain fiducial probability TP, bases subset minimum number of samples M in RANSAC algorithms and at least
The probability P of a correct sampling subset meet following relationship：
P=1- (1-inner_ratiom)M,
In above formula, inner_ratio is that available point accounts for the ratio always counted, required for m is one judgment criteria of structure
Minimum parameters number.It is 0.98 that the present invention, which sets fiducial probability, and maximum sampling number is 30 times.In the plan that four subsets are all independent
After conjunction, tower body reconstructed results are optimized using specification (such as symmetry, coplanarity, collimation) is built so that rebuild knot
Fruit meets actual architectural feature, as shown in Figure 8.In addition, tower body reconstructed results can be used in tower head weight as auxiliary information
During building.As shown in figure 9, it is determined that behind the position of characteristic face, the long tdx and wide tdy of characteristic face are calculated, and as tower
The nose heave known parameters built.
Step 3：Tower head is rebuild based on model-driven, a model library for including tower head fundamental type is predefined, then adopts
Tower head fundamental type is identified with Shape context algorithm, then Metropolis-Hastings algorithms are combined with simulated annealing
Estimate the optimized parameter of tower head model.
First, the tower head model library of parametrization is established.
Tower head is complicated, wide variety, and the reconstruction of tower head is difficult to realize using the strategy of data-driven.Because tower head has
There is certain construction rule, and only main material part needs to rebuild, therefore, using carrying out weight to tower head based on the method for model-driven
Build.The strategy of model-driven is to be based on predefined model library.With reference to the fundamental type of Chinese high-voltage power tower, a three-dimensional ginseng
The model library of numberization is defined first.The integrality of model library directly affects the result of reconstruction.The model driven method of the present invention
It is only capable of rebuilding the tower head type tower head point cloud defined in model library.If tower head types of models does not define, need first to exist
The type is defined in model library, then rebuild again.
As shown in Figure 10 to Figure 13, in the present invention, tower head model library includes four kinds in Chinese 220-500kV high voltage power transmissions
The widely used power tower type of circuit.To put it more simply, tower head model is represented with key point.In order to obtain the coordinate of key point,
Tower head parameter is mainly formed by feature is high with feature length.Due to the complexity of tower head, tower head model includes many parameters, causes to hold high
Expensive calculation cost carrys out the optimized parameter of appraising model.In order to reduce model parameter number and parameter search space, the present invention will
Make full use of the geometrical relationship between tower body reconstructed results, original point cloud information and parameter.The tower head parameter of the four types such as institute of table 1
Show.Wherein, the tower head parameter that unknown parameter is estimated for needs, it is known that parameter is that can be entered according to information such as the geometrical relationships between parameter
The parameter that row derives, is not involved in parameter estimation.
The tower head model parameter of table 1
From table 1 it follows that tower head model includes four basic parameters：tdx,tdy,Height,Length.tdx
It is special positive length and width respectively with tdy, can be obtained from tower body reconstructed results.Height and Length is tower head respectively
Size, it can be obtained from the length and width of original tower head point cloud.Other parameters can be entered according to the geometrical relationship with other specification
Row is derived by.By taking Model1 as an example, H3、L3Difference can obtain from formula (2) and formula (3).
H3=Height-H1-H2； (2)
In addition, parameter search space can also be according to the reduction of geometrical relationship high degree.So, the efficiency that tower body is rebuild
It can be greatly improved.
Secondly, tower head type is identified.
Difference is very big in shape for different types of tower head, therefore, tower head type can be entered according to the shape of tower head
Row identification.In the present invention, tower head point cloud and tower head model are first converted into two-value image, are then calculated using Shape context
Method realizes the matching of tower head point cloud image and corresponding phantom images.
Shape context is the Feature Descriptor measured shape similarity and find point correspondence.It knows in numeral
Not, the retrieval based on similitude, trade mark retrieval and Three-dimensional target recognition etc. extensive use.As shown in Figure 14, Figure 15, shape
The most basic thought of contextual algorithms is the n sampled point of selection on profile, then calculates the Shape context each put.Its
In, the Shape context each put is defined as histogram of the remaining n-1 point to the relative coordinate of current point, with current point piFor
In the center of circle, the local that R is radius N number of concentric circles is established by logarithm distance interval.By this region along the circumferential direction M deciles, formed
Target shape template as shown in figure 16.Current point is reduced in template the point minute in each sector to the vectorial relative position of other each points
Cloth number.The statistical distribution histogram h of these pointsi(k), referred to as point piShape context.By calculating image and model to be matched
Shape distance between image each pair point, finally the type of phantom images of the selection with minimum cost is as the tower head point cloud
Type.
