CN112949686B - Matching method based on optimal local distance - Google Patents

Matching method based on optimal local distance Download PDF

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CN112949686B
CN112949686B CN202110126685.4A CN202110126685A CN112949686B CN 112949686 B CN112949686 B CN 112949686B CN 202110126685 A CN202110126685 A CN 202110126685A CN 112949686 B CN112949686 B CN 112949686B
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范敏
张志娟
张跃
田磊
刘子辉
粱辉
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China National Heavy Duty Truck Group Jinan Power Co Ltd
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Abstract

The invention relates to a matching method based on optimal local distance, which is used for researching the matching problem in the multi-source information fusion technology of an unmanned vehicle based on a bipartite graph model and dividing an object to be matched into a set X and a set Y. Preferentially selecting the element X with the highest element matching degree in the set X and the set Y i 、Y j Forming an optimal matching pair; and then, forming a new bipartite graph by the remaining elements, and matching according to the optimal local distance between the elements. The invention considers the influence of the distance between elements on the matching result, and ensures the matching quality and the matching quantity.

Description

Matching method based on optimal local distance
Technical Field
The invention belongs to the technical field of unmanned driving, relates to a matching method in a multivariate information fusion technology, and particularly relates to a matching method based on an optimal local distance.
Background
The rapid development of science and technology makes the unmanned technology mature day by day. The key technologies of the unmanned automobile comprise environment perception, navigation positioning, decision planning, control and the like. Environmental sensing is usually accomplished by a combination of multiple sensors, subject to the performance of the sensors themselves. The sensors are independent in the observation process, and are particularly complex when a plurality of detection targets exist, and the system is difficult to spontaneously determine the matching relationship among the targets detected by the sensors, so that a matching method in the multi-source information fusion technology needs to be introduced.
The matching method commonly used at present is the Hungarian algorithm, which is the most common bipartite graph matching method and is based on the idea proved by sufficiency in Hall's theorem, and the core is to search an augmented path and use the augmented path to ask the bipartite graph for maximum matching. The Hungarian algorithm preferentially selects matching objects according to the ranking order and determines as many matching pairs as possible, but the difference degree between each matching pair is not fully considered, and the situation that the optimal matching is abandoned for acquiring the maximum matching number may exist, namely the final result is the maximum matching and is not the optimal matching.
In view of this, it is very necessary to provide a matching method based on optimal local distance to solve the defects in the prior art.
Disclosure of Invention
Aiming at the problem that the difference degree between each matching pair is not fully considered in the existing method, and the final result is probably the most matching rather than the optimal matching, the invention provides a matching method based on the optimal local distance, which ensures the matching quality and the matching quantity.
In order to achieve the purpose, the invention provides the following technical scheme:
a matching method based on optimal local distance utilizes a bipartite graph model to research the matching problem in the multi-source information fusion technology of an unmanned vehicle, and comprises the following steps:
s1: let the bipartite graph have a set X and a set Y, the number of internal elements is m and n, respectively, and any element in the set X is represented as X i Any element in the set Y is represented as Y j Wherein i is more than or equal to 1 and less than or equal to m, and j is more than or equal to 1 and less than or equal to n;
s2: calculating the difference S between m elements in the set X and n elements in the set Y by adopting a distance calculation method i_j ；
S3: according to the degree of difference S i_j Building X i And Y j The connection relation between the two;
s4: preferentially selecting the element with the highest element matching degree in the set X and the set YPrime X i 、Y j Establishing an optimal matching pair;
s5: the rest of X i 、Y j Forming new sets A and B, and respectively representing the internal elements thereof as A i 、B j ；
S6：A i Sequentially selecting one B with optimal local distance j Matching and selected B j No longer participate in matching;
s7: original X i And (4) whether the traversal judgment is finished or not, if so, outputting all matching results, and otherwise, sequentially continuing to perform the matching judgment.
Preferably, the set X represents a detection target of the laser radar, and the set Y represents a detection target of the camera; the set X and the set Y in the bipartite graph respectively represent a detection target of the laser radar and a detection target of the camera, and measurement-measurement data matching in multi-source information fusion of the unmanned vehicle is achieved.
