CN105427317A - Method suitable for multi-view-angle automatic registration of ground laser point cloud data of multiple stations - Google Patents

Method suitable for multi-view-angle automatic registration of ground laser point cloud data of multiple stations Download PDF

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CN105427317A
CN105427317A CN201510833425.5A CN201510833425A CN105427317A CN 105427317 A CN105427317 A CN 105427317A CN 201510833425 A CN201510833425 A CN 201510833425A CN 105427317 A CN105427317 A CN 105427317A
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triangle
point
registration
station
semantic
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CN105427317B (en
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杨必胜
董震
周桐
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Wuhan University WHU
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Abstract

The invention relates to a method suitable for multi-view-angle automatic global registration of ground laser point cloud data. The method involves two key modules: a module for extracting semantic feature points and a module for feature matching. The method comprises: step 1, extracting the semantic feature points: obtaining the semantic feature points in a series of modes of data slicing, distance clustering, geometric element fitting and the like; step 2, matching the semantic feature points: matching the semantic feature points by constructing a triangular geometric constraint condition and a semantic constraint condition, and removing error matching in a geometric consistency clustering mode; and finally, constructing a weighted undirected graph by taking a reciprocal of a feature point number as a weight, and finally obtaining global registration parameters of all stations by taking a minimum spanning tree of the weighted undirected graph as a registration path, thereby realizing global optimal registration. According to the invention, the method suitable for multi-view-angle automatic global registration of the ground laser point cloud data is constructed; the method can effectively resist influences of noise, point density and coverage; and the method improves the laser scanning operation efficiency, thereby having very high practical values.

Description

A kind of method being applicable to various visual angles robotization registration multistation ground laser point cloud data
Technical field
The present invention relates to land station's laser scanning point cloud multistation robotization registration, belong to laser point cloud Measurement and Data Processing automation research field.
Background technology
Along with appearance and the development of laser scanner technique, people can the dense list face cloud data of quick obtaining object and scene.This technology is widely used in the fields such as reverse-engineering, virtual reality, three-dimensional reconstruction.Due to the scope that scans at every turn with apart from limited, obtain the complete point cloud data of a scene or object, need repeatedly to establish station scanning.Each coordinate establishing station scanning to obtain is all the local coordinates relative to scanning center, and this just needs to use registration technology by the data unification between different survey station under the same coordinate system.The registration of current main flow is generally divided into rough registration and smart registration two steps.ICP algorithm (BeslandMcKay, 1992) is the smart registration Algorithm of widespread use, but ICP algorithm needs comparatively accurately initial value reliably, otherwise cannot find the registration parameter of global optimum by being absorbed in local optimum.Rough registration algorithm, by calculating relevant geological information, for smart registration Algorithm provides comparatively accurate initial value, is mainly divided into based on point, based on line, based on face three class methods.Based on point method mainly through local shape factor Partial Feature point, then according to certain search strategy carry out registration parameter search calculate ( andBecker, 2007; BarneaandFilin, 2008; Rusu, 2008; Weinmann, 2011 etc.).Relative less with the method for registering based on face based on line, mainly by coupling of the same name, line of the same name, calculate registration parameter (StamosandLeordeanu, 2003; Habib, 2005; YangandZang, 2014; DoldandBrenner, 2004; TheilerandSchindler, 2012).These algorithms are mostly directed to a certain specific scene, devise the rough registration scheme of robotization, have got rough registration result.
Although said method all achieves certain result under special scenes, the dot density inequality that they are also subject to existing in scene, noise, the impact of blocking, need a large amount of man-machine interactivelies, and labour intensity is large, and efficiency is low.Based on the registration of point, be more prone to relative to based on face and the algorithm based on line the interference being subject to noise.Algorithm based on face requires to there is abundant face in scene, and when being subject to circumstance of occlusion, plane quantity of the same name can not meet the demands.Algorithm based on line generally only considered the sideline of buildings, and seldom utilizes the vertical shaft in scene, when scene be suburb or field forest time algorithm will be failed.Under the scene of these algorithm failures, registration needs manually to carry out, and substantially increases labor workload, reduces production efficiency.On the other hand, above algorithm all only relates to registration between two, needs adjacent two station location of artificial appointment, cannot realize the robotization registration of the overall situation.Therefore, higher in the urgent need to a kind of automaticity, be applicable to many scenes, globalize registration Algorithm that opposing noise and dot density affect enhances productivity further, minimizing labor workload.