Comprise the following steps that：
1. it is two-value image by all tower head model conversations in tower head point cloud and model library to be identified；
2. the outline of all images of extraction, samples to obtain n sampled point point set P={ p to contour edge1,p2,p3,
...pn}；
3. the Shape context h of each sampled point is calculated according to Shape context algorithmi(k), formula such as formula (4), k=
{ 1,2 ..K }, K=M*N；
hi(k)={ q ≠ pi:(q-pi∈ bin (k) }, (4)
4. calculate between the shape histogram of image to be matched and the shape histogram each pair point of each phantom images
With cost, formula such as formula (5), wherein, hi(k) the point p for being target PiShape histogram；hj(k) the point q for being target QiShape
Shape histogram；
5. carrying out Point matching operation based on the cost matrix C being calculated, the model shadow with minimum shape distance is chosen
As being best matching result, the model is that tower head point cloud corresponds to model.
Finally, tower head process of reconstruction is changed into determining for topic, Gibbs energy for Gibbs energy optimizations solution by the present invention
Justice is the similitude of tower head point cloud and model, is used for estimating using Metropolis-Hastings samplers and simulated annealing
Calculate the most optimized parameter of model.
Step 4：According to Step 1: Step 2: the position and direction in step 3 acquired results are combined, obtain
Whole three-dimensional power tower model.
Obviously, the above embodiment of the present invention is only intended to clearly illustrate example of the present invention, and is not pair
The restriction of embodiments of the present invention.For those of ordinary skill in the field, may be used also on the basis of the above description
To make other changes in different forms.There is no necessity and possibility to exhaust all the enbodiments.It is all this
All any modification, equivalent and improvement made within the spirit and principle of invention etc., should be included in the claims in the present invention
Protection domain within.
Claims (10)
1. a kind of power tower three-dimensional rebuilding method based on LiDAR point cloud, it is characterised in that comprise the following steps：
S1. the decomposition of power tower, the LiDAR point cloud data of power tower are obtained, is entered by the density to power tower point cloud and width
Row statistical analysis, power tower is decomposed into tower body and tower head two parts；
S2. tower body is rebuild based on data-driven, angle steel joint is extracted and split, four main wheel profiles of tower body are based on
RANSAC algorithms carry out 3 d-line fitting；
S3. tower head is rebuild based on model-driven, predefines a model library for including tower head fundamental type, then using in shape
Hereafter algorithm identification tower head fundamental type, then Metropolis-Hastings algorithms are combined estimation tower head with simulated annealing
The optimized parameter of model；
S4. the position and direction in S2, S3 step acquired results are combined, and obtain complete three-dimensional power tower model.
2. the power tower three-dimensional rebuilding method according to claim 1 based on LiDAR point cloud, it is characterised in that described
Density and width progress statistical analysis in S1 steps to power tower point cloud comprises the following steps：
If power tower point cloud is S101. divided into dried layer according to Δ h at equal intervals；
S102. each layer of density and width is calculated, and it is straight using density and width as X-axis, using the number of plies as Y-axis to establish density respectively
Side's figure and width histogram；
S103. one is defined centered on current layer number, and size is straight in density and width respectively for the moving window of 2L*1 sizes
Very dense value and width minimum are found in square figure, and layer where maximum and minimum is marked；
S104. power tower select first from bottom to up and meanwhile meet this layer of density local maxima and it is upper and lower two layers in the range of exist
Position of the number of plies of width Local Minimum as characteristic face, is decomposed by boundary line of characteristic face, part more than characteristic face point
Solve as tower head, the decomposed below characteristic face is tower body.
3. the power tower three-dimensional rebuilding method according to claim 1 based on LiDAR point cloud, it is characterised in that described
Angle steel joint extract and comprised the following steps with segmentation in S2 steps：
S2011. current point and adjacent 2 points of angle are calculated, if angle is more than predetermined threshold df；Then retain current point, otherwise,
Reject；
S2012. simplified by pipeline algorithm, delete most of non-angle point；
S2013. the angle point simplified to each layer, split according to it in the position at minimum enclosed rectangle center, by angle point
It is divided into the subset of four direction.
