CN114740505A

CN114740505A - Positioning processing method and device

Info

Publication number: CN114740505A
Application number: CN202110023096.3A
Authority: CN
Inventors: 郑东方; 郭辉文; 杨东升; 徐一梁
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2021-01-08
Filing date: 2021-01-08
Publication date: 2022-07-12

Abstract

The application provides a positioning processing method, a positioning processing device, electronic equipment and a computer readable storage medium; relates to an artificial intelligence positioning technology, and the method comprises the following steps: determining an observed position of the positioning target based on a carrier phase; determining an identified position of the positioning target based on a road element identified from the image of the road environment and a position of the road element in the map; determining a deviation between the observed position of the positioning target and the identified position of the positioning target based on the observed position of the positioning target and the identified position of the positioning target; and correcting the observation position based on the deviation to obtain an updated observation position of the positioning target. Through the method and the device, the positioning deviation can be eliminated, and the positioning precision is improved.

Description

Positioning processing method and device

Technical Field

The present application relates to artificial intelligence positioning technologies, and in particular, to a positioning processing method and apparatus, an electronic device, and a computer-readable storage medium.

Background

As artificial intelligence technology has been researched and developed, the artificial intelligence technology has been developed and applied in various fields, such as automatic driving. The automatic driving technology generally comprises technologies such as high-precision maps, environment perception, behavior decision, path planning, motion control and the like, and has wide application prospects.

In a vehicle-mounted positioning product in automatic driving, positioning is realized by a visual fusion high-precision map and Real-time kinematic (RTK) carrier difference technology. The global position (e.g. longitude and latitude) of the road element in the default high-precision map and the global observation longitude and latitude of the RTK are consistent, i.e. there is no deviation between the high-precision map and the RTK. However, in an actual application scenario, due to the influence of factors such as movement of the earth crust, position deviation of a GPS base station, or clock synchronization, the position of the RTK and the position of an element in the high-precision map have a certain deviation, so that the accuracy of a result of fusion positioning is reduced, and even a positioning result is unavailable.

Disclosure of Invention

The embodiment of the application provides a positioning processing method and device, electronic equipment and a computer readable storage medium, which can eliminate positioning deviation and further improve positioning precision.

The technical scheme of the embodiment of the application is realized as follows:

the embodiment of the application provides a positioning processing method, which comprises the following steps:

determining an observed position of the positioning target based on a carrier phase;

determining an identified position of the positioning target based on a road element identified from an image of a road environment and a position of the road element in a map;

determining a deviation between the observed position of the positioning target and the identified position of the positioning target based on the observed position of the positioning target and the identified position of the positioning target;

and correcting the observation position based on the deviation to obtain an updated observation position of the positioning target.

An embodiment of the present application provides a positioning processing apparatus, including:

the observation module is used for determining the observation position of the positioning target based on the carrier phase;

a positioning module for determining an identified position of the positioning target based on a road element identified from an image of a road environment and a position of the road element in a map;

a positioning and online deviation estimation module for determining a deviation between an observed position of the positioning target and an identified position of the positioning target based on the observed position of the positioning target and the identified position of the positioning target;

In the above scheme, the observation module is further configured to obtain a first carrier phase acquired by a reference station and a second carrier phase of the positioning target; performing difference calculation on the first carrier phase and the second carrier phase to obtain a vector taking the reference station as a starting point and the positioning target as an end point; and summing the vector and the first coordinate of the reference station, and determining the summation result as the observation position of the positioning target.

In the above solution, the positioning module is further configured to identify at least one road element from the image of the road environment, and determine a vector with the at least one road element as a starting point and the positioning target as an end point; acquiring a second coordinate of the at least one road element in the map; superimposing the vector with the at least one road element as a starting point and the positioning target as an end point on the basis of the second coordinate to determine a third coordinate of the positioning target on the map; and taking the third coordinate as the identification position of the positioning target.