Preferably, the distance calculation method in step S2 adopts the following euclidean distance calculation process, where the element in the set X is X i (x 1 ,y 1 ) The element in the set Y is Y j (x 2 ，y 2 ) Calculating X i And Y j Degree of difference therebetween
Figure BDA0002923747820000031
Preferably, the distance calculation method in step S2 adopts a manhattan distance calculation process, where an element in the set X is X i (x 1 ,y 1 ) The elements in the set Y are Y j (x 2 ，y 2 ) Calculating X i And Y j Degree of difference S between i_j ＝|x 1 -x 2 |+|y 1 -y 2 |；
Preferably, the degree of difference S is calculated in the step S2 i_j Later difference degree S i_j Arranged in a small-to-large manner; the difference degree S i_j Arranging the elements in a small-to-large manner facilitates subsequent selection of the element with the least degree of difference for matching.
Preferably, X is established in the step S3 i And Y j The specific process of the connection relationship is as follows:
the first step is as follows: setting a threshold value T, wherein T is greater than 0;
the second step is that: if the difference is S i_j >T, then S ij If 0 is not less than 1, the difference S i_j T is less than or equal to T, then S ij ＝S i_j ；
The third step: according to S ij The size establishes a connection relation if S ij <0, then X i And Y j If S has no connecting edge ij Greater than or equal to 0, then X i And Y j With connecting edges in between.
Establishing connection relation by comparing difference degrees, wherein X with larger difference degree i And Y j Cannot be connected with each other, and has small difference degree of X i And Y j And a connection relation is established between the two modules, so that the matching quality is ensured.
Preferably, the process of establishing the optimal matching pair in step S4 includes the following steps:
is X i Finding matching element and finding matching element Y from set Y j Matching element Y j Should satisfy the following relation with X i Degree of difference S of i_j Minimum, Y satisfying the above requirements j There are 0 or more;
is Y j Finding matching element and finding matching element X from set X i Matching element X i Should satisfy the relation with Y j Degree of difference S of i_j Minimum, X satisfying the above requirements i There are 0 or more;
selecting the optimal matching pair, if some X exists i And Y j Matching each other, and when both have one and only one matching element, the two are determined as the optimal matching pair;
by preferentially selecting the element X with the highest element matching degree in the set X and the set Y i 、Y j The optimal matching pair is established, so that the matching quality can be greatly improved.
Preferably, A in the step S6 i Sequentially selecting one B with optimal local distance j In the process of matching A i Selecting the degree of difference S i_j Smallest one B j Matching is carried out; and selecting the element with the optimal local distance and the minimum difference degree for matching, thereby effectively ensuring the matching quality.
The method has the beneficial effect that the matching problem in the multi-source information fusion technology is researched based on the bipartite graph model. In the matching process, the element with the highest element matching degree in the two sets is preferentially selected for matching, and the method has the advantage that the element can be prevented from occupying the matching elements of other elements.
Therefore, compared with the prior art, the invention has prominent substantive features and remarkable progress, and the beneficial effects of the implementation are also obvious.
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In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a flowchart of a matching method based on an optimal local distance according to embodiment 1 of the present invention.
FIG. 2 shows X in example 2 of the present invention i And Y j The connection relationship diagram of (1).
FIG. 3 shows X in example 2 of the present invention i And Y j Of the matching list.
FIG. 4 shows X in example 2 of the present invention i And Y j The optimal matching connection graph.
FIG. 5 shows A in example 2 of the present invention i And B j The matching connection graph of (1).
FIG. 6 shows X in example 3 of the present invention i And Y j The connection relationship diagram of (1).
FIG. 7 shows X in example 3 of the present invention i And Y j The matching list of (2).
FIG. 8 shows X in example 3 of the present invention i And Y j The optimal matching connection graph.
FIG. 9 shows A in example 3 of the present invention i And B j The matching connection graph of (2).