Summary of the invention
The present invention, on the basis of above research, proposes a kind of new method being applicable to the multistation ground laser point cloud data robotization registration of various visual angles.The method is divided into two crucial modules: the extraction of semantic feature point and characteristic matching.The first step, carries out the extraction of semantic feature point, by data slicer, a series of mode such as distance cluster, geometric primitive matching etc., obtains semantic feature point; Second step, carries out the coupling of semantic feature point, mates semantic feature point by building triangle geometry constraint conditions with semantic constraint condition; And adopt the cluster mode of Geometrical consistency to reject the coupling of wherein mistake; Finally, using the inverse of the feature point number of mating as weights, build a weighted undirected graph, using the minimum spanning tree of weighted undirected graph as the path of registration, finally obtain the global registration parameter at each station, realize global optimum's registration.
The present invention is solved the problem by following techniqueflow:
Be applicable to a method for various visual angles robotization registration multistation ground laser point cloud data, it is characterized in that, comprising:
The step of an acquisition original point cloud data: the complete point cloud data obtaining a scene or object, the diverse location erection laser scanner in scene is needed to obtain data, each coordinate establishing station scanning to obtain is all the local coordinates relative to laser scanner center, and this just needs to use registration technology by the data unification between different survey station under the same coordinate system;
The step that a semantic feature point extracts: first a cloud segmentation is carried out to the original point cloud data obtained, then carry out the extraction of numerical characteristics line for the cloud data after segmentation, and carry out the calculating of semantic information; Specifically comprise:
Step 1.1, utilizes elevation information to be separated above ground portion in cloud data, divides horizontal graticule mesh, and the point within the scope of the certain elevation of minimum point thinking in graticule mesh is ground point.Then, non-above ground portion is divided section according to a determining deviation, certain thickness.
Step 1.2, carries out self-adaptation distance Euclidean distance cluster in each section.First in section, the Delauney triangulation network is built.Then, add up the length of side of the Delauney triangulation network around this point, can calculate distance threshold is:
dT s＝Mean(P s)+Variation(P s)
Wherein the average side length on the leg-of-mutton limit of all Delauney of being connected with this point, and Variation (P s) be the standard deviation of these length of sides.
Step 1.3, selects interested classification from cluster block, is facade and the shaft of buildings respectively.Shaft shows as a columniform structure in slice of data, demand fulfillment model:
||(P-Q)×Ca||-r＝0
Wherein, P=(x p, y p, z p) be on cylinder a bit, Q=(x q, y q, z q) be cylinder axis on a bit, Ca=(Ca x, Ca y, Ca z) be the vector of unit length of cylinder axis, r is the radius of cylinder.This method predetermined radius can only at certain threshold value R minto R maxin scope, and axial direction and Z axis less parallel.Aperture closes the cylinder section cluster block of condition of stating, and calculates the central point of center as shaft of these cylinder cluster blocks.And the facade of another kind of buildings shows as straight line in slice of data, should meeting geometric model:
x - x 0 a = y - y 0 b = z - z 0 c
Wherein, (x 0, y 0, z 0) and (x, y, z) be all point on straight line, (a, b, c) is the direction of this straight line.The minimum length of straight limit is L simultaneously min, and direction (a, b, c) is approximate vertical with Z axis.Retain qualified cluster block as buildings cluster block, calculate its two end points, retain as unique point, and judge that it is sideline point or the intersection point of buildings.
