CN116067357A - High-precision map diagnosis and updating method based on multi-source fusion positioning - Google Patents

High-precision map diagnosis and updating method based on multi-source fusion positioning Download PDF

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Publication number
CN116067357A
CN116067357A CN202211524868.2A CN202211524868A CN116067357A CN 116067357 A CN116067357 A CN 116067357A CN 202211524868 A CN202211524868 A CN 202211524868A CN 116067357 A CN116067357 A CN 116067357A
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map
sensing
precision map
feature element
result
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CN202211524868.2A
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Chinese (zh)
Inventor
刘胜
万木春
尹玉成
刘奋
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Heading Data Intelligence Co Ltd
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Heading Data Intelligence Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3804—Creation or updating of map data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3804—Creation or updating of map data
    • G01C21/3807—Creation or updating of map data characterised by the type of data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3804—Creation or updating of map data
    • G01C21/3807—Creation or updating of map data characterised by the type of data
    • G01C21/3811—Point data, e.g. Point of Interest [POI]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3804—Creation or updating of map data
    • G01C21/3833—Creation or updating of map data characterised by the source of data
    • G01C21/3841—Data obtained from two or more sources, e.g. probe vehicles
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention relates to a high-precision map diagnosis and updating method based on multi-source fusion positioning, which comprises the following steps: acquiring various map feature element data of the physical environment of the space-time region to be detected based on a plurality of sensing devices, and counting to obtain a sensing result of any map feature element data; classifying the sensing results to obtain the statistical number of the sensing results of various types, wherein the statistical number is the number of sensing devices sensing the sensing results of the type; consistency judgment is carried out on the type of the perception result when the statistical number is the largest and the type of the map feature element in the current high-precision map, and if the type of the perception result is inconsistent, the diagnosis result of the space-time area to be detected is that the current high-precision map needs to be updated; through data analysis and matching of program automation, whether map information with local errors exists is obtained on the basis of statistics and mathematics, effective diagnosis of map problems is achieved, and time, labor and material costs of manual troubleshooting are reduced.

