CN114492573A - Map matching method and device based on relaxation iteration semantic features - Google Patents

Map matching method and device based on relaxation iteration semantic features Download PDF

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CN114492573A
CN114492573A CN202111576197.XA CN202111576197A CN114492573A CN 114492573 A CN114492573 A CN 114492573A CN 202111576197 A CN202111576197 A CN 202111576197A CN 114492573 A CN114492573 A CN 114492573A
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李公维
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Heading Data Intelligence Co Ltd
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Abstract

The invention relates to a map matching method and a device based on semantic features of relaxation iteration, wherein the method comprises the following steps: defining the similarity and compatibility of elements in any two subgraphs; defining a matching probability matrix of any two subgraphs; and determining an iterative formula of the matching probability matrix according to the similarity and compatibility of the elements, and calculating and determining the matching relation of any two subgraphs according to the iterative formula. The invention realizes the element matching of the characteristic map under the crowdsourcing map scene, can adapt to the input data with larger errors in position, shape and attribute, and has fast convergence and high accuracy.

Description

Map matching method and device based on relaxation iteration semantic features
Technical Field
The invention belongs to the technical field of high-precision maps and automatic driving, and particularly relates to a map matching method and device based on relaxation iteration semantic features.
Background
The high-precision map is generated by using the crowdsourcing scheme, the defects of low positioning precision and unstable identification of the marker elements of crowdsourcing data are overcome, and the advantages of low cost and large data volume are exerted. The high-precision map is synthesized by using multi-vehicle multi-observation data, and two key steps of sub-graph matching and sub-graph fusion are required to be completed. Wherein the quality of sub-graph element matching is very critical.
In the actual production process, the uplink bandwidth limitation of crowdsourcing vehicle data collection equipment is considered, and local maps acquired by a vehicle end are compressed to reduce the data volume. The data compression mode is to convert the grid map into a line feature map or a surface feature map, so that the matching method based on point cloud registration is not suitable for matching of the crowdsourced feature map any more in the process of back-end processing. In addition, many hidden information in the original data is discarded in the compression process, and errors are introduced into the semantic information generation algorithm, so that difficulty is brought to matching. The success rate of the existing matching algorithm based on local features is low. Therefore, an algorithm capable of matching the line feature and the face feature map after the compression process is required.
Disclosure of Invention
In order to solve the problems of information loss, difficult point cloud matching and low accuracy of the crowdsourced map in the mapping process, the invention provides a map matching method based on relaxation iteration semantic features in a first aspect, which comprises the following steps: defining the similarity and compatibility of elements in any two subgraphs; defining a matching probability matrix of any two subgraphs; and determining an iterative formula of the matching probability matrix according to the similarity and compatibility of the elements, and calculating and determining the matching relation of any two subgraphs according to the iterative formula.
In some embodiments of the present invention, the defining similarity and compatibility of elements in any two subgraphs includes: determining the similarity of the elements according to the geometric similarity and semantic similarity between any two elements; the compatibility of the elements is determined based on the relative position between any two elements.
Further, the similarity of the elements is calculated by the following method:
Sim(Ai，Bj)＝k1*GeoSim(Ai，Bj)+k2*SemanticSim(Ai，Bj)，
wherein A isi、BjRespectively representing the ith element of sub-graph A and the jth element of sub-graph B, Sim (A)i，Bj) Is represented by AiAnd BjSimilarity of (A), GeoSim (A)i，Bj) Is AiAnd BjGeometric similarity of (A), SemanticSim (A)i，Bj) Is represented by AiAnd BjSemantic similarity of (c), k1、k2Representing the weighting parameters.
Further, the compatibility of the elements is calculated by the following method:
let Ai、BjRespectively representing the ith element of sub-graph A and the jth element of sub-graph B, Ah、BkRespectively represent aiAnd BjAny one element in proximity;
translating B in sub-graph B simultaneouslyjAnd BkTo BjAnd AiCoincidence, BkMove to Bk′The relative position deviation delta (A)i，Ah，Bk′)＝dist(Ah，Bk′)/dist(Ai，Ah) (ii) a Wherein dist (.) indicates the distance between each element in parentheses;
with C (A)i，Bj，Ah，Bk) Is represented by Ai，Bj，Ah，BkCompatibility of the four, then
Figure BDA0003424916970000021
Figure BDA0003424916970000022