Step 1.4, obtains the unique point with semantic information.Using the intersection point on above-mentioned straight line and ground as unique point.Calculate the semantic information of this point simultaneously.These semantic informations can be expressed as with next vector:
L Feature(Pt lowest,Pt highest,Pt num,L height,L id,L category,L radius,Pl direction1,Pl direction2)
Pt lowest, Pt highest, Pt num, L heightand L idbe respectively the minimum point, peak, the ID of some number, elevation and this point that comprises that obtain this some vertical curve crossing with ground used.L categoryrepresenting the type of this point, is facade sideline, facade intersection or the intersection point on shaft center line and ground.L radiusrepresent the mean radius of shaft, be only present in shaft center line and ground intersection point.Pl direction1and Pl direction2represent the trend of two the buildings facades building this point, be only present in the intersection point on two facade intersections and ground.If the intersection point on the sideline of buildings and ground, then only has a direction Pl direction1
One mates the step with global registration: mate semantic feature point by building triangle geometry constraint conditions with semantic constraint condition, first obtain the triangle pair of a preliminary matches, the triangle then for the preliminary matches obtained adopts the cluster mode of Geometrical consistency to reject the coupling of wherein mistake; Finally, using the inverse of the feature point number of mating as weights, build a weighted undirected graph, using the minimum spanning tree of weighted undirected graph as the path of registration, finally obtain the global registration parameter at each station, specifically comprise:
Step 2.1, builds semantic feature point network of triangle.Triangle is built to all semantic feature points, can obtain individual triangle, wherein NI represents the number of semantic feature point.In order to reduce operand, accelerating arithmetic speed, rejecting wherein isogonism, be similar to conllinear and the shorter triangle of the length of side.Hash table is set up, with area and this leg-of-mutton call number of circumference calculating for remaining triangle:
l i n e _ i n d e x = [ A r e a B i n ] r o w _ i n d e x = [ P e r i m e t e r B i n ]
Above formula illustrates, the account form of line index and column index.Bin value is by given in advance, and [] expression rounds up.
Step 2.2, carries out mating of geometrical constraint and semantic constraint. with represent the triangle sets retained in base station Ps and Target Station Pt respectively.For a triangle T of wherein base station i s, find the triangle of the most similar to it (congruence) in Target Station its similarity is judged by following formula:
s i m i l a r i t y = 1 | Δ 1 | + | Δ 2 | + | Δ 3 |
Wherein Δ 1, Δ 2, Δ 3be respectively the difference on three limits corresponding to triangle, and given three restrictive conditions:
Restrictive condition one: T i sbe in Ps with the most similar triangle, simultaneously also be in Pt with T i sthe most similar triangle.
Restrictive condition two: meet wherein, <> represents corresponding difference in length, be in Pt with T i striangle like second-phase, be in Ps with triangle like second-phase.This condition ensure that similar stability, eliminates the impact of noise.
Restrictive condition three: semantic information retrains.The type of corresponding point is identical.
Meet the triangle pair of triangle pair as preliminary matches of above three restrictive conditions simultaneously.
Step 2.3, further rejects the triangle pair of preliminary matches.The principle rejected be based on the triangle pair of correct coupling between distance be consistent, and the spacing of the triangle pair of incorrect coupling is inconsistent, and principle illustrates sees Fig. 7.{ C 1, C 2... C nCrepresent the set of mating triangle pair. with it is the triangle pair of two couples coupling wherein.If these two triangle pairs meet:
a b s ( | | T i S - T n S | | - | | T j t - T m t | | ) < GC c o n s t r a int
Then these two triangles are classified as same class.|| || represent the Euclidean distance of two triangle center, T i swith a cloud P sthe feature triangle constructed, with p tin corresponding with it triangle, GC constraintit is a less threshold value.Gained comprised the maximum classification of number of triangles as correct classification, can obtain mating triangle, and then obtain the corresponding point of mating.These points will be used for calculating registration parameter.
Step 2.4, global coherency registration.The triangle that above step can obtain coupling is performed to any two scanning movement data.With the inverse of the number of triangles of coupling for power, build the weighted graph of full-mesh.Utilize Kruskal algorithm to generate the minimum spanning tree of this figure, the registration path between any two stations can be obtained.Choose one of them station as the root node (fixed station) of tree, make the degree of depth of this tree minimum.Utilize the corresponding vertex of the triangle pair of coupling, calculate the conversion parameter between any two stations be communicated with, and then the path by setting, can often be stood relative to the conversion parameter of fixed station.Complete the overall robotization registration at all stations.
The step that registration result exports: the parameter of mating between two in the coupling path obtained for last step and path, utilizes ICP registration principle, carry out smart registration, obtain the conversion parameter of refining to registration station each in path.
The step that registration result exports: the parameter of mating between two in the coupling path obtained for last step and path, utilizes ICP registration principle, carry out smart registration, obtain the conversion parameter of refining to registration station each in path.