Description

High-precision map diagnosis and updating method based on multi-source fusion positioning
Technical Field
The invention relates to the technical field of computers, in particular to a high-precision map diagnosis and updating method based on multi-source fusion positioning.
Background
The unmanned automobile is an intelligent automobile, also called a wheel type mobile robot, and mainly depends on an intelligent driver taking a computer system as a main part in the automobile to realize the aim of unmanned.
The multi-source information fusion is abbreviated as multi-source fusion and is a method for processing multi-source information. The method has the advantages of high precision, good fault tolerance, low information acquisition cost, capability of realizing information complementation and the like, and is widely applied to the fields of modern military, industry, traffic, finance and the like, thereby playing a pushing role in the development of technological modernization.
In outdoor open scenes, GNSS can provide real-time, reliable and stable navigation positioning service, but in urban environments, due to the fact that the scenes are complex, the problems of few visible satellites in urban canyons, multipath effect, out-of-lock of scene signals of tunnels and the like exist, so that positioning accuracy is poor, reliability is low, and positioning requirements of full scenes, real-time, high accuracy and high reliability are difficult to meet. Therefore, a multisource fusion positioning technology is proposed, which processes satellite navigation positioning, wireless sensor positioning and other auxiliary positioning technologies by adopting an information fusion method, and finally reliable, accurate and stable positioning service is obtained.
The high-precision Map (HD Map or HAD Map) is an electronic Map, has absolute precision and relative precision in centimeter level, and has the characteristics of high precision, high freshness and high richness. The high-precision map provides an environment model of an automatic driving vehicle and comprises a static high-precision map at the bottom layer and other dynamic information. High-precision maps have been considered as an "important infrastructure" for the period of automatic driving, and have also certainly played an important role as a "data base" in the fields of intelligent traffic, intelligent cities, and the like. Map data errors often occur in produced high-precision maps. Because of the large amount of map data, a great deal of labor, material resources and time costs are required to manually check the problem of the high-precision map, and the map updating production efficiency is affected.
The generated vehicle-end data cannot upload data containing absolute position information to the cloud end. In order to realize normal mapping on the premise that the vehicle-end data uploading does not contain absolute position information, a vehicle-end sensing data uploading method needs to be designed for updating the map.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a high-precision map diagnosis and updating method based on multi-source fusion positioning, which is characterized in that under the data conditions provided by a plurality of unmanned vehicles with environment sensing and positioning capabilities, map information with local errors is obtained on the basis of statistics and mathematics through data analysis and matching of program automation, absolute positions in sensing information are converted into relative position information relative to a reference point selected in advance, and then the relative position information is quickly uploaded to a cloud through a network, and a map is updated through a series of special processing methods, so that the requirements of problem diagnosis and updating of the high-precision map are met.
According to a first aspect of the present invention, there is provided a high-precision map diagnosis method based on multi-source fusion positioning, comprising:
step 1, acquiring various map feature element data of the physical environment of a space-time region to be detected based on a plurality of sensing devices, and counting to obtain a sensing result of any map feature element data;
step 2, classifying the sensing results to obtain the statistical number of the sensing results of various types, wherein the statistical number is the number of sensing devices sensing the sensing results of the type;
and step 3, judging consistency between the type of the perception result when the statistical number is maximum and the type of the map feature element in the current high-precision map, and if the type of the perception result is inconsistent with the type of the map feature element in the current high-precision map, updating the current high-precision map as a diagnosis result of the space-time region to be detected.
On the basis of the technical scheme, the invention can also make the following improvements.
Optionally, the sensing device is a vehicle with environment sensing and positioning capabilities, and a plurality of vehicles travel in the space-time area to be measured to obtain the map ground feature element data; the types of the map feature element data comprise: lane lines and poi.
Optionally, the classification result of classifying the sensing result in the step 2 is:
{(obj 0 ,N 0 )…(obj i ,N i )…(obj m ,N m )}；
wherein obj is i As the i-th type of sensing result of the map feature element, (obj) i ,N i ) Perception result obj representing the ith category of the map feature element i N is the statistical number of (C) i M is the total number of classes of the sensing result;
the kinds of the perception result include: type, relative position information, track angle, and time.
Optionally, before the step 3 of performing the consistency determination, the method includes:
and matching and positioning the map feature elements corresponding to the current high-precision map according to the position information, and obtaining the types of the map feature elements in the current high-precision map.
According to a second aspect of the present invention, there is provided a high-precision map updating method based on multi-source fusion positioning, where the updating method is based on the high-precision map diagnosis method according to the embodiment of the present invention, and when the result of consistency determination in the step 3 is inconsistent, the method for updating the current high-precision map includes:
step 4, at the sensing equipment end, the absolute position information of the map feature element is converted into the relative position information relative to a preset reference point;
step 5, uploading the feature element information converted into the relative position and the reference point index information to the cloud;
and 6, pulling the latest data from the cloud at fixed time, acquiring datum point data from the vehicle end according to the index information, generating a local vector map, and updating the local information of the high-precision map.
On the basis of the technical scheme, the invention can also make the following improvements.
Optionally, the location information in the step 4 includes longitude lon, latitude lat and elevation high;
in the step 4, the absolute position information of the map feature element is (lon i ,lat i ,high i ) The absolute position information of the reference point is longitude, latitude and elevation (lon b ,lat b ,high b ) The converted relative position information is (lon i -lon b ,lat i -lat b ,high i -high b )。
Optionally, the reference point index information is time information.
According to the high-precision map diagnosis and updating method based on multi-source fusion positioning, under the data conditions provided by a plurality of unmanned vehicles with environment sensing and positioning capabilities, map information with local errors is obtained on the basis of statistics and mathematics through program automation, absolute positions in the sensing information are converted into relative position information relative to a reference point selected in advance and then are quickly uploaded to a cloud through a network, and a map is updated through a series of special processing methods, so that the requirements of problem diagnosis and updating of the high-precision map are met. The map problem can be effectively diagnosed, the data updating and repairing efficiency of the map of the local problem can be improved by uploading the diagnosed key ground object information, and the time, labor and material cost of manually checking the problem can be reduced.
Drawings
FIG. 1 is a flow chart of a high-precision map diagnosis method based on multi-source fusion positioning provided by the invention;
fig. 2 is a flowchart of a high-precision map diagnosis and updating method based on multi-source fusion positioning.
Detailed Description
The principles and features of the present invention are described below with reference to the drawings, the examples are illustrated for the purpose of illustrating the invention and are not to be construed as limiting the scope of the invention.
