CN103279989A - Three-dimensional laser imaging system planar point cloud data triangularization processing method - Google Patents
Three-dimensional laser imaging system planar point cloud data triangularization processing method Download PDFInfo
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- CN103279989A CN103279989A CN2013102083502A CN201310208350A CN103279989A CN 103279989 A CN103279989 A CN 103279989A CN 2013102083502 A CN2013102083502 A CN 2013102083502A CN 201310208350 A CN201310208350 A CN 201310208350A CN 103279989 A CN103279989 A CN 103279989A
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Abstract
The invention discloses a three-dimensional laser imaging system planar point cloud data triangularization processing method. According to the three-dimensional laser imaging system planar point cloud data triangularization processing method, firstly, three-dimensional point cloud data which are on the same plane are obtained from three-dimensional point cloud data, the three-dimensional point cloud data which are on the same plane are projected to a two-dimensional plane to form plane two-dimensional point cloud data, a virtual central point of the two-dimensional point cloud data is calculated, a point which is closest to the virtual central point is used as an actual central point of scattered data, distances between discrete points and the actual central point are compared, the two-dimensional point cloud data are presorted to find an initial triangle and initial boundary vertexes, the initial boundary vertexes are sorted anticlockwise, all the two-dimensional point cloud data are inserted into according to the arrayed sequence, boundary vertex update and triangle update are conducted, and planar point cloud data of a plane triangulation network are mapped back to a three-dimensional coordinate to generate three-dimensional triangular network model of an article to be scanned. Compared with the prior method, the three-dimensional laser imaging system planar point cloud data triangularization processing method has the advantages that the train of thought is simple and clear, programming is easy to achieve, and planes with holes or concave surfaces or other complex surface conditions can be processed.
Description
Technical field
The present invention relates to a kind of three-dimensional laser imaging system plane cloud data trigonometric ratio disposal route, relate in particular to a kind of method that adopts pointwise insertion and triangulation network growth plane cloud data trigonometric ratio, belong to free form surface constructing technology field.
Background technology
The three-dimensional laser imaging system refers to laser scanner, GPS, inertial measurement system etc. are integrated into a set of equipment, be contained on the carrier and scan on a surface target, obtain the three-dimensional information of terrain object, obtain the three-dimension space image that needs by processing.Because the laser spots data of obtaining that reflect are the nebulous dense distribution, so be called laser point cloud (Point Cloud) visually, looking like presents the result of object in computing machine with the rule of measuring for countless points.The three-dimensional laser imaging system is joined true color by giving digital point cloud chart, thereby obtains the colored true three-dimensional model of scanned object.Obtain the laser spots cloud atlas by the three-dimensional laser imaging system and can not present scanned three-dimensional shape features, and scan repeatedly scanned during the work of three-dimensional laser imaging system, therefore the complex point of much attaching most importance to of the data point in the laser spots cloud atlas that forms, at first need to delete the data point that repeats in the laser spots cloud atlas in order to construct scanned three-dimensional model, then according to scanned shape facility (length, highly, width etc.) laser point cloud is segmented, cloud data point after will segmenting at last connects for the plane by certain rule, so just can finish scanned three-dimensional imaging.
The laser point cloud that the three-dimensional laser imaging system forms is three-dimensional laser point cloud, carries out surface modeling and make up very three-dimensional this technology of the Delaunay triangulation network also do not have maturation for the some cloud of three-dimensional.Do not have unified algorithm can solve this problem fully at present, the model of structure in detail can't be satisfactory.Because the complicacy of topological relation is not perfect to theory and the algorithm of its direct subdivision between the three-dimensional scattered data points.The invention discloses the method for a kind of pointwise insertion and triangulation network growth plane cloud data trigonometric ratio, method not only can be used in the triangulation of plane scattered data points in this, also can be used in the triangulation of three-dimensional laser imaging system laser point cloud.It is simple that the present invention has thinking, and programming realizes easily and can handle the advantage on the plane of complex surface situations such as concave surface or hole.
Summary of the invention
The technical matters that the present invention solves is: overcome the deficiencies in the prior art, a kind of three-dimensional laser imaging system plane cloud data trigonometric ratio disposal route is provided, this method is tied the three-dimensional laser point cloud data point, thereby generate scanned three-dimensional model, the inventive method is simple, is easy to programming and realizes that the triangulation curved surface is smooth meticulous, generate the high advantage of three-dimensional model resolution, can handle the curved surface of the complex surface situation that has hole or concave surface.