In a kind of above-mentioned method being applicable to various visual angles robotization registration multistation ground laser point cloud data, the self-adaptation distance taked in described step 1.2, there is following characteristic: its adaptive threshold is determined by the mean value of the Delaunay triangle length of side built and variance
dT s＝Mean(P s)+Variation(P s)
Wherein the average side length on the leg-of-mutton limit of all Delaunay of being connected with this point, and Variation (P s) be the standard deviation of these length of sides.
In a kind of above-mentioned method being applicable to various visual angles robotization registration multistation ground laser point cloud data, in described step 2.2, its three matching criterior have following characteristic: with represent the triangle sets retained in station, basis and Target Station respectively.For wherein basis station a triangle T i s, find the triangle of the most similar to it (congruence) in Target Station specifically comprise:
Bar matching criterior one, each other optiaml ciriterion.T i sbe in Ps with the most similar triangle, simultaneously also be in Pt with T i sthe most similar triangle.
Bar matching criterior two, optimum is better than suboptimum criterion, meets:
wherein, <> represents corresponding difference in length, be in Pt with T i striangle like second-phase, be in Ps with triangle like second-phase.This condition ensure that similar stability, eliminates the impact of noise.
Bar matching criterior three, semantic congruence criterion.The type of corresponding point is identical, and the facade direction β of point by facade gained 1=Pl directio1n-Pl ' direct1ionwith β 2=Pl direction2-Pl ' direction2the difference of the rotation angle and 3 rotation angle α calculated that calculate gained will be within the specific limits.
Therefore, tool of the present invention has the following advantages: achieve multistation ground laser point cloud without the full-automatic registration of target, under the condition without any priori match information, automatically realize resolving of the initial conversion parameter between two stations, and rely on minimum spanning tree principle automatically to obtain Optimum Matching path, achieve the overall robotization registration of large scene multi-site cloud.Present method solves multistation without target robotization initial registration, the difficult problem in the registration process such as the automatic selection in registration path, provides the total solution of land station's laser point cloud registration.The method adopts computer software mode to support automatic operational scheme, simple to operate, easily realizes, can greatly reduce labor workload, improve production work efficiency, in measurement is produced, have very high using value.
Accompanying drawing illustrates:
Fig. 1 is the method flow diagram of the embodiment of the present invention.
Fig. 2 is that the adaptive threshold cluster threshold value of the embodiment of the present invention chooses schematic diagram.
Fig. 3 is dropping cut slice and the cluster result schematic diagram of the embodiment of the present invention.
Fig. 4 is the matching of embodiment of the present invention geometric primitive and sideline thereof, central point result of calculation schematic diagram.
Fig. 5 is embodiment of the present invention semantic feature point result of calculation and its semantic information schematic diagram.
Fig. 6 is embodiment of the present invention semantic information constraint schematic diagram.
Fig. 7 is embodiment of the present invention Geometrical consistency constraint schematic diagram.
Fig. 8 a is the weighted undirected graph established between website.
Fig. 8 b is the minimum spanning tree of weighted undirected graph.
Fig. 9 a is the result figure of urban settings test data registration in the embodiment of the present invention.
Fig. 9 b is the result figure of scrnario testing Registration of Measuring Data in suburb in the embodiment of the present invention.
Fig. 9 c is the result figure of indoor scene test data registration in the embodiment of the present invention.
Embodiment
The present invention relates to a kind of many scenes ground laser point cloud data robotization global registration method.The method is divided into two crucial modules: the extraction of semantic feature point and characteristic matching.The first step, carries out the extraction of semantic feature point, by data slicer, a series of mode such as distance cluster, geometric primitive matching etc., obtains semantic feature point; Second step, carries out the coupling of semantic feature point, mates semantic feature point by building triangle geometry constraint conditions with semantic constraint condition; And adopt the cluster mode of Geometrical consistency to reject the coupling of wherein mistake; Finally, using the inverse of the feature point number of mating as weights, build a weighted undirected graph, using the minimum spanning tree of weighted undirected graph as the path of registration, finally obtain the global registration parameter at each station, realize global optimum's registration.The present invention constructs a kind of full automatic without target land station laser point cloud data global registration method, improve the automaticity of Point Cloud Processing, improve production efficiency, and method simply, easily realizes.