4. the power tower three-dimensional rebuilding method according to claim 3 based on LiDAR point cloud, it is characterised in that described
The contour line fitting based on RANSAC comprises the following steps in S2 steps：
S2021. two point P1 (x1, y1, z1) and P2 (x2, y2, z2) are randomly selected from the subset obtained by S1 steps, and calculate it
The 3 d-line equation L at place；
S2022. for the every bit in subset, calculate it and arrive straight line L distance, if current point to straight line distance less than away from
From threshold value Td, then it is available point, otherwise, is defined as Null Spot；And the ratio inner_ratio of available point is counted, wherein,
Inner_ratio is that available point accounts for the ratio always counted；
S2023. stochastical sampling is repeated, until meeting that P is more than default maximum sampling less than default fiducial probability TP or sampling number M
Number TN, choose parameter of the one group of minimum parameter of regression criterion as the subset 3 d-line equation；Wherein, P is sampling
The probability of collection；
S2024. after four subsets are fitted, symmetry is added, coplanarity restrictive condition optimizes to model.
5. the power tower three-dimensional rebuilding method according to claim 3 based on LiDAR point cloud, it is characterised in that described
Predetermined threshold df is 30 °.
6. the power tower three-dimensional rebuilding method according to claim 4 based on LiDAR point cloud, it is characterised in that described
3 d-line equation L calculation is：
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7. the power tower three-dimensional rebuilding method according to claim 4 based on LiDAR point cloud, it is characterised in that described
The probability P of sampling subset meets following relationship：
P=1- (1-inner_ratiom)M,
Wherein, m is to build the minimum parameters number required for a judgment criteria.
8. the power tower three-dimensional rebuilding method according to claim 1 based on LiDAR point cloud, it is characterised in that described
Identify that tower head fundamental type comprises the following steps in S3 steps：
S3011. it is two-value image by all tower head model conversations in tower head point cloud and model library to be identified；
S3012. the outline of all images is extracted, contour edge is sampled to obtain n sampled point point set P={ p1, p2,
p3... ..pn}；
S3013. the Shape context h of each sampled point is calculated according to Shape context algorithmi(k), calculation formula such as formula (1), k
={ 1,2 ..K }, K=M*N；
hi(k)={ q ≠ pi:(q-pi∈ bin (k) }, (1)
S3014. between the shape histogram of image to be matched and the shape histogram each pair point of each phantom images is calculated
With cost, calculation formula such as formula (2), wherein, hi (k) is target P point pi shape histogram；Hj (k) is target Q point qi
Shape histogram,
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S3015. Point matching operation is carried out based on the cost matrix C being calculated, chooses the model shadow with minimum shape distance
As being best matching result, the model is that tower head point cloud corresponds to model.
9. the power tower three-dimensional rebuilding method according to claim 1 based on LiDAR point cloud, it is characterised in that described
Parameter optimization comprises the following steps in S3 steps：
S3021.Gibbs energy definitions, Gibbs energy are expressed as formula and are：
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Using alpha shape algorithms come the key point of extraction model and point cloud, distance um(xi) for each model key point to most
The distance of nearly tower head point cloud key point, distance up(xi) it is distance of each tower head point cloud key point to nearest model key point, its
In：
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Wherein,Average distance us of the degree of similarity u (x) by model to point cloudm(xi) and point cloud putting down to model
Distance up(xi) composition, and u (x)=aum(x)+bup(x), a+b=1, a and b are respectively the weights of two distances；N closes for model
Key point number, m are tower head point cloud key point number；
S3022.Metropolis-Hastings is sampled and simulated annealing, first, according to suggestion distribution q (x*| x) in search space
A current parameter value x candidate parameter value x is provided in X*, then, the receiving of candidate parameter value is calculated according to calculation formula (6)
Rate A (x, x*)；If receptance A (x, x*) be more than default acceptance threshold Ta, then by candidate value x*Instead of currency x, otherwise, protect
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10. the power tower three-dimensional rebuilding method according to claim 9 based on LiDAR point cloud, it is characterised in that described
Acceptance threshold Ta=0.9+0.1*random (0,1), random (0,1) are that random sampling result is carried out in the range of 0~1.
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