Fig. 1 is a flowchart of a high-precision map diagnosis method based on multi-source fusion positioning, provided by the invention, as shown in fig. 1, the diagnosis method comprises:
step 1, acquiring various map feature element data of the physical environment of a space-time region to be detected based on a plurality of sensing devices, and counting to obtain a sensing result of any map feature element data.
And step 2, classifying the sensing results to obtain the statistical number of the sensing results of various types, wherein the statistical number is the number of sensing devices sensing the sensing results of the type.
And 3, judging consistency between the type of the perception result when the statistical number is maximum and the type of the map feature element in the current high-precision map, and if the type of the perception result is inconsistent with the type of the map feature element in the current high-precision map, updating the current high-precision map as a diagnosis result of the space-time region to be detected.
According to the high-precision map diagnosis method based on multi-source fusion positioning, under the data conditions provided by a plurality of unmanned vehicles with environment sensing and positioning capabilities, whether map information with local errors exists is obtained on the basis of statistics and mathematics through data analysis and matching of program automation, so that effective diagnosis of map problems is realized, and time, labor and material cost of manual investigation of the problems are reduced.
Example 1
Embodiment 1 provided by the present invention is an embodiment of a high-precision map diagnosis method based on multi-source fusion positioning provided by the present invention, and as can be seen from fig. 2, the embodiment of the diagnosis method includes:
step 1, acquiring various map feature element data of the physical environment of a space-time region to be detected based on a plurality of sensing devices, and counting to obtain a sensing result of any map feature element data.
In one possible embodiment, the sensing device is an unmanned vehicle with environment sensing and positioning capabilities, and a plurality of vehicles run in the space-time area to be measured to obtain map ground feature element data. The types of map feature element data include: lane lines and poi (Point of Interest, points of interest), etc.
And step 2, classifying the sensing results to obtain the statistical number of the sensing results of various types, wherein the statistical number is the number of sensing devices sensing the sensing results of the type.
In a possible embodiment, the classification result of classifying the sensing result in step 2 is:
{(obj 0 ,N 0 )…(obj i ,N i )…(obj m ,N m )}。
wherein obj is i As the i-th type of sensing result of the map feature element, (obj) i ,N i ) Perception result obj representing the ith category of the map feature element i N is the statistical number of (C) i M is the total number of classes of perception results.
The kinds of the perception result include: key information such as type, relative position information, track angle and time.
In specific implementation, the system is powered on to count the number of classification of the sensing result of certain map feature element data in the same space-time area { (obj) 0 ,N 0 )…(obj i ,N i )…(obj m ,N m ) And when the sensing equipment is a vehicle, N is the number of vehicles for sensing the sensing result of the type of the map feature element.
And 3, judging consistency between the type of the perception result when the statistical number is maximum and the type of the map feature element in the current high-precision map, and if the type of the perception result is inconsistent with the type of the map feature element in the current high-precision map, updating the current high-precision map as a diagnosis result of the space-time region to be detected.
In a possible embodiment, the step 3 includes, before the step of determining the consistency:
and matching and positioning the map feature elements corresponding to the current high-precision map according to the position information, and obtaining the types of the map feature elements in the current high-precision map.
In the specific implementation, in the step 3, the sensing result corresponding to the maximum value of the statistical number is taken as the classification information of the map feature element, that is, obj=argmax { (obj) 0 ,N 0 )…(obj i ,N i )…(obj m ,N m ) }. Matching and positioning the map feature elements corresponding to the current high-precision map, and obtaining attribute information obj map of the elements, whether the attribute information obj map is similar to perceived elements or notIf the model is consistent, the data is not required to be uploaded and the map is not required to be updated, and the process is finished; if not, the map needs to be updated.
Example 2
An embodiment 2 provided by the present invention is an embodiment of a high-precision map updating method based on multi-source fusion positioning provided by the present invention, fig. 2 is a flowchart of a high-precision map updating method based on multi-source fusion positioning provided by the embodiment of the present invention, and as can be known from fig. 2, the embodiment of the updating method includes:
step 1, acquiring various map feature element data of the physical environment of a space-time region to be detected based on a plurality of sensing devices, and counting to obtain a sensing result of any map feature element data.
And step 2, classifying the sensing results to obtain the statistical number of the sensing results of various types, wherein the statistical number is the number of sensing devices sensing the sensing results of the type.
And 3, judging consistency between the type of the perception result when the statistical number is maximum and the type of the map feature element in the current high-precision map, and if the type of the perception result is inconsistent with the type of the map feature element in the current high-precision map, updating the current high-precision map as a diagnosis result of the space-time region to be detected. And when the consistency judgment result in the step 3 is inconsistent, executing the steps 4-6.
And 4, at the sensing equipment end, the absolute position information of the map feature element is converted into the relative position information relative to a preset reference point.
In a specific implementation, when the sensing device is a vehicle, the vehicle end needs to select a reference point in advance.
In one possible embodiment, the location information in step 4 includes longitude lon, latitude lat, and elevation high.
In step 4, the absolute position information of the map feature element is (lon i ,lat i ,high i ) The absolute position information of the reference point is longitude, latitude, and elevation (lon b ,lat b ,high b ) The converted relative position information is (lon i -lon b ,lat i -lat b ,high i -high b )
And 5, uploading the feature element information converted into the relative position and the reference point index information to the cloud.
In one possible embodiment, the reference point index information is time information, and in a specific implementation, the code may be in terms of year, month, day, and time seconds (abbreviated as YMDHMS).
And 6, the map generation center regularly pulls up the latest data from the cloud end, acquires datum point data from the vehicle end according to the index information, generates a local vector map, and updates the local information of the high-precision map through a series of professional processing methods.
It can be understood that the high-precision map updating method based on multi-source fusion positioning provided by the invention corresponds to the high-precision map diagnosis method based on multi-source fusion positioning provided by the foregoing embodiments, and the relevant technical features of the high-precision map updating method based on multi-source fusion positioning can refer to the relevant technical features of the high-precision map diagnosis method based on multi-source fusion positioning, which are not described herein again.
According to the high-precision map diagnosis and updating method based on multi-source fusion positioning, under the data conditions provided by a plurality of unmanned vehicles with environment sensing and positioning capabilities, map information with local errors is obtained on the basis of statistics and mathematics through program automation data analysis and matching, absolute positions in sensing information are converted into relative position information relative to a reference point selected in advance and then are quickly uploaded to a cloud through a network, and a map is updated through a series of special processing methods, so that the requirements of problem diagnosis and updating of the high-precision map are met. The map problem can be effectively diagnosed, the data updating and repairing efficiency of the map of the local problem can be improved by uploading the diagnosed key ground object information, and the time, labor and material cost of manually checking the problem can be reduced.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (7)