Technical solution of the present invention adopts computer software mode to support automatic operational scheme, and its flow process as shown in Figure 1.Technical solution of the present invention is described in detail below in conjunction with three embodiments and accompanying drawing.
Three examples of implementation correspond respectively to different scenes.First is urban settings, and second scenario is suburb scene, and the 3rd scene is indoor scene.Three examples of implementation carry out registration according to the scheme of design, are progressively described in detail as follows
1) ground and dropping cut slice is split.
First, to original some cloud, dividing resolution is the horizontal graticule mesh of 0.2m, and the point within the scope of the minimum point 0.1m elevation thinking in graticule mesh is ground point, and other points are non-ground points.Then, non-above ground portion is divided elevation section according to the spacing of 0.4m, the thickness of 0.2m, and it divides schematic diagram as shown in Fig. 3 (a).
2) self-adaptation distance threshold cluster
The Delauney triangulation network is built in section.Then, the length of side of the Delauney triangulation network around this point is added up, as shown in Figure 2.Can calculate distance threshold is:
dT s＝Mean(P s)+Variation(P s)
Wherein the average side length on the leg-of-mutton limit of all Delauney of being connected with this point, and Variation (P s) be the standard deviation of these length of sides.Carry out distance cluster according to this threshold value, the cluster section as shown in Fig. 3 (b) can be obtained.
3) to each cluster fitting circle column model:
||(P-Q)×Ca||-r＝0
Wherein, P=(x p, y p, z p) be on cylinder a bit, Q=(x q, y q, z q) be cylinder axis on a bit, Ca=(Ca x, Ca y, Ca z) be the vector of unit length of cylinder axis, r is the radius of cylinder.Predetermined radius threshold value is in 0.05m to 0.5m scope, and axial direction and Z axis less parallel (angular separation is less than 10 °).Aperture closes the cylinder section cluster block of condition of stating, and calculates the central point of center as shaft of these cylinder cluster blocks, as shown in Figure 4.Again to each cluster block fitting a straight line model:
x - x 0 a = y - y 0 b = z - z 0 c
Wherein, (x 0, y 0, z 0) and (x, y, z) be all point on straight line, (a, b, c) is the direction of this straight line.The minimum length of straight limit is 1m simultaneously, and direction (a, b, c) is close perpendicular to Z axis (angular separation is less than 10 °).Retain qualified cluster block as buildings cluster block, calculate its two end points, retain as unique point, and judge that it is sideline point or the intersection point of buildings, schematic diagram as shown in Figure 4.
4) projection obtains unique point, computing semantic information
Calculate 3) in the intersection point on the vertical line that is linked to be of institute invocation point and ground as unique point, schematic diagram is as shown in Figure 5.Give this point to be correlated with semantic information simultaneously.These semantic informations can be expressed as with next vector:
L Feature(Pt lowest,Pt highest,Pt num,L height,L id,L category,L radius,Pl direction1,Pl direction2)
Pt lowest, Pt highest, Pt num, L heightand L idbe respectively the minimum point, peak, the ID of some number, elevation and this point that comprises that obtain this some vertical curve crossing with ground used.L categoryrepresenting the type of this point, is facade sideline, facade intersection or the intersection point on shaft center line and ground.L radiusrepresent the mean radius of shaft, be only present in shaft center line and ground intersection point.Pl direction1and Pl direction2represent the trend of two the buildings facades building this point, be only present in the intersection point on two facade intersections and ground.If the intersection point on the sideline of buildings and ground, then only has a direction Pl direction1
5) foundation shown of leg-of-mutton structure and hash
All semantic feature points are appointed and gets three somes structure triangles, can obtain individual triangle, wherein NI represents the number of semantic feature point.Calculate each leg-of-mutton length of side and angle, reject wherein isogonism, be similar to conllinear and the too short triangle of most minor face.Hash table is set up, with area and this triangle of girth index for remaining triangle:
l i n e _ i n d e x = &lsqb; A r e a B i n &rsqb; r o w _ i n d e x = &lsqb; P e r i m e t e r B i n &rsqb;
Above formula illustrates, the account form of line index and column index.Bin value is respectively 0.05m 2with 0.1m, [] expression rounds up.The corresponding leg-of-mutton ID value of storage of each index value.