The technical scheme that the present invention solves is: a kind of three-dimensional laser imaging system plane cloud data trigonometric ratio disposal route, and step is as follows:
(1) from the 3 D stereo cloud data that the scanning of three-dimensional laser imaging system obtains, obtains to belong to conplane 3 D stereo cloud data, conplane 3 D stereo cloud data is projected to form the planar cloud data on the two dimensional surface;
(2) virtual center point of calculation procedure (1) planar cloud data;
(3) ask for distance between all planar cloud datas and its virtual center point, find out all planar cloud data middle distance virtual center point apart from the planar cloud data of minimum, it as the real center point, and is designated as a little 1;
(4) ask for distance between the real center point that finds in all planar cloud datas and the step (3), according to real center point distance order from small to large the planar cloud data being sorted, the result of ordering is designated as a little 1 successively, point 2, point 3, put 4 ... point N;
(5) utilize first three the planar cloud data through sorting after handling to surround an initial delta as the summit, three summits of initial delta are as the initial boundary summit, and definite initial boundary zenith directions is for be that border vertices rises and presses counter clockwise direction and sort to put 1;
(6) visible starting point and the visible destination node on initial boundary summit found to the vector on initial boundary summit in insertion point 4 on the basis of step (5), calculation level 4, forms new border vertices, then initial delta upgraded;
(7) according to the method for step (6) insertion point 5 successively, point 6 ... point N, calculate the insertion point respectively to the vector between the border vertices that forms before, the visible starting point of the border vertices that forms before finding and visible destination node finally form a plane trigonometry net;
(8) the plane trigonometry net is optimized processing;
(9) will return three-dimensional from two-dimensional map through the plane trigonometry net after step (8) processing, finally obtain the 3 D stereo triangulation network trrellis diagram of 3 D stereo cloud data, and realize the trigonometric ratio of three-dimensional laser imaging system plane cloud data is handled.
The implementation method of described step (1) is:
(1) selects according to the three-dimensional coordinate of 3 D stereo cloud data and belong to conplane 3 D stereo cloud data and preserve;
(2) with the 3 D stereo cloud data preserved or be rotated to remove or directly remove third dimension cloud data and obtain the planar cloud data, and the third dimension cloud data after will removing is preserved.
The implementation method of described step (2) is:
(1) finds the minimum horizontal ordinate x of cloud data in all planar cloud datas
Min, maximum horizontal ordinate x
Max, minimum ordinate y
Min, maximum ordinate y
Max, according to minimum horizontal ordinate x
Min, maximum horizontal ordinate x
Max, minimum ordinate y
Min, maximum ordinate y
MaxObtain comprising the minimum rectangle of all planar cloud datas, work: A(x is remembered on four summits of rectangle respectively
Min, y
Min), B(x
Max, y
Min), C(x
Max, y
Max), D(x
Min, y
Max);
(2) ask for the central point E of rectangle, the coordinate of some E is
Point E is the virtual center point of planar cloud data.
The initial boundary zenith directions is for be that border vertices rises and presses the method that counter clockwise direction sorts and be to put 1 in the described step (5):
(1) the planar cloud data is increased three-dimensional data, wherein the value of third dimension data is 0;
(2) try to achieve a little 1 vector to point 2
With the vector of point 1 to point 3
(3) to vector
With
Carry out the multiplication cross computing, judge the third dimension value of the vector that the multiplication cross computing obtains whether greater than zero, if greater than 0, then the initial boundary summit is point 1, point 2, point 3 by ordering counterclockwise, otherwise then the initial boundary summit is point 1, point 3, point 2 by ordering counterclockwise.
Find the method for the visible starting point on initial boundary summit to be in the described step (6):
(1) the planar cloud data is increased three-dimensional data, wherein the value of third dimension data is 0, and the vector on 4 to three initial boundary summits of calculation level is designated as
(2) with vector
With
Carry out the multiplication cross computing, judge that whether the third dimension value of the vector that the multiplication cross computing obtains is more than or equal to zero, if more than or equal to 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
The multiplication cross computing obtains the third dimension value of vector whether more than or equal to zero, if more than or equal to 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
Whether the third dimension value of the vector that the multiplication cross computing obtains works as vector more than or equal to zero
With
The third dimension value of the vector that the multiplication cross computing obtains is less than zero, and then the 3rd the visible starting point that the initial boundary summit is the initial boundary summit worked as vector
With
The multiplication cross computing obtains the third dimension value of vector less than zero, then second visible starting point that the initial boundary summit is the initial boundary summit; If
With
The vector that carries out obtaining after the multiplication cross computing is less than 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
The multiplication cross computing obtains the third dimension value of vector whether more than or equal to zero, if more than or equal to 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
Whether the third dimension value of the vector that the multiplication cross computing obtains works as vector more than or equal to zero
With
The third dimension value of the vector that the multiplication cross computing obtains is less than zero, and then the 3rd the visible starting point that the initial boundary summit is the initial boundary summit worked as vector
With
The third dimension value of the vector that the multiplication cross computing obtains is less than zero, and then first initial boundary summit is the visible starting point on initial boundary summit.