1. The high-precision map diagnosis method based on multi-source fusion positioning is characterized by comprising the following steps of:
step 1, acquiring various map feature element data of the physical environment of a space-time region to be detected based on a plurality of sensing devices, and counting to obtain a sensing result of any map feature element data;
step 2, classifying the sensing results to obtain the statistical number of the sensing results of various types, wherein the statistical number is the number of sensing devices sensing the sensing results of the type;
and step 3, judging consistency between the type of the perception result when the statistical number is maximum and the type of the map feature element in the current high-precision map, and if the type of the perception result is inconsistent with the type of the map feature element in the current high-precision map, updating the current high-precision map as a diagnosis result of the space-time region to be detected.
2. The method according to claim 1, wherein the sensing device is a vehicle with environment sensing and positioning capabilities, and a plurality of vehicles travel in the space-time area to be measured to obtain the map feature element data; the types of the map feature element data comprise: lane lines and poi.
3. The high-precision map diagnosis method according to claim 1, wherein the classification result of classifying the perception result in step 2 is:
{(obj 0 ,N 0 )…(obj i ,N i )…(obj m ,N m )}；
wherein obj is i As the i-th type of sensing result of the map feature element, (obj) i ,N i ) Perception result obj representing the ith category of the map feature element i N is the statistical number of (C) i M is the total number of classes of the sensing result;
the kinds of the perception result include: type, relative position information, track angle, and time.
4. The high-precision map diagnosis method according to claim 1, wherein the step 3 before making the consistency determination comprises:
and matching and positioning the map feature elements corresponding to the current high-precision map according to the position information, and obtaining the types of the map feature elements in the current high-precision map.
5. A high-precision map updating method based on multisource fusion positioning, the updating method is based on the high-precision map diagnosis method according to any one of claims 1 to 4, and is characterized in that when the consistency judgment result in the step 3 is inconsistent, the method for updating the current high-precision map comprises the following steps:
step 4, at the sensing equipment end, the absolute position information of the map feature element is converted into the relative position information relative to a preset reference point;
step 5, uploading the feature element information converted into the relative position and the reference point index information to the cloud;
and 6, pulling the latest data from the cloud at fixed time, acquiring datum point data from the vehicle end according to the index information, generating a local vector map, and updating the local information of the high-precision map.
6. The high-precision map diagnosis method according to claim 5, wherein the position information in step 4 includes longitude lon, latitude lat, and elevation high;
in the step 4, the absolute position information of the map feature element is (lon i ,lat i ,high i ) The absolute position information of the reference point is longitude, latitude and elevation (lon b ,lat b ,high b ) The converted relative position information is (lon i -lon b ,lat i -lat b ,high i -high b )。
7. The high-precision map diagnostic method according to claim 5, wherein the reference point index information is time information.
CN202211524868.2A 2022-11-30 2022-11-30 High-precision map diagnosis and updating method based on multi-source fusion positioning Pending CN116067357A (en)

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CN116067357A true CN116067357A (en) 2023-05-05

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