6) geometrical constraint mates with semantic constraint
Leg-of-mutton similarity is calculated by following formula.
s i m i l a r i t y = 1 | &Delta; 1 | + | &Delta; 2 | + | &Delta; 3 |
Wherein Δ 1, Δ 2, Δ 3be respectively the difference on three limits corresponding to triangle.
with represent the triangle sets retained in station, basis and Target Station respectively.For wherein basis station a triangle T i swith Target Station intermediate cam shape find all triangle pairs meeting following three restrictive conditions.
1. T i sbe in basic site cloud Ps with the most similar triangle, simultaneously also be in targeted sites cloud Pt with T i sthe most similar triangle.
2. meet wherein, <> represents corresponding difference in length, be in Pt with T i striangle like second-phase, be in Ps with triangle like second-phase.U herein matchingvalue is 1.1.
3. semantic information constraint.The type of corresponding point is identical, and the facade direction β of point by facade gained 1=Pl direction1-Pl ' directio1nwith β 2=Pl direction2-Pl ' direction2the difference calculating the rotation angle of gained and 3 rotation angle α calculated will within the specific limits (in embodiment, value is 5 °), as shown in Figure 6.
Meet the triangle pair of triangle pair as preliminary matches of above three restrictive conditions simultaneously.
7) Geometrical consistency rejects erroneous matching
Distance between the triangle pair of correct coupling is consistent, and the spacing of the triangle pair of incorrect coupling is inconsistent, principle illustrates sees Fig. 7, relative position relation between the triangle of wherein same color is consistent, triangle relative position relation between different colours is inconsistent, can think that maximum group (blueness) is correct coupling.{ C 1, C 2... C nCrepresent the set of mating triangle pair. with it is the triangle pair of two couples coupling wherein.If these two triangle pairs meet:
a b s ( | | T i S - T n S | | - | | T j t - T m t | | ) < GC c o n s t r a int
Then these two triangles are classified as same class.|| || represent the Euclidean distance of two triangle center, T i swith a cloud P sthe feature triangle constructed, with p tin corresponding with it triangle, GC constraintget 0.3m.Gained comprised the maximum classification of number of triangles as correct classification.Using the corresponding vertex of the triangle pair of correct classification as match point.
8) global coherency registration
Above-mentioned steps is carried out to any two stations, the coupling number of triangles at any two stations can be obtained.With the inverse of the number of triangles of coupling for power, build the weighted graph of full-mesh, schematic diagram is as shown in Fig. 8 (a).Utilize Kruskal algorithm to generate the minimum spanning tree of this figure, schematic diagram is as shown in Fig. 8 (b).Choose one of them station as the root node (fixed station) of tree, make the degree of depth of this tree minimum.
Travel through every bar limit of this connection tree, the match point at two stations utilizing this limit to be connected, use least square method to calculate conversion parameter, make following formula minimum:
&delta; = &Sigma; l = 1 m | | P l , t arg e t - T ( P l , s o u r c e ) | | , T ( P l , s o u r c e ) = A * P l , s o u r c e + B ,
Wherein A is 3 × 3 rotation matrixs, B D translation vector, P l, sourceand P l, targetbe the semantic feature point coordinate of two station couplings respectively, m is the quantity of match point, and δ is the residual error distance of corresponding point between two stations.Namely the conversion parameter of gained can be used as the initial value of ICP algorithm, carries out ICP iteration and obtains optimum registration parameter.This method for registering is applicable to many scenes, and Fig. 9 (a), (b), (c) are respectively suburb, city, indoor scene registration result.
Based on the present invention, the global registration without target multistation ground laser point cloud data fast can be realized steadily.
Specific embodiment described herein is only to the explanation for example of the present invention's spirit.Those skilled in the art can make various amendment or supplement or adopt similar mode to substitute to described specific embodiment, but can't depart from spirit of the present invention or surmount the scope that appended claims defines.