In some embodiments of the present invention, the determining the iterative formula of the matching probability matrix according to the similarity and compatibility of the elements comprises: determining an initial value of the matching probability matrix according to the similarity of the elements; and determining an iterative formula of the matching probability matrix according to the compatibility of the elements.
Further, the iterative formula of the matching probability matrix is represented as:
Figure BDA0003424916970000023
wherein,
Figure BDA0003424916970000024
respectively representing the matching probability matrixes of the subgraph A and the subgraph B during the (r + 1) th iteration and the (r) th iteration; a. thei、BjRespectively representing the ith element of the subgraph A and the jth element of the subgraph B, and m and n respectively representing the total number of the elements of the subgraph A and the subgraph B; a. theh、BkRespectively represent aiAnd BjAny one element adjacent, C (A)i，Bj，Ah，Bk) Is represented by AiAnd BjThe compatibility of (c).
In a second aspect of the present invention, a map matching apparatus based on relaxed iterative semantic features is provided, including: the first definition module is used for defining the similarity and compatibility of elements in any two subgraphs; the second definition module is used for defining a matching probability matrix of any two subgraphs; and the determining module is used for determining an iterative formula of the matching probability matrix according to the similarity and compatibility of the elements, and calculating and determining the matching relation of any two subgraphs according to the iterative formula.
Further, the first defining module comprises: the first determining unit is used for determining the similarity of the elements according to the geometric similarity and semantic similarity between any two elements; a second determining unit for determining compatibility of the elements according to a relative position between any two elements.
In a third aspect of the present invention, there is provided an electronic device comprising: one or more processors; a storage device, configured to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the map matching method based on relaxed iterative semantic features provided in the first aspect of the present invention.
In a fourth aspect of the present invention, a computer-readable medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the map matching method based on semantic features of relaxation iteration provided by the invention in the first aspect.
The invention has the beneficial effects that:
1. the invention realizes the integration of semantic similarity and geometric similarity between elements and realizes the element matching of the feature map under the crowdsourcing application scene;
2. the method can adapt to input data with large errors in position, shape and attribute, and has the advantages of fast convergence and high accuracy.
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FIG. 1 is a basic flow diagram of a method for map matching based on relaxed iterative semantic features in some embodiments of the present invention;
FIG. 2 is a schematic diagram illustrating the principle of calculating relative positional deviation in the compatibility of elements in some embodiments of the present invention;
FIG. 3 is a schematic structural diagram of a map matching apparatus based on semantic features of relaxation iteration in some embodiments of the present invention;
fig. 4 is a schematic structural diagram of an electronic device in some embodiments of the invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1 and fig. 2, in a first aspect of the present invention, there is provided a map matching method based on semantic features of relaxation iteration, including: s100, defining similarity and compatibility of elements in any two subgraphs; s200, defining a matching probability matrix of any two subgraphs; s300, determining an iterative formula of the matching probability matrix according to the similarity and compatibility of the elements, and calculating and determining the matching relation of any two sub-graphs according to the iterative formula.
It should be noted that, in some scenarios of the present invention, any two sub-graphs mainly refer to one or more element sets extracted from multiple crowd-sourced maps, where there is an association, where the association includes collecting time closeness, distance closeness, or semantic correlation, for example, a lane line on the same road and a corresponding traffic sign belong to semantic correlation.
In step S100 of some embodiments of the present invention, the defining similarity and compatibility of elements in any two sub-graphs includes: s101, determining the similarity of the elements according to the geometric similarity and semantic similarity between any two elements; s102, determining compatibility of the elements according to the relative position between any two elements.
Specifically, subgraphs a and B are defined: a ═ A1，A2...Am}，B＝{B，B2...BnEach is a set consisting of a plurality of lane markings and traffic signboards; the similarity of the elements is calculated by:
Sim(Ai，Bj)＝k1*GeoSim(Ai，Bj)+k2*SemanticSim(Ai，Bj)，