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
Example 1:
as shown in fig. 1, the embodiment provides a matching method based on an optimal local distance, which utilizes a bipartite graph model to research a matching problem in a multi-source information fusion technology of an unmanned vehicle, and includes the following steps:
s1: let the bipartite graph have a set X and a set Y, the number of internal elements is m and n, respectively, and represent any element in the set X as X i Any element in the set Y is represented as Y j Wherein i is more than or equal to 1 and less than or equal to m, and j is more than or equal to 1 and less than or equal to n;
s2: calculating the difference S between m elements in the set X and n elements in the set Y by adopting a distance calculation method i_j ；
S3: according to the degree of difference S i_j Establishing X i And Y j The connection relation between the two;
s4: preferentially selecting the element X with the highest element matching degree in the set X and the set Y i 、Y j Establishing an optimal matching pair;
s5: the rest of X i 、Y j Forming new sets A and B, and respectively representing the internal elements thereof as A i 、B j ；
S6：A i Sequentially selecting one B with optimal local distance j Matching and selected B j No longer participate in matching;
s7: original X i And (4) whether the traversal judgment is finished or not, if so, outputting all matching results, and otherwise, sequentially continuing to perform the matching judgment.
Example 2:
the embodiment provides a matching method based on an optimal local distance, which utilizes a bipartite graph model to research a matching problem, takes measurement-measurement target matching in multi-source information fusion of an unmanned vehicle as an example, and comprises the following steps:
s1: let a bipartite graph have a set X and a set Y, where the number of elements in the set X is m =10, the number of elements in the set Y is n =12, and any element in the set X is denoted as X i And any element in the set Y is represented as Y j Wherein i is more than or equal to 1 and less than or equal to 10, j is more than or equal to 1 and less than or equal to 12, and the element X in the set X i ＝X 1 、X 2 、X 3 、X 4 、X 5 、X 6 、X 7 、X 8 、X 9 、X 10 Element Y = Y in set Y 1 、Y 2 、Y 3 、Y 4 、Y 5 、Y 6 、Y 7 、Y 8 、Y 9 、Y 10 、Y 11 、Y 12 (ii) a The set X represents a detection target of the laser radar, the set Y represents a detection target of the camera, and measurement-measurement data matching in multi-source information fusion of the unmanned vehicle is achieved.
S2: calculating the difference S between every two 10 elements in the set X and 12 elements in the set Y by adopting a distance calculation method i_j ；
The distance calculation adopts the Euclidean distance calculation mode process as follows: the elements in the set X are X i (x 1 ,y 1 ) The element in the set Y is Y j (x 2 ，y 2 ) Calculating X i And Y j Degree of difference therebetween
Figure BDA0002923747820000071
Calculating the degree of difference S i_j The degree of difference S i_j Arranged in a small-to-large manner; the difference degree S i_j Arranging the elements in a small-to-large manner facilitates subsequent selection of the element with the least difference for matching.
S3: according to the degree of difference S i_j Building X i And Y j The connection relationship between them is shown in fig. 2, and the specific process is as follows:
the first step is as follows: setting a threshold value T, wherein T is greater than 0, and taking T =6;
the second step: if the difference is S i_j >6, then S ij = -1, if 0 ≦ degree of difference S i_j If the ratio is less than or equal to 6, then S ij ＝S i_j ；
The third step: according to S ij The size establishes a connection relation if S ij <0, then X i And Y j If S has no connecting edge ij Greater than or equal to 0, then X i And Y j With connecting edges therebetween.
Establishing connection relation by comparing difference degrees, wherein X with larger difference degree i And Y j Cannot be connected with each other, and has small difference degree of X i And Y j And a connection relation is established between the two modules, so that the matching quality is ensured.
S4: as shown in FIGS. 3 to 4, the element X with the highest local matching degree in the set X and the set Y is preferentially selected i 、Y j Establishing an optimal matching pair, wherein the specific process comprises the following steps:
is X i Finding matching element and finding matching element Y from set Y j Matching element Y j Should satisfy the following relation with X i Degree of difference S of i_j Minimum, Y satisfying the above requirements j There are 0 or more; wherein X 1 Match Y 2 ，X 2 Matching Y 1 、Y 3 ，X 3 Matching Y 4 ，X 4 Matching Y 2 ，X 5 Match Y 10 ，X 6 Match Y 5 ，X 7 Match Y 6 ，X 8 Matching Y 9 ，X 9 Match Y 8 、Y 11 ，X 10 Matching Y 8 、Y 10 ；
Is Y j Finding matching element and finding matching element X from set X i Match element X i Should satisfy the following relation with Y j Degree of difference S of i_j At minimum, X satisfying the above requirements i There are 0 or more; wherein Y is 1 Match X 2 ，Y 2 Match X 4 ，Y 3 Match X 2 、X 4 ，Y 4 Match X 3 ，Y 5 Matching X 6 ，Y 6 Match X 4 ，Y 7 Match X 4 、X 10 ，Y 8 Match X 5 、X 9 、X 10 ，Y 9 Match X 8 ，Y 10 Match X 5 ，Y 11 Match X 9 ，Y 12 Match X 10 ；
Selecting the optimal matching pair, if there is some X i And Y j When the matching elements are matched with each other and only one matching element exists, the two matching elements are determined as the optimal matching pair; wherein X 3 Y 4 、X 4 Y 2 、X 5 Y 10 、X 6 Y 5 、X 8 Y 9 Is an optimal matching pair;
by preferentially selecting the element X with the highest element matching degree in the set X and the set Y i 、Y j The optimal matching pair is established, so that the matching quality can be greatly improved.