In the above solution, the positioning and online deviation estimating module is further configured to determine a deviation between the observed position of the positioning target and the identified position of the positioning target under a constraint condition that a sum of a square of an observation error, a square of an observation initial error, and a square of a deviation initial error is minimum; wherein the observation error is a difference between an observed position of the positioning target and a first predicted position of the positioning target; the deviation initial error is the difference value between the deviation initial value and the deviation estimation initial value; the observation initial error is a difference value between an observation initial value and an observation estimation initial value; and the first predicted position of the positioning target is a positioning result obtained by fusing the observation position of the positioning target and the identification position of the positioning target.

In the above scheme, the positioning and online deviation estimating module is further configured to perform covariance normalization processing on the square of the observation error, the square of the observation initial error, and the square of the deviation initial error, respectively, so as to obtain the square of the observation error, the square of the observation initial error, and the square of the deviation initial error after the covariance normalization processing; and determining the deviation between the observed position of the positioning target and the identified position of the positioning target by taking the sum of the square of the observation error, the square of the observation initial error and the square of the deviation initial error after the covariance normalization processing as a constraint condition.

In the above scheme, the positioning and online deviation estimating module is further configured to obtain an observation error corresponding to a target time and each historical time before the target time, and sum squares of the observation errors corresponding to the target time and each historical time to obtain a square of the observation error at the first time; determining a corresponding value when a deviation between the observation position of the positioning target and the recognition position of the positioning target meets the following constraint condition: the sum of the square of the observation error, the square of the observation initial error, and the square of the deviation initial error at the first time is minimized.

In the above solution, the positioning and online deviation estimation module is further configured to determine a deviation between the observed position of the positioning target and the identified position of the positioning target under a constraint condition that a sum of a square of an observation error, a square of an observation initial error, a square of a deviation initial error, and a square of a motion error is minimum; wherein the observation error is a difference between an observed position of the positioning target and a first predicted position of the positioning target; the deviation initial error is the difference value between the deviation initial value and the deviation estimation initial value; the observation initial error is a difference value between an observation initial value and an observation estimation initial value; the motion error is a difference between an observed position of the positioning target and a second predicted position of the positioning target; the second predicted position is a predicted position at a second time, and is predicted according to motion information and an observed position of a historical time at the second time.

In the foregoing solution, the positioning processing apparatus provided in this embodiment of the present application further includes:

and the early warning module is used for generating early warning information when the deviation between the observation position of the positioning target and the identification position of the positioning target exceeds a deviation threshold value so as to prompt that the obtained updated observation position of the positioning target is inaccurate.

In the above scheme, the early warning module is further configured to determine that the map is inaccurate when a deviation between the observed position of the positioning target and the identified position of the positioning target exceeds a deviation threshold; and updating the map by taking the condition that the deviation between the observation position of the positioning target and the identification position of the positioning target is smaller than the deviation threshold value as a constraint condition.

An embodiment of the present application provides an electronic device, including:

a memory for storing executable instructions;

and the processor is used for realizing the positioning processing method provided by the embodiment of the application when the processor executes the executable instructions stored in the memory.

The embodiment of the present application provides a computer-readable storage medium, which stores executable instructions and is used for implementing the positioning processing method provided by the embodiment of the present application when being executed by a processor.

The embodiment of the application has the following beneficial effects:

the observation position of the positioning target is corrected based on the deviation between the observation position of the positioning target and the recognition position of the positioning target to obtain the updated observation position of the positioning target, the observation position of the positioning target can be corrected through the deviation between the high-precision map and the carrier phase to eliminate the influence of the deviation on high-precision positioning, and the positioning precision is improved.

Drawings

Fig. 1 is a schematic diagram of an architecture of a positioning processing system 100 according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of a terminal 400 provided in an embodiment of the present application;

fig. 3A is a schematic flowchart of a positioning processing method according to an embodiment of the present application;

fig. 3B is a schematic flowchart of a positioning processing method according to an embodiment of the present application;

fig. 3C is a schematic flowchart of a positioning processing method according to an embodiment of the present application;

fig. 4 is a schematic view of an application scenario of a positioning processing method provided in an embodiment of the present application;

fig. 5 is a schematic diagram illustrating a comparison between a positioning result obtained by directly fusing a high-precision map and an RTK and a positioning result obtained by eliminating a bias between the high-precision map and the RTK by using the method provided by the embodiment of the present application.