wherein A isi、BjRespectively representing the ith element of sub-graph A and the jth element of sub-graph B, Sim (A)i，Bj) Is represented by AiAnd BjSimilarity of (A), GeoSim (A)i，Bj) Is AiAnd BjGeometric similarity of (A), SemanticSim (A)i，Bj) Is represented by AiAnd BjSemantic similarity of (c), k1、k2Representing the weighting parameters. It should be noted that the geometric similarity is generally a function of the area and distance of the elements; semantic similarity is represented by the probability of whether elements belong to the same class of elements or not, and can also be represented by the distance of a multidimensional vector.
Referring to fig. 3, further, the compatibility of the elements is calculated by the following method:
let Ai、BjRespectively representing the ith element of sub-graph A and the jth element of sub-graph B, Ah、BkRespectively represent aiAnd BjAny one element in proximity;
translating B in sub-graph B simultaneouslyjAnd BkTo BjAnd AiCoincidence, BkMove to Bk′The relative position deviation delta (A)i，Ah，Bk′)＝dist(Ah，Bk′)/dist(Ai，Ah) (ii) a Wherein dist (.) indicates the distance between each element in parentheses;
with C (A)i，Bj，Ah，Bk) Is represented by Ai，Bj，Ah，BkCompatibility of the four, then
Figure BDA0003424916970000051
Figure BDA0003424916970000052
In step S200 of some embodiments of the invention, a matching probability matrix is defined for any two subgraphs. Specifically, a matching probability matrix P ═ P (P) for subgraph a and subgraph B is definedi，j)∈Rm×nWherein p isi，jRepresenting A in subfigure AiProbability that an element should be matched to a Bj element in sub-graph B;
initialization
Figure BDA0003424916970000053
In step S300 of some embodiments of the present invention, the determining the iterative formula of the matching probability matrix according to the similarity and compatibility of the elements includes: s301, determining an initial value of the matching probability matrix according to the similarity of the elements; s302, determining an iterative formula of the matching probability matrix according to the compatibility of the elements.
Specifically, step S302 includes: for each pair Ai，BjSearch for element A in its respective neighborhoodh，BkAnd calculating a compatibility function, then: the iterative formula of the matching probability matrix is expressed as:
Figure BDA0003424916970000061
wherein,
Figure BDA0003424916970000062
respectively representing the matching probability matrixes of the subgraph A and the subgraph B during the (r + 1) th iteration and the (r) th iteration; a. thei、BjRespectively representing the ith element of the subgraph A and the jth element of the subgraph B, and m and n respectively representing the total number of the elements of the subgraph A and the subgraph B; a. theh、BkRespectively represent aiAnd BjAny one element adjacent, C (A)i，Bj，Ah，Bk) Is represented by AiAnd BjThe compatibility of (c). Step S302 is repeatedly executed for each AiSearching the column mark x of the ith row maximum value of the P matrix, and then the matching relation A existsi→Bx. Finally, any element A of subgraph AiAll elements B with the highest similarity can be matched in the sub-graph Bx。
Example 2
Referring to fig. 3, in a second aspect of the present invention, there is provided a map matching apparatus 1 based on semantic features of relaxation iteration, including: the first definition module 11 is used for defining the similarity and compatibility of elements in any two subgraphs; a second defining module 12, configured to define a matching probability matrix of any two subgraphs; and the determining module 13 is configured to determine an iterative formula of the matching probability matrix according to the similarity and compatibility of the elements, and calculate and determine a matching relationship between any two sub-graphs according to the iterative formula.
Further, the first defining module 11 includes: the first determining unit is used for determining the similarity of the elements according to the geometric similarity and semantic similarity between any two elements; a second determining unit for determining compatibility of the elements according to a relative position between any two elements.
Example 3
Referring to fig. 4, in a third aspect of the present invention, there is provided an electronic apparatus comprising: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method of the invention in the first aspect.
The electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following devices may be connected to the I/O interface 505 in general: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; a storage device 508 including, for example, a hard disk; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 4 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 4 may represent one device or may represent multiple devices as desired.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of embodiments of the present disclosure. It should be noted that the computer readable medium described in the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In embodiments of the present disclosure, however, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more computer programs which, when executed by the electronic device, cause the electronic device to:
computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, Python, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (10)