Claims (3)

1. be applicable to a method for various visual angles robotization registration multistation ground laser point cloud data, it is characterized in that, comprising:
The step of an acquisition original point cloud data: the complete point cloud data obtaining a scene or object, the diverse location erection laser scanner in scene is needed to obtain data, each coordinate establishing station scanning to obtain is all the local coordinates relative to laser scanner center, and this just needs to use registration technology by the data unification between different survey station under the same coordinate system;
The step that a semantic feature point extracts: first a cloud segmentation is carried out to the original point cloud data obtained, then carry out the extraction of numerical characteristics line for the cloud data after segmentation, and carry out the calculating of semantic information; Specifically comprise:
Step 1.1, utilizes elevation information to be separated above ground portion in cloud data, divides horizontal graticule mesh, and the point within the scope of the certain elevation of minimum point thinking in graticule mesh is ground point; Then, non-above ground portion is divided section according to a determining deviation, certain thickness;
Step 1.2, carries out self-adaptation distance Euclidean distance cluster in each section; First in section, the Delauney triangulation network is built; Then, add up the length of side of the Delauney triangulation network around this point, can calculate distance threshold is:
dT s＝Mean(P s)+Variation(P s)
Wherein Mean (P s) be the average side length on the leg-of-mutton limit of all Delauney of being connected with this point, and Variation (P s) be the standard deviation of these length of sides;
Step 1.3, selects interested classification from cluster block, is facade and the shaft of buildings respectively; Shaft shows as a columniform structure in slice of data, demand fulfillment model:
||(P-Q)×Ca||-r＝0
Wherein, P=(x p, y p, z p) be on cylinder a bit, Q=(x q, y q, z q) be cylinder axis on a bit, Ca=(Ca x, Ca y, Ca z) be the vector of unit length of cylinder axis, r is the radius of cylinder; This method predetermined radius can only at certain threshold value R minto R maxin scope, and axial direction and Z axis less parallel; Aperture closes the cylinder section cluster block of condition of stating, and calculates the central point of center as shaft of these cylinder cluster blocks; And the facade of another kind of buildings shows as straight line in slice of data, should meeting geometric model:
x - x 0 a = y - y 0 b = z - z 0 c
Wherein, (x 0, y 0, z 0) and (x, y, z) be all point on straight line, (a, b, c) is the direction of this straight line; The minimum length of straight limit is L simultaneously min, and direction (a, b, c) is approximate vertical with Z axis; Retain qualified cluster block as buildings cluster block, calculate its two end points, retain as unique point, and judge that it is sideline point or the intersection point of buildings;
Step 1.4, obtains the unique point with semantic information; Using the intersection point on above-mentioned straight line and ground as unique point; Calculate the semantic information of this point simultaneously; These semantic informations can be expressed as with next vector:
L Feature(Pt lowest,Pt highest,Pt num,L height,L id,L category,L radius,Pl direction1,Pl direction2)
Pt lowest, Pt highest, Pt num, L heightand L idbe respectively the minimum point, peak, the ID of some number, elevation and this point that comprises that obtain this some vertical curve crossing with ground used; L categoryrepresenting the type of this point, is facade sideline, facade intersection or the intersection point on shaft center line and ground; L radiusrepresent the mean radius of shaft, be only present in shaft center line and ground intersection point; Pl direction1and Pl direction2represent the trend of two the buildings facades building this point, be only present in the intersection point on two facade intersections and ground; If the intersection point on the sideline of buildings and ground, then only has a direction Pl direction1
One mates the step with global registration: mate semantic feature point by building triangle geometry constraint conditions with semantic constraint condition, first obtain the triangle pair of a preliminary matches, the triangle then for the preliminary matches obtained adopts the cluster mode of Geometrical consistency to reject the coupling of wherein mistake; Finally, using the inverse of the feature point number of mating as weights, build a weighted undirected graph, using the minimum spanning tree of weighted undirected graph as the path of registration, finally obtain the global registration parameter at each station, specifically comprise:
Step 2.1, builds semantic feature point network of triangle; Triangle is built to all semantic feature points, can obtain individual triangle, wherein NI represents the number of semantic feature point; In order to reduce operand, accelerating arithmetic speed, rejecting wherein isogonism, be similar to conllinear and the shorter triangle of the length of side; Hash table is set up, with area and this leg-of-mutton call number of circumference calculating for remaining triangle:
l i n e _ i n d e x = &lsqb; A r e a B i n &rsqb; r o w _ i n d e x = &lsqb; P e r i m e t e r B i n &rsqb;