Detailed Description

In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be described in further detail with reference to the accompanying drawings, the described embodiments should not be considered as limiting the present application, and all other embodiments obtained by a person of ordinary skill in the art without making creative efforts fall within the protection scope of the present application.

In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.

In the following description, references to the terms "first \ second \ third" are only to distinguish similar objects and do not denote a particular order, but rather the terms "first \ second \ third" are used to interchange specific orders or sequences, where appropriate, so as to enable the embodiments of the application described herein to be practiced in other than the order shown or described herein.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.

Before further detailed description of the embodiments of the present application, terms and expressions referred to in the embodiments of the present application will be described, and the terms and expressions referred to in the embodiments of the present application will be used for the following explanation.

1) The high-precision map is an electronic map with higher precision and more data dimensions. The precision is higher and is embodied at least to the centimeter level, and the data dimension embodies more road element position information. The high-precision map stores a large amount of driving assistance information as structured data, for example, road element position information such as lane lines, traffic signs, and the like.

2) A Real-time kinematic (RTK) carrier phase differential technique is a differential method for processing the observed quantity of carrier phases of two measuring stations in Real time, and the carrier phases collected by a reference station are sent to a user receiver for calculating the difference and the coordinates.

3) The automatic driving is a function of guiding and deciding a vehicle driving task without testing the physical driving operation performed by a driver, and replacing the test of the operation and control behavior of the driver to enable the vehicle to complete safe driving.

4) The least square method is a mathematical tool widely applied in the fields of data processing subjects such as error estimation, uncertainty, system identification and prediction, forecast and the like.

In current higher-level automatic driving or other high-precision vehicle-mounted positioning products, positioning is generally achieved by a visual fusion of a high-precision map and a Real-time kinematic (RTK) carrier differential technology (for example, positioning is achieved by directly fusing the high-precision map and the vision and an R TK through a filtering or optimization method). In a general fusion positioning algorithm, the global position (e.g., longitude and latitude) of a road element in a default high-precision map and the global observation longitude and latitude of the RTK are consistent, i.e., there is no deviation between the high-precision map and the RTK. However, in an actual application scenario, due to the influence of factors such as movement of the earth crust, position deviation of a Global Positioning System (GPS) base station, or clock synchronization, the position of the RTK and the position of an element in the high-precision map have a certain deviation, and if the deviation is not considered, the accuracy of the final fused Positioning result is reduced, and even the Positioning result is not usable.

It can be seen that the following problems are found in the related art in the implementation process of the present application:

1) the requirement on the consistency between the high-precision map and the RTK is high, and when the high-precision map deviates from the RTK due to untimely updating, the positioning precision is reduced, so that the automatic driving and other high-precision positioning products are greatly influenced.

2) The method for eliminating the inaccurate high-precision map can only be realized by the iterative update of the off-line high-precision map. Long-time data acquisition and map making and publishing are needed, the cost for realizing large-scale map updating is high, the updating frequency is slow, and the influence of deviation cannot be quickly eliminated in high-precision positioning.

In view of the above technical problems, embodiments of the present application provide a positioning processing method, an apparatus, an electronic device, and a computer-readable storage medium, which can eliminate a positioning deviation and further improve positioning accuracy, and an exemplary application of the positioning processing method provided in the embodiments of the present application is described below. In the following, an exemplary application will be explained when the electronic device is implemented as a terminal.

Referring to fig. 1, fig. 1 is a schematic structural diagram of a positioning processing system 100 according to an embodiment of the present application, in order to support a positioning processing application, a terminal 400 is connected to a server 200 through a network 300, where the network 300 may be a wide area network or a local area network, or a combination of the two.

The terminal 400 is configured to determine an observation position of a positioning target based on a carrier phase; determining an identified position of the positioning target based on a road element identified from an image of the road environment and a position of the road element in the map; the identified position and the observed position of the positioning target are sent to the server 200 for correction processing, so that the updated observed position of the positioning target returned by the server 200 is received and displayed in the client of the terminal 400 for the user to view the positioning related information.

The server 200 is configured to perform deviation determination and correction processing on the recognition position and the observation position of the positioning target sent by the receiving terminal 400 to obtain an updated observation position of the positioning target, and thus return the updated observation position to the receiving terminal 400.