S5: the rest of X 1 、X 2 、X 7 、X 9 、X 10 、Y 1 、Y 3 、Y 6 、Y 7 、Y 8 、Y 11 、Y 12 Form a new set A, B, the internal element A of the set A i Is A 1 、A 2 、A 7 、A 9 、A 10 Internal elements B of set B j Is B 1 、B 3 、B 6 、B 7 、B 8 、B 11 、B 12 ；
S6: as shown in FIG. 5, A i Sequentially selecting one B with optimal local distance j Matching and selected B j No longer participate inPreparing; wherein A is i Sequentially selecting one B with optimal local distance j In the process of matching A i Selecting the degree of difference S i_j Smallest one B j Carrying out matching, A 1 Match B 3 ，A 2 Match B 1 ,A 7 Match B 6 ,A 9 Match B 8 ，A 10 Match B 7 (ii) a And selecting the element with the optimal local distance and the minimum difference degree for matching, thereby effectively ensuring the matching quality.
S7: original X i Are all traversed and judged, and all matched pairs X are output 3 Y 4 、X 4 Y 2 、X 5 Y 10 、X 6 Y 5 、X 8 Y 9 ，X 1 Y 3 ，X 2 Y 1 ,X 7 Y 6 ,X 9 Y 8 ，X 10 Y 7 。
Example 3:
the embodiment provides a matching method based on an optimal local distance, which utilizes a bipartite graph model to research a matching problem, takes measurement-measurement target matching in multi-source information fusion of an unmanned vehicle as an example, and comprises the following steps:
s1: let a bipartite graph have a set X and a set Y, where the number of elements in the set X is m =10, the number of elements in the set Y is n =10, and any one element in the set X is represented as X i Any element in the set Y is represented as Y j Wherein i is more than or equal to 1 and less than or equal to 10, j is more than or equal to 1 and less than or equal to 10, and the element X in the set X i ＝X 1 、X 2 、X 3 、X 4 、X 5 、X 6 、X 7 、X 8 、X 9 、X 10 Element Y = Y in set Y 1 、Y 2 、Y 3 、Y 4 、Y 5 、Y 6 、Y 7 、Y 8 、Y 9 、Y 10 (ii) a The set X represents a detection target of the laser radar, the set Y represents a detection target of the camera, and measurement-measurement data matching in multi-source information fusion of the unmanned vehicle is achieved.
S2: by means of distance metersThe calculation method calculates the difference S between every two of 10 elements in the set X and 12 elements in the set Y i_j ；
The distance calculation adopts a Manhattan distance calculation mode process as follows, and the element in the set X is X i (x 1 ,y 1 ) The elements in the set Y are Y j (x 2 ，y 2 ) Calculating X i And Y j Degree of difference S between i_j ＝|x 1 -x 2 |+|y 1 -y 2 |；
Calculating the degree of difference S i_j The degree of difference S i_j Arranged in a small-to-large manner; the difference degree S i_j Arranging the elements in a small-to-large manner facilitates subsequent selection of the element with the least difference for matching.
S3: according to the degree of difference S i_j Building X i And Y j The connection relationship between the two is shown in fig. 6, and the specific process is as follows:
the first step is as follows: setting a threshold value T, wherein T is greater than 0, and taking T =6;
the second step is that: if the difference degree S i_j >6, then S ij If 0 is not less than 1, the difference S i_j If the ratio is less than or equal to 6, then S ij ＝S i_j ；
The third step: according to S ij The size establishes a connection relation if S ij <0, then X i And Y j If S has no connecting edge ij Greater than or equal to 0, then X i And Y j With connecting edges therebetween.