Above formula illustrates, the account form of line index and column index; Bin value is by given in advance, and [] expression rounds up;
Step 2.2, carries out mating of geometrical constraint and semantic constraint; with represent the triangle sets retained in base station Ps and Target Station Pt respectively; For a triangle of wherein base station find the triangle of the most similar to it (congruence) in Target Station its similarity is judged by following formula:
s i m i l a r i t y = 1 | &Delta; 1 | + | &Delta; 2 | + | &Delta; 3 |
Wherein Δ 1, Δ 2, Δ 3be respectively the difference on three limits corresponding to triangle, and given three restrictive conditions:
Restrictive condition one: be in Ps with the most similar triangle, simultaneously also be in Pt with the most similar triangle;
Restrictive condition two: meet wherein, <> represents corresponding difference in length, be in Pt with triangle like second-phase, be in Ps with triangle like second-phase; This condition ensure that similar stability, eliminates the impact of noise;
Restrictive condition three: semantic information retrains; The type of corresponding point is identical;
Meet the triangle pair of triangle pair as preliminary matches of above three restrictive conditions simultaneously;
Step 2.3, further rejects the triangle pair of preliminary matches; The principle rejected be based on the triangle pair of correct coupling between distance be consistent, and the spacing of the triangle pair of incorrect coupling is inconsistent, and principle illustrates sees Fig. 7; { C 1, C 2... C nCrepresent the set of mating triangle pair; with it is the triangle pair of two couples coupling wherein; If these two triangle pairs meet:
a b s ( | | T i S - T n S | | - | | T j t - T m t | | ) < GC c o n s t r a int
Then these two triangles are classified as same class; || || represent the Euclidean distance of two triangle center, with a cloud P sthe feature triangle constructed, with p tin corresponding with it triangle, GC constraintit is a less threshold value; Gained comprised the maximum classification of number of triangles as correct classification, can obtain mating triangle, and then obtain the corresponding point of mating; These points will be used for calculating registration parameter;
Step 2.4, global coherency registration; The triangle that above step can obtain coupling is performed to any two scanning movement data; With the inverse of the number of triangles of coupling for power, build the weighted graph of full-mesh; Utilize Kruskal algorithm to generate the minimum spanning tree of this figure, the registration path between any two stations can be obtained; Choose one of them station as the root node (fixed station) of tree, make the degree of depth of this tree minimum; Utilize the corresponding vertex of the triangle pair of coupling, calculate the conversion parameter between any two stations be communicated with, and then the path by setting, can often be stood relative to the conversion parameter of fixed station; Complete the overall robotization registration at all stations; The step that registration result exports: the parameter of mating between two in the coupling path obtained for last step and path, utilizes ICP registration principle, carry out smart registration, obtain the conversion parameter of refining to registration station each in path.
2. a kind of method being applicable to various visual angles robotization registration multistation ground laser point cloud data according to claims, it is characterized in that, the self-adaptation distance taked in described step 1.2, there is following characteristic: its adaptive threshold is determined by the mean value of the Delaunay triangle length of side built and variance
dT s＝Mean(P s)+Variation(P s)
Wherein Mean (P s) be the average side length on the leg-of-mutton limit of all Delaunay of being connected with this point, and Variation (P s) be the standard deviation of these length of sides.
3. a kind of method being applicable to various visual angles robotization registration multistation ground laser point cloud data according to claims, it is characterized in that, in described step 2.2, its three matching criterior have following characteristic: with represent the triangle sets retained in station, basis and Target Station respectively; For wherein basis station a triangle find the triangle of the most similar to it (congruence) in Target Station specifically comprise:
Bar matching criterior one, each other optiaml ciriterion; be in Ps with the most similar triangle, simultaneously also be in Pt with the most similar triangle;
Bar matching criterior two, optimum is better than suboptimum criterion, meets:
wherein, <> represents corresponding difference in length, be in Pt with triangle like second-phase, be in Ps with triangle like second-phase; This condition ensure that similar stability, eliminates the impact of noise;
Bar matching criterior three, semantic congruence criterion; The type of corresponding point is identical, and the facade direction β of point by facade gained 1=Pl directio1n-Pl ' direct1ionwith β 2=Pl direction2-Pl ' direction2the difference of the rotation angle and 3 rotation angle α calculated that calculate gained will be within the specific limits.
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