Find the method for the visible destination node on initial boundary summit to be in the described step (6):
(1) the planar cloud data is increased three-dimensional data, wherein the value of third dimension data is 0, and the vector on 4 to three initial boundary summits of calculation level is designated as
(2) with vector
With
Carry out the multiplication cross computing, judge vector
With
Whether the vector third dimension value that the multiplication cross computing obtains is smaller or equal to zero, if smaller or equal to 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
The multiplication cross computing obtains the third dimension value of vector whether smaller or equal to zero, if smaller or equal to 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
Whether the third dimension value of the vector that the multiplication cross computing obtains is smaller or equal to zero, if vector
With
The third dimension value of the vector that the multiplication cross computing obtains is greater than zero, and then first initial boundary summit is the visible destination node on initial boundary summit, if vector
With
The multiplication cross computing obtains the third dimension value of vector greater than zero, then second visible destination node that the initial boundary summit is the initial boundary summit; If
With
The vector that carries out obtaining after the multiplication cross computing is greater than 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
Whether the third dimension value of the vector that the multiplication cross computing obtains is smaller or equal to zero, if smaller or equal to 0, then with vector
With
Carry out the multiplication cross computing, if vector
With
The multiplication cross computing obtains the third dimension value of vector greater than zero, and then first initial boundary summit is the visible destination node on initial boundary summit, if vector
With
The third dimension value of the vector that the multiplication cross computing obtains is greater than zero, then the 3rd the visible destination node that the initial boundary summit is the initial boundary summit.
The method that forms new border vertices in the described step (6) is:
(1) visible starting point and the visible destination node that obtains according to step (6) connects insertion point and visible starting point to each border vertices between the visible destination node;
(2) if the ordering sequence number of visible starting point greater than the ordering sequence number of visible destination node, then comprises new border vertices from visible destination node to all border vertices the visible starting point and new insertion point that the order of border vertices is counterclockwise; If the ordering sequence number of visible starting point is less than the ordering sequence number of visible destination node, then new border vertices comprises from visible destination node to all border vertices, former border vertices the former border vertices last point at first to all border vertices the visible starting point and new insertion point, and the order of border vertices is counterclockwise.
In the described step (6) initial delta being carried out method for updating is: if the ordering sequence number of visible starting point greater than the ordering sequence number of visible destination node, then Xin Zeng triangle be the insertion point with from the limit of visible starting point to all limits that former border vertices last point forms, from former border vertices last point to first of former border vertices, from first triangle to all limits compositions of visible destination node of former border vertices; If the ordering sequence number of visible starting point is less than the ordering sequence number of visible destination node, then Xin Zeng triangle is insertion point and the triangle of forming to all limits of visible destination node formation from visible starting point.
The method that in the described step (8) the plane trigonometry net is optimized processing is:
(1) all triangles in the traversal plane trigonometry net, calculate leg-of-mutton three angle sizes, if the angular dimension at one of them angle is greater than 90 °, the opposite side of just finding out this angle from the plane trigonometry net is as on one side triangle wherein, the angular dimension at leg-of-mutton this diagonal angle, limit that calculating is found out, with last two angle value additions, if greater than 180 °, then be optimized, the quadrilateral that two original triangles are formed cuts from another diagonal angle, become two new triangles, originally two triangles are replaced.
(2) all triangles in the traversal plane trigonometry net, the length of side realizes optimization to the plane trigonometry net greater than all triangles of setting threshold in the deletion plane trigonometry net.
In the described step (8) the plane trigonometry net being returned three-dimensional method from two-dimensional map is: the third dimension cloud data that step (1) is removed adds the 3 D stereo triangulation network trrellis diagram that obtains the 3 D stereo cloud data the plane trigonometry net after the optimization to.
The present invention's advantage compared with prior art is as follows: the present invention at first obtains to belong to conplane 3 D stereo cloud data from the 3 D stereo cloud data that the scanning of three-dimensional laser imaging system obtains, conplane 3 D stereo cloud data is projected to formation planar cloud data on the two dimensional surface, calculate the virtual center point of planar cloud data, then by comparing the distance of each data point and virtual center point, will be apart from the real center point of the nearest point of virtual center point as scattered data being, again by comparing the distance of each discrete point and real center point, according to from small to large rule of distance with two-dimentional cloud data presort, find initial delta and initial boundary summit, sorted by counterclockwise in the initial boundary summit again, insert each two dimensional surface cloud data successively according to the order that has sequenced, go forward side by side row bound vertex update and triangle upgrade.The plane cloud data that the plane trigonometry net is optimized after the processing shines upon back three-dimensional coordinate, so just can realize that three-dimensional laser imaging system plane cloud data trigonometric ratio handles, thereby generate scanned THREE DIMENSIONAL TRIANGULATION NET lattice model.The present invention can be tied the three-dimensional laser point cloud data point that the three-dimensional laser imaging system generates by certain rule, this method is compared existing method, and to have thinking simple and clear, the advantage that programming realizes easily, the plane that can handle complex surface situations such as hole or concave surface.
Description of drawings
Fig. 1 is realization flow figure of the present invention;
Fig. 2 seeks the synoptic diagram of virtual center point for the present invention;
Fig. 3 is for determining the synoptic diagram on initial delta and initial boundary summit;
Fig. 4 looks for visible starting point and visible destination node synoptic diagram for inserting at the 4th;
Fig. 5 looks for visible starting point and visible destination node synoptic diagram for inserting at the 5th;
Fig. 6 is the treatment effect figure behind the cloud data trigonometric ratio of plane;
Fig. 7 is the treatment effect figure after the plane trigonometry network optimizationization;
Fig. 8 is the final 3 D stereo triangulation network trrellis diagram that obtains.
Embodiment
Realization principle of the present invention is: at first obtain to belong to conplane 3 D stereo cloud data from the 3 D stereo cloud data that the scanning of three-dimensional laser imaging system obtains, conplane 3 D stereo cloud data is projected to formation planar cloud data on the two dimensional surface, calculate the virtual center point of planar cloud data, then by comparing the distance of each data point and virtual center point, will be apart from the real center point of the nearest point of virtual center point as scattered data being, again by comparing the distance of each discrete point and real center point, according to from small to large rule of distance with two-dimentional cloud data presort, find initial delta and initial boundary summit, sorted by counterclockwise in the initial boundary summit again.Insert each two dimensional surface cloud data successively according to the order that has sequenced, go forward side by side row bound vertex update and triangle upgrade.The plane cloud data that the plane trigonometry net is optimized after the processing shines upon back three-dimensional coordinate, so just can realize that three-dimensional laser imaging system plane cloud data trigonometric ratio handles, thereby generate scanned THREE DIMENSIONAL TRIANGULATION NET lattice model.
Introduce this three-dimensional laser imaging system plane cloud data trigonometric ratio disposal route below in conjunction with Fig. 1, step is as follows:
A kind of three-dimensional laser imaging system plane cloud data trigonometric ratio disposal route, step is as follows:
(1) from the 3 D stereo cloud data that the scanning of three-dimensional laser imaging system obtains, obtains to belong to conplane 3 D stereo cloud data, conplane 3 D stereo cloud data is projected to form the planar cloud data on the two dimensional surface;
(2) virtual center point of calculation procedure (1) planar cloud data;
(3) ask for distance between all planar cloud datas and its virtual center point, find out all planar cloud data middle distance virtual center point apart from the planar cloud data of minimum, it as the real center point, and is designated as a little 1;
(4) ask for distance between the real center point that finds in all planar cloud datas and the step (3), according to real center point distance order from small to large the planar cloud data being sorted, the result of ordering is designated as a little 1 successively, point 2, point 3, put 4 ... point N;
(5) utilize first three the planar cloud data through sorting after handling to surround an initial delta as the summit, three summits of initial delta are as the initial boundary summit, and definite initial boundary zenith directions is for be that border vertices rises and presses counter clockwise direction and sort to put 1;
(6) visible starting point and the visible destination node on initial boundary summit found to the vector on initial boundary summit in insertion point 4 on the basis of step (5), calculation level 4, forms new border vertices, then initial delta upgraded;
(7) according to the method for step (6) insertion point 5 successively, point 6 ... point N, calculate the insertion point respectively to the vector between the border vertices that forms before, the visible starting point of the border vertices that forms before finding and visible destination node finally form a plane trigonometry net;
(8) the plane trigonometry net is optimized processing;
(9) will return three-dimensional from two-dimensional map through the plane trigonometry net after step (8) processing, finally obtain the 3 D stereo triangulation network trrellis diagram of 3 D stereo cloud data, and realize the trigonometric ratio of three-dimensional laser imaging system plane cloud data is handled.
The implementation method of step (1) is:
(1) selects according to the three-dimensional coordinate of 3 D stereo cloud data and belong to conplane 3 D stereo cloud data and preserve;
(2) with the 3 D stereo cloud data preserved or be rotated to remove or directly remove third dimension cloud data and obtain the planar cloud data, and the third dimension cloud data after will removing is preserved.
The implementation method of step (2) is:
(1) finds the minimum horizontal ordinate x of cloud data in all planar cloud datas
Min, maximum horizontal ordinate x
Max, minimum ordinate y
Min, maximum ordinate y
Max, according to minimum horizontal ordinate x
Min, maximum horizontal ordinate x
Max, minimum ordinate y
Min, maximum ordinate y
MaxObtain comprising the minimum rectangle of all planar cloud datas, work: A(x is remembered on four summits of rectangle respectively
Min, y
Min), B(x
Max, y
Min), C(x
Max, y
Max), D(x
Min, y
Max);
(2) ask for the central point E of rectangle, the coordinate of some E is
Point E is the virtual center point of planar cloud data.
As shown in Figure 2, the stain among the figure is represented all planar cloud datas, and the black quadrilateral represents to comprise the minimum rectangle of all planar cloud datas, and E represents virtual center point.The sequence number point 1 of planar cloud data, point 2, point 3, point 4 ... it is the planar cloud data sequence order after the ordering.
The initial boundary zenith directions is for be that border vertices rises and presses the method that counter clockwise direction sorts and be to put 1 in the step (5):
(1) the planar cloud data is increased three-dimensional data, wherein the value of third dimension data is 0;
(2) try to achieve a little 1 vector to point 2
With the vector of point 1 to point 3
(3) to vector
With
Carry out the multiplication cross computing, judge the third dimension value of the vector that the multiplication cross computing obtains whether greater than zero, if greater than 0, then the initial boundary summit is point 1, point 2, point 3 by ordering counterclockwise, otherwise then the initial boundary summit is point 1, point 3, point 2 by ordering counterclockwise.
As shown in Figure 3, try to achieve a little 1 vector to point 2
With the vector of point 1 to point 3
To vector
With
Carry out the multiplication cross computing, the third dimension value of the vector that the multiplication cross computing obtains is greater than zero, so the initial boundary summit is point 1, point 2, point 3 by sorting counterclockwise.
Find the method for the visible starting point on initial boundary summit to be in the described step (6):
(1) the planar cloud data is increased three-dimensional data, wherein the value of third dimension data is 0, and the vector on 4 to three initial boundary summits of calculation level is designated as
(2) with vector
With
Carry out the multiplication cross computing, judge that whether the third dimension value of the vector that the multiplication cross computing obtains is more than or equal to zero, if more than or equal to 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
The multiplication cross computing obtains the third dimension value of vector whether more than or equal to zero, if more than or equal to 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
Whether the third dimension value of the vector that the multiplication cross computing obtains works as vector more than or equal to zero
With
The third dimension value of the vector that the multiplication cross computing obtains is less than zero, and then the 3rd the visible starting point that the initial boundary summit is the initial boundary summit worked as vector
With
The multiplication cross computing obtains the third dimension value of vector less than zero, then second visible starting point that the initial boundary summit is the initial boundary summit; If
With
The vector that carries out obtaining after the multiplication cross computing is less than 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
The multiplication cross computing obtains the third dimension value of vector whether more than or equal to zero, if more than or equal to 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
Whether the third dimension value of the vector that the multiplication cross computing obtains works as vector more than or equal to zero
With
The third dimension value of the vector that the multiplication cross computing obtains is less than zero, and then the 3rd the visible starting point that the initial boundary summit is the initial boundary summit worked as vector
With
The third dimension value of the vector that the multiplication cross computing obtains is less than zero, and then first initial boundary summit is the visible starting point on initial boundary summit.
Shown in Fig. 4,5, the initial boundary summit is point 1, point 2, point 3 among Fig. 4, and new insertion point 4 increases three-dimensional data with the planar cloud data, and wherein the value of third dimension data is 0, and the vector on 4 to three initial boundary summits of calculation level is designated as
With vector
With
Carry out the multiplication cross computing, the third dimension value of the vector that the multiplication cross computing obtains is greater than zero, with vector
With
Carry out the multiplication cross computing, the third dimension value of the vector that the multiplication cross computing obtains is greater than zero, with vector
With
Carry out the multiplication cross computing, the third dimension value of the vector that the multiplication cross computing obtains is less than zero, and the 3rd initial boundary summit is the visible starting point on initial boundary summit; Border vertices is point 1, point 2, point 3, point 4 among Fig. 5, and new insertion point 5 increases three-dimensional data with the planar cloud data, and wherein the value of third dimension data is 0, and the vector on 5 to three initial boundary summits of calculation level is designated as
With vector
With
Carry out the multiplication cross computing, the third dimension value of the vector that the multiplication cross computing obtains is less than zero, with vector
With
Carry out the multiplication cross computing, the third dimension value of the vector that the multiplication cross computing obtains is more than or equal to zero, with vector
With
Carry out the multiplication cross computing, the third dimension value of the vector that the multiplication cross computing obtains is less than zero, then the 4th the visible starting point that the initial boundary summit is the initial boundary summit.
Find the method for the visible destination node on initial boundary summit to be in the described step (6):
(1) the planar cloud data is increased three-dimensional data, wherein the value of third dimension data is 0, and the vector on 4 to three initial boundary summits of calculation level is designated as
(2) with vector
With
Carry out the multiplication cross computing, judge vector
With
Whether the vector third dimension value that the multiplication cross computing obtains is smaller or equal to zero, if smaller or equal to 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
The multiplication cross computing obtains the third dimension value of vector whether smaller or equal to zero, if smaller or equal to 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
Whether the third dimension value of the vector that the multiplication cross computing obtains is smaller or equal to zero, if vector
With
The third dimension value of the vector that the multiplication cross computing obtains is greater than zero, and then first initial boundary summit is the visible destination node on initial boundary summit, if vector
With
The multiplication cross computing obtains the third dimension value of vector greater than zero, then second visible destination node that the initial boundary summit is the initial boundary summit; If
With
The vector that carries out obtaining after the multiplication cross computing is greater than 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
Whether the third dimension value of the vector that the multiplication cross computing obtains is smaller or equal to zero, if smaller or equal to 0, then with vector
With
Carry out the multiplication cross computing, if vector
With
The multiplication cross computing obtains the third dimension value of vector greater than zero, and then first initial boundary summit is the visible destination node on initial boundary summit, if vector
With
The third dimension value of the vector that the multiplication cross computing obtains is greater than zero, then the 3rd the visible destination node that the initial boundary summit is the initial boundary summit.
Shown in Fig. 4,5, among Fig. 4 the planar cloud data is increased three-dimensional data, wherein the value of third dimension data is 0, the vector on 4 to three initial boundary summits of calculation level is designated as
Vector
With
Carry out the multiplication cross computing, the third dimension value of the vector that the multiplication cross computing obtains is less than zero, with vector
With
Carry out the multiplication cross computing, the third dimension value of the vector that the multiplication cross computing obtains is less than zero, with vector
With
Carry out the multiplication cross computing, the third dimension value of the vector that the multiplication cross computing obtains is greater than zero, and then first initial boundary summit is the visible destination node on initial boundary summit.Fig. 5 midplane two dimension cloud data increases three-dimensional data, and wherein the value of third dimension data is 0, and the vector on 5 to three initial boundary summits of calculation level is designated as
Vector
With
Carry out the multiplication cross computing, the third dimension value of the vector that the multiplication cross computing obtains is less than zero, with vector
With
Carry out the multiplication cross computing, the third dimension value of the vector that the multiplication cross computing obtains is less than zero, with vector
With
Carry out the multiplication cross computing, the third dimension value of the vector that the multiplication cross computing obtains is greater than zero, then second visible destination node that the initial boundary summit is the initial boundary summit.
The method that forms new border vertices in the described step (6) is:
(1) visible starting point and the visible destination node that obtains according to step (6) connects insertion point and visible starting point to each border vertices between the visible destination node;
(2) if the ordering sequence number of visible starting point greater than the ordering sequence number of visible destination node, then comprises new border vertices from visible destination node to all the former border vertices the visible starting point and new insertion point that the order of border vertices is counterclockwise; If the ordering sequence number of visible starting point is less than the ordering sequence number of visible destination node, then new border vertices comprises from visible destination node to all border vertices, former border vertices the former border vertices last point at first to all border vertices the visible starting point and new insertion point, and the order of border vertices is counterclockwise.
Shown in Fig. 4,5, Fig. 4 Central Plains border vertices is point 1, point 2, point 3, as seen the ordering sequence number of starting point is three, as seen the ordering sequence number of destination node is one, so the ordering sequence number of visible starting point is greater than the ordering sequence number of visible destination node, then new border vertices is point 1, point 2, point 3, new insertion point 4.Fig. 5 Central Plains border vertices is the border vertices behind the insertion point 4, as seen the ordering sequence number of starting point is four, as seen the ordering sequence number of destination node is two, so the ordering sequence number of visible starting point is greater than the ordering sequence number of visible destination node, then new border vertices is point 2, point 3, point 4, new insertion point 5.
In the step (6) initial delta being carried out method for updating is: if the ordering sequence number of visible starting point greater than the ordering sequence number of visible destination node, then Xin Zeng triangle be the insertion point with from the limit of visible starting point to all limits that former border vertices last point forms, from former border vertices last point to first of former border vertices, from first triangle to all limits compositions of visible destination node of former border vertices; If the ordering sequence number of visible starting point is less than the ordering sequence number of visible destination node, then Xin Zeng triangle is insertion point and the triangle of forming to all limits of visible destination node formation from visible starting point.
Describe in conjunction with Fig. 4, Fig. 5, because the ordering sequence number of visible starting point is greater than the ordering sequence number of visible destination node among Fig. 4, then Xin Zeng vertex of a triangle is (point 3, point 1, point 4); Because the ordering sequence number of visible starting point is greater than the ordering sequence number of visible destination node among Fig. 5, then Xin Zeng vertex of a triangle is (point 4, point 1, point 5), (point 1, point 2, point 5).
Shown in Fig. 6,7, the method that in the step (8) the plane trigonometry net is optimized processing is:
(1) all triangles in the traversal plane trigonometry net, calculate leg-of-mutton three angle sizes, if the angular dimension at one of them angle is greater than 90 °, the opposite side of just finding out this angle from the plane trigonometry net is as on one side triangle wherein, the angular dimension at leg-of-mutton this diagonal angle, limit that calculating is found out, with last two angle value additions, if greater than 180 °, then be optimized, the quadrilateral that two original triangles are formed cuts from another diagonal angle, become two new triangles, originally two triangles are replaced.
(2) all triangles in the traversal plane trigonometry net, the length of side realizes optimization to the plane trigonometry net greater than all triangles of setting threshold in the deletion plane trigonometry net.
In conjunction with Fig. 6,7 as can be known, the threshold values size of setting can be the three-to-four-fold of spacing distance size between the planar cloud data, and plane triangle is optimized, and removes triangle in the concave surface.Fig. 6 does not also have optimization process design sketch before, and Fig. 7 is the design sketch after the optimization process.
In the step (8) the plane trigonometry net being returned three-dimensional method from two-dimensional map is: the third dimension cloud data that step (1) is removed adds the 3 D stereo triangulation network trrellis diagram that obtains the 3 D stereo cloud data the plane trigonometry net after the optimization to, as shown in Figure 8.
The unspecified content of the present invention is technology as well known to those skilled in the art.
Claims (10)
1. three-dimensional laser imaging system plane cloud data trigonometric ratio disposal route is characterized in that step is as follows:
(1) from the 3 D stereo cloud data that the scanning of three-dimensional laser imaging system obtains, obtains to belong to conplane 3 D stereo cloud data, conplane 3 D stereo cloud data is projected to form the planar cloud data on the two dimensional surface;
(2) virtual center point of calculation procedure (1) planar cloud data;
(3) ask for distance between all planar cloud datas and its virtual center point, find out all planar cloud data middle distance virtual center point apart from the planar cloud data of minimum, it as the real center point, and is designated as a little 1;
(4) ask for distance between the real center point that finds in all planar cloud datas and the step (3), according to real center point distance order from small to large the planar cloud data being sorted, the result of ordering is designated as a little 1 successively, point 2, point 3, put 4 ... point N;
(5) utilize first three the planar cloud data through sorting after handling to surround an initial delta as the summit, three summits of initial delta are as the initial boundary summit, and definite initial boundary zenith directions is for be that border vertices rises and presses counter clockwise direction and sort to put 1;
(6) visible starting point and the visible destination node on initial boundary summit found to the vector on initial boundary summit in insertion point 4 on the basis of step (5), calculation level 4, forms new border vertices, then initial delta upgraded;
(7) according to the method for step (6) insertion point 5 successively, point 6 ... point N, calculate the insertion point respectively to the vector between the border vertices that forms before, the visible starting point of the border vertices that forms before finding and visible destination node finally form a plane trigonometry net;
(8) the plane trigonometry net is optimized processing;
(9) will return three-dimensional from two-dimensional map through the plane trigonometry net after step (8) processing, finally obtain the 3 D stereo triangulation network trrellis diagram of 3 D stereo cloud data, and realize the trigonometric ratio of three-dimensional laser imaging system plane cloud data is handled.
2. a kind of three-dimensional laser imaging system according to claim 1 plane cloud data trigonometric ratio disposal route, it is characterized in that: the implementation method of described step (1) is:
(1) selects according to the three-dimensional coordinate of 3 D stereo cloud data and belong to conplane 3 D stereo cloud data and preserve;
(2) with the 3 D stereo cloud data preserved or be rotated to remove or directly remove third dimension cloud data and obtain the planar cloud data, and the third dimension cloud data after will removing is preserved.
3. a kind of three-dimensional laser imaging system according to claim 1 plane cloud data trigonometric ratio disposal route, it is characterized in that: the implementation method of described step (2) is:
(1) finds the minimum horizontal ordinate x of cloud data in all planar cloud datas
Min, maximum horizontal ordinate x
Max, minimum ordinate y
Min, maximum ordinate y
Max, according to minimum horizontal ordinate x
Min, maximum horizontal ordinate x
Max, minimum ordinate y
Min, maximum ordinate y
MaxObtain comprising the minimum rectangle of all planar cloud datas, work: A(x is remembered on four summits of rectangle respectively
Min, y
Min), B(x
Max, y
Min), C(x
Max, y
Max), D(x
Min, y
Max);
(2) ask for the central point E of rectangle, the coordinate of some E is
Point E is the virtual center point of planar cloud data.
4. a kind of three-dimensional laser imaging system according to claim 1 plane cloud data trigonometric ratio disposal route is characterized in that: the initial boundary zenith directions is for be that border vertices rises and presses the method that counter clockwise direction sorts and be to put 1 in the described step (5):
(1) the planar cloud data is increased three-dimensional data, wherein the value of third dimension data is 0;
(2) try to achieve a little 1 vector to point 2
With the vector of point 1 to point 3
(3) to vector
With
Carry out the multiplication cross computing, judge the third dimension value of the vector that the multiplication cross computing obtains whether greater than zero, if greater than 0, then the initial boundary summit is point 1, point 2, point 3 by ordering counterclockwise, otherwise then the initial boundary summit is point 1, point 3, point 2 by ordering counterclockwise.
5. a kind of three-dimensional laser imaging system according to claim 1 plane cloud data trigonometric ratio disposal route is characterized in that: find the method for the visible starting point on initial boundary summit to be in the described step (6):
(1) the planar cloud data is increased three-dimensional data, wherein the value of third dimension data is 0, and the vector on 4 to three initial boundary summits of calculation level is designated as
(2) with vector
With
Carry out the multiplication cross computing, judge that whether the third dimension value of the vector that the multiplication cross computing obtains is more than or equal to zero, if more than or equal to 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
The multiplication cross computing obtains the third dimension value of vector whether more than or equal to zero, if more than or equal to 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
Whether the third dimension value of the vector that the multiplication cross computing obtains works as vector more than or equal to zero
With
The third dimension value of the vector that the multiplication cross computing obtains is less than zero, and then the 3rd the visible starting point that the initial boundary summit is the initial boundary summit worked as vector
With
The multiplication cross computing obtains the third dimension value of vector less than zero, then second visible starting point that the initial boundary summit is the initial boundary summit;
If
With
The vector that carries out obtaining after the multiplication cross computing is less than 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
The multiplication cross computing obtains the third dimension value of vector whether more than or equal to zero, if more than or equal to 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
Whether the third dimension value of the vector that the multiplication cross computing obtains works as vector more than or equal to zero
With
The third dimension value of the vector that the multiplication cross computing obtains is less than zero, and then the 3rd the visible starting point that the initial boundary summit is the initial boundary summit worked as vector
With
The third dimension value of the vector that the multiplication cross computing obtains is less than zero, and then first initial boundary summit is the visible starting point on initial boundary summit.
6. a kind of three-dimensional laser imaging system according to claim 1 plane cloud data trigonometric ratio disposal route is characterized in that: find the method for the visible destination node on initial boundary summit to be in the described step (6):
(1) the planar cloud data is increased three-dimensional data, wherein the value of third dimension data is 0, and the vector on 4 to three initial boundary summits of calculation level is designated as
(2) with vector
With
Carry out the multiplication cross computing, judge vector
With
Whether the vector third dimension value that the multiplication cross computing obtains is smaller or equal to zero, if smaller or equal to 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
The multiplication cross computing obtains the third dimension value of vector whether smaller or equal to zero, if smaller or equal to 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
Whether the third dimension value of the vector that the multiplication cross computing obtains is smaller or equal to zero, if vector
With
The third dimension value of the vector that the multiplication cross computing obtains is greater than zero, and then first initial boundary summit is the visible destination node on initial boundary summit, if vector
With
The multiplication cross computing obtains the third dimension value of vector greater than zero, then second visible destination node that the initial boundary summit is the initial boundary summit;
If
With
The vector that carries out obtaining after the multiplication cross computing is greater than 0, then with vector
With
Carry out the multiplication cross computing, judge vector
With
Whether the third dimension value of the vector that the multiplication cross computing obtains is smaller or equal to zero, if smaller or equal to 0, then with vector
With
Carry out the multiplication cross computing, if vector
With
The multiplication cross computing obtains the third dimension value of vector greater than zero, and then first initial boundary summit is the visible destination node on initial boundary summit, if vector
With
The third dimension value of the vector that the multiplication cross computing obtains is greater than zero, then the 3rd the visible destination node that the initial boundary summit is the initial boundary summit.
7. according to claim 1,5 or 6 described a kind of three-dimensional laser imaging system plane cloud data trigonometric ratio disposal routes, it is characterized in that: the method that forms new border vertices in the described step (6) is:
(1) visible starting point and the visible destination node that obtains according to step (6) connects insertion point and visible starting point to each border vertices between the visible destination node;
(2) if the ordering sequence number of visible starting point greater than the ordering sequence number of visible destination node, then comprises new border vertices from visible destination node to all border vertices the visible starting point and new insertion point that the order of border vertices is counterclockwise; If the ordering sequence number of visible starting point is less than the ordering sequence number of visible destination node, then new border vertices comprises from visible destination node to all border vertices, former border vertices the former border vertices last point at first to all border vertices the visible starting point and new insertion point, and the order of border vertices is counterclockwise.
8. a kind of three-dimensional laser imaging system according to claim 7 plane cloud data trigonometric ratio disposal route, it is characterized in that: in the described step (6) initial delta being carried out method for updating is: if the ordering sequence number of visible starting point greater than the ordering sequence number of visible destination node, then Xin Zeng triangle be the insertion point with from the limit of visible starting point to all limits that former border vertices last point forms, from former border vertices last point to first of former border vertices, from first triangle to all limits compositions of visible destination node of former border vertices; If the ordering sequence number of visible starting point is less than the ordering sequence number of visible destination node, then Xin Zeng triangle is insertion point and the triangle of forming to all limits of visible destination node formation from visible starting point.
9. a kind of three-dimensional laser imaging system according to claim 1 plane cloud data trigonometric ratio disposal route is characterized in that: the method that in the described step (8) the plane trigonometry net is optimized processing is:
(1) all triangles in the traversal plane trigonometry net, calculate leg-of-mutton three angle sizes, if the angular dimension at one of them angle is greater than 90 °, the opposite side of just finding out this angle from the plane trigonometry net is as on one side triangle wherein, the angular dimension at leg-of-mutton this diagonal angle, limit that calculating is found out, with last two angle value additions, if greater than 180 °, then be optimized, the quadrilateral that two original triangles are formed cuts from another diagonal angle, become two new triangles, originally two triangles are replaced.
(2) all triangles in the traversal plane trigonometry net, the length of side realizes optimization to the plane trigonometry net greater than all triangles of setting threshold in the deletion plane trigonometry net.
10. a kind of three-dimensional laser imaging system according to claim 2 plane cloud data trigonometric ratio disposal route, in the described step (8) the plane trigonometry net is returned three-dimensional method from two-dimensional map and be: the third dimension cloud data that step (1) is removed adds the 3 D stereo triangulation network trrellis diagram that obtains the 3 D stereo cloud data the plane trigonometry net after the optimization to.
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