Establishing a connection relation by comparing the difference degrees, wherein X with larger difference degree i And Y j Cannot be connected with each other, and has small difference degree of X i And Y j And a connection relation is established between the two modules, so that the matching quality is ensured.
S4: as shown in FIGS. 7 to 8, the element X with the highest local matching degree in the set X and the set Y is preferentially selected i 、Y j Establishing an optimal matching pair, wherein the specific process comprises the following steps:
is X i Finding matching element and finding matching element Y from set Y j Matching element Y j Should satisfy the following relation with X i Degree of difference S of i_j Minimum, Y satisfying the above requirements j There are 0 or more; wherein X 1 Match Y 2 ，X 2 Matching Y 1 、Y 3 ，X 3 Match Y 4 ，X 4 Match Y 2 ，X 5 Matching Y 10 ，X 6 Match Y 5 ，X 7 Match Y 6 ，X 8 Matching Y 9 ，X 9 Matching Y 8 ，X 10 Match Y 8 、Y 10 ；
Is Y j Finding matching element and finding matching element X from set X i Matching element X i Should satisfy the following relation with Y j Degree of difference S of i_j Minimum, X satisfying the above requirements i There are 0 or more; wherein Y is 1 Matching X 2 ，Y 2 Match X 4 ，Y 3 Matching X 2 、X 4 ，Y 4 Match X 3 ，Y 5 Matching X 6 ，Y 6 Match X 4 ，Y 7 Match X 4 、X 10 ，Y 8 Match X 5 、X 9 、X 10 ，Y 9 Match X 8 ，Y 10 Matching X 5 ；
Selecting the optimal matching pair, if there is some X i And Y j When the matching elements are matched with each other and only one matching element exists, the two matching elements are determined as the optimal matching pair; wherein X 3 Y 4 、X 4 Y 2 、X 5 Y 10 、X 6 Y 5 、X 8 Y 9 Is an optimal matching pair;
by preferentially selecting the element X with the highest element matching degree in the set X and the set Y i 、Y j The optimal matching pair is established, so that the matching quality can be greatly improved.
S5: the rest of X 1 、X 2 、X 7 、X 9 、X 10 、Y 1 、Y 3 、Y 6 、Y 7 、Y 8 Form a new set A, B, the internal element A of the set A i Is A 1 、A 2 、A 7 、A 9 、A 10 Internal element B of set B j Is B 1 、B 3 、B 6 、B 7 、B 8 ；
S6: as shown in FIG. 9, A i Sequentially selecting one B with optimal local distance j Matching and selected B j No longer participate in the matching; wherein A is i Sequentially selecting one B with optimal local distance j In the process of matching A i Selecting the degree of difference S i_j Smallest one B j Carrying out matching, A 1 Match B 3 ，A 2 Match B 1 ,A 7 Match B 6 ,A 9 Match B 8 ，A 10 Match B 7 (ii) a And selecting the element with the optimal local distance and the minimum difference degree for matching, thereby effectively ensuring the matching quality.
S7: original X i Are all traversed and judged, and all matched pairs X are output 3 Y 4 、X 4 Y 2 、X 5 Y 10 、X 6 Y 5 、X 8 Y 9 ，X 1 Y 3 ，X 2 Y 1 ,X 7 Y 6 ,X 9 Y 8 ，X 10 Y 7 。
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions should be within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present disclosure and the scope of the present invention.