In some embodiments, the terminal 400 implements the positioning processing method provided in the embodiments of the present application by running a computer program, where the computer program may be a native program or a software module in an operating system; can be a local (Native) Application program (APP), i.e. a program that needs to be installed in an operating system to run; or may be an applet, i.e. a program that can be run only by downloading it to the browser environment; but also instant messenger applets that can be embedded in any APP. In general, the computer programs described above may be any form of application, module, or plug-in.

In some embodiments, the server 200 may be an independent physical server, may also be a server cluster or a distributed system formed by a plurality of physical servers, and may also be a cloud server that provides basic cloud computing services such as cloud services, a cloud database, cloud computing, cloud functions, cloud storage, a network service, cloud communication, middleware services, domain name services, security services, a CDN, and a big data and artificial intelligence platform. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the embodiment of the present application is not limited.

Next, a structure of an electronic device for implementing a positioning processing method according to an embodiment of the present application is described, and as described above, the electronic device according to the embodiment of the present application may be the terminal 400 in fig. 1. Referring to fig. 2, fig. 2 is a schematic structural diagram of a terminal 400 provided in an embodiment of the present application, where the terminal 400 shown in fig. 2 includes: at least one processor 410, memory 450, at least one network interface 420, and a user interface 430. The various components in the terminal 400 are coupled together by a bus system 440. It is understood that the bus system 440 is used to enable communications among the components. The bus system 440 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, however, the various buses are labeled as bus system 440 in fig. 2.

The Processor 410 may be an integrated circuit chip having Signal processing capabilities, such as a general purpose Processor, a Digital Signal Processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like, wherein the general purpose Processor may be a microprocessor or any conventional Processor, or the like.

The user interface 430 includes one or more output devices 431, including one or more speakers and/or one or more visual displays, that enable the presentation of media content. The user interface 430 also includes one or more input devices 432, including user interface components that facilitate user input, such as a keyboard, mouse, microphone, touch screen display, camera, other input buttons and controls.

The memory 450 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid state memory, hard disk drives, optical disk drives, and the like. Memory 450 optionally includes one or more storage devices physically located remote from processor 410.

The memory 450 includes either volatile memory or nonvolatile memory, and may include both volatile and nonvolatile memory. The nonvolatile memory may be a Read Only Memory (ROM), and the volatile memory may be a Random Access Memory (RAM). The memory 450 described in embodiments herein is intended to comprise any suitable type of memory.

In some embodiments, memory 450 is capable of storing data, examples of which include programs, modules, and data structures, or a subset or superset thereof, to support various operations, as exemplified below.

An operating system 451, including system programs for handling various basic system services and performing hardware-related tasks, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and handling hardware-based tasks;

a network communication module 452 for communicating to other computing devices via one or more (wired or wireless) network interfaces 420, exemplary network interfaces 420 including: bluetooth, wireless compatibility authentication (WiFi), and Universal Serial Bus (USB), and the like;

a presentation module 453 for enabling presentation of information (e.g., user interfaces for operating peripherals and displaying content and information) via one or more output devices 431 (e.g., display screens, speakers, etc.) associated with user interface 430;

an input processing module 454 for detecting one or more user inputs or interactions from one of the one or more input devices 432 and translating the detected inputs or interactions.

In some embodiments, the positioning processing device provided in the embodiments of the present application may be implemented in software, and fig. 2 illustrates the positioning processing device 455 stored in the memory 450, which may be software in the form of programs and plug-ins, and includes the following software modules: observation module 4551, location module 4552, location and online bias estimation module 4553 and early warning module 4554, which are logical and therefore can be arbitrarily combined or further split depending on the functions implemented. The functions of the respective modules will be explained below.

The positioning processing method provided by the embodiment of the present application may be executed by the terminal 400 or the server 200 in fig. 1 alone, or may be executed by the terminal 400 and the server 200 in fig. 1 in cooperation.

Next, a positioning processing method provided by the embodiment of the present application executed by the terminal 400 in fig. 1 is described as an example. Referring to fig. 3A, fig. 3A is a schematic flowchart of a positioning processing method provided