1. A map matching method based on semantic features of relaxation iteration is characterized by comprising the following steps:
defining the similarity and compatibility of elements in any two subgraphs;
defining a matching probability matrix of any two subgraphs;
and determining an iterative formula of the matching probability matrix according to the similarity and compatibility of the elements, and calculating and determining the matching relation of any two subgraphs according to the iterative formula.
2. The map matching method based on semantic features of relaxation iteration as claimed in claim 1, wherein the defining similarity and compatibility of elements in any two subgraphs comprises:
determining the similarity of the elements according to the geometric similarity and semantic similarity between any two elements;
the compatibility of the elements is determined based on the relative position between any two elements.
3. The relaxation iteration based semantic feature map matching method according to claim 2, characterized in that the similarity of the elements is calculated by the following method:
Sim(Ai，Bj)＝k1*GeoSim(Ai，Bj)+k2*SemanticSim(Ai，Bj)，
wherein A isi、BjRespectively represent the ith element of sub-graph A and the jth element of sub-graph B, Sim (A)i，Bj) Is represented by AiAnd BjSimilarity of (A), GeoSim (A)i，Bj) Is AiAnd BjGeometric similarity of (A), SemanticSim (A)i，Bj) Is represented by AiAnd BjSemantic similarity of (c), k1、k2Representing the weighting parameters.
4. The relaxation iteration based semantic feature map matching method according to claim 2, characterized in that the compatibility of the elements is calculated by the following method:
let Ai、BjRespectively representing the ith element of sub-graph A and the jth element of sub-graph B, Ah、BkRespectively represent aiAnd BjAny one element in proximity;
translating B in sub-graph B simultaneouslyjAnd BkTo BjAnd AiCoincidence, BkMove to Bk′Then the relative position deviation delta (A)i，Ah，Bk′)＝dist(Ah，Bk′)/dist(Ai，Ah) (ii) a Wherein dist (.) indicates the distance between each element in parentheses;
with C (A)i，Bj，Ah，Bk) Is represented by Ai，Bj，Ah，BkCompatibility of the four, then
Figure FDA0003424916960000021
Figure FDA0003424916960000022
5. The method for map matching based on semantic features of relaxation iteration as claimed in claim 1, wherein the determining the iterative formula of the matching probability matrix according to similarity and compatibility of the elements comprises:
determining an initial value of the matching probability matrix according to the similarity of the elements;
and determining an iterative formula of the matching probability matrix according to the compatibility of the elements.
6. The map matching method based on semantic features of relaxation iteration as claimed in claim 5, wherein the iterative formula of the matching probability matrix is represented as:
Figure FDA0003424916960000023
wherein,
Figure FDA0003424916960000024
respectively representing the matching probability matrixes of the subgraph A and the subgraph B during the (r + 1) th iteration and the (r) th iteration; a. thei、BjRespectively representing the ith element of the subgraph A and the jth element of the subgraph B, and m and n respectively representing the total number of the elements of the subgraph A and the subgraph B; a. theh、BkRespectively represent with AiAnd BjAny one element adjacent, C (A)i，Bj，Ah，Bk) Is represented by AiAnd BjThe compatibility of (c).
7. A map matching apparatus based on semantic features of relaxation iteration, comprising:
the first definition module is used for defining the similarity and compatibility of elements in any two subgraphs;
the second definition module is used for defining a matching probability matrix of any two subgraphs;
and the determining module is used for determining an iterative formula of the matching probability matrix according to the similarity and compatibility of the elements, and calculating and determining the matching relation of any two subgraphs according to the iterative formula.
8. The iterative relaxation-based semantic feature map matching device of claim 7, wherein the first definition module comprises:
the first determining unit is used for determining the similarity of the elements according to the geometric similarity and semantic similarity between any two elements;
a second determining unit for determining compatibility of the elements according to a relative position between any two elements.
9. An electronic device, comprising: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the relaxed iterative semantic feature based map matching method of any one of claims 1 to 6.
10. A computer-readable medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the relaxation iteration based map matching method of semantic features of any one of claims 1 to 6.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116361665A (en) * 2023-02-15 2023-06-30 江西师范大学 Building object plane element matching method and system based on environment information

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116361665A (en) * 2023-02-15 2023-06-30 江西师范大学 Building object plane element matching method and system based on environment information
CN116361665B (en) * 2023-02-15 2024-02-23 江西师范大学 Building object plane element matching method and system based on environment information

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