Claims (6)

1. A matching method based on optimal local distance is characterized in that the method utilizes a bipartite graph model to research the matching problem in the multi-source information fusion technology of an unmanned vehicle, and comprises the following steps:
s1: is provided withThe bipartite graph has a set X and a set Y, the number of internal elements is m and n respectively, and any element in the set X is represented as X i Any element in the set Y is represented as Y j Wherein i is more than or equal to 1 and less than or equal to m, and j is more than or equal to 1 and less than or equal to n; the set X represents a detection target of the laser radar, and the set Y represents a detection target of the camera;
s2: calculating the difference S between m elements in the set X and n elements in the set Y by adopting a distance calculation method i_j ；
S3: according to the degree of difference S i_j Building X i And Y j The connection relation between the two;
s4: preferentially selecting the element X with the highest element matching degree in the set X and the set Y i 、Y j Establishing an optimal matching pair;
s5: the rest of X i 、Y j Forming new sets A and B, and respectively representing the internal elements thereof as A i 、B j ；
S6：A i Sequentially selecting one B with optimal local distance j Matching and selected B j No longer participate in the matching;
s7: original X i Whether all the traversal judgment is finished or not is judged, if so, all the matching results are output, and otherwise, the matching judgment is sequentially continued;
the process of establishing the optimal matching pair in the step S4 includes the following steps:
is X i Finding matching element and finding matching element Y from set Y j Matching element Y j Should satisfy the following relation with X i Degree of difference S of i_j Minimum, Y satisfying the above requirements j There are 0 or more;
is Y j Finding matching element and finding matching element X from set X i Match element X i Should satisfy the following relation with Y j Degree of difference S of i_j Minimum, X satisfying the above requirements i There are 0 or more;
selecting the optimal matching pair, if some X exists i And Y j When they match each other and both have one and only one matching element, they are both confirmedDetermining an optimal matching pair;
a in said step S6 i Sequentially selecting one B with optimal local distance j In the process of matching A i Selecting the degree of difference S i_j Smallest one B j And (6) matching.
2. The matching method based on the optimal local distance as claimed in claim 1, wherein the distance calculation method in step S2 adopts the euclidean distance calculation process, and the element in the set X is X i (x 1 ,y 1 ) The element in the set Y is Y j (x 2 ，y 2 ) Calculating X i And Y j Degree of difference therebetween
Figure FDA0003872971950000021
3. The matching method based on the optimal local distance as claimed in claim 1, wherein the distance calculation method in step S2 adopts a manhattan distance calculation process, and the element in the set X is X i (x 1 ,y 1 ) The elements in the set Y are Y j (x 2 ，y 2 ) Calculating X i And Y j Degree of difference S between i_j ＝|x 1 -x 2 |+|y 1 -y 2 |。
4. The matching method based on the optimal local distance as claimed in claim 2 or 3, wherein the difference S is calculated in step S2 i_j The degree of difference S i_j Arranged in a small to large manner.
5. The matching method based on optimal local distance as claimed in claim 4, wherein X is established in step S3 i And Y j The specific process of the connection relationship is as follows:
the first step is as follows: setting a threshold value T, wherein T is greater than 0;
the second step is that: if the difference is S i_j >T, then S ij If 0 is not less than 1, the difference S i_j T is less than or equal to T, then S ij ＝S i_j ；
The third step: according to S ij The size establishes a connection relation if S ij <0, then X i And Y j There is no connecting edge between them, if S ij Greater than or equal to 0, then X i And Y j With connecting edges in between.
6. The optimal local distance-based matching method as claimed in claim 1, wherein the method is applied to target matching of "measure-measure" in multi-source information fusion of unmanned vehicles.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110866687A (en) * 2019-11-07 2020-03-06 中盈优创资讯科技有限公司 Task allocation method and device
CN111078803A (en) * 2019-11-26 2020-04-28 武汉大学 Load event multidimensional feature matching method and system based on non-invasive technology
CN111126799A (en) * 2019-12-10 2020-05-08 广东工业大学 Shared network driver and crew matching method based on bipartite graph

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110866687A (en) * 2019-11-07 2020-03-06 中盈优创资讯科技有限公司 Task allocation method and device
CN111078803A (en) * 2019-11-26 2020-04-28 武汉大学 Load event multidimensional feature matching method and system based on non-invasive technology
CN111126799A (en) * 2019-12-10 2020-05-08 广东工业大学 Shared network driver and crew matching method based on bipartite graph

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
"Approximate graph edit distance compution by means of bipartite graph matching";Kaspar Riesen etc.;《Image and Vision Computing》;20091231;第950-959页 *
"Bipartite Graph Partition Based Coclustering With Joint Sparsity for Hyperspectral Images";Nan Huang etc.;《IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING》;20191231;第12卷(第12期);第4698-4705页 *
"二部图及匹配算法的描述";王立波;《杭州电子工业学院学报》;20020630;第22卷(第3期);第15-18页 *
"基于局部优化与二分图匹配的PPI网络比对算法";祝家烨;《计算机应用与软件》;20180131;第35卷(第1期);第281-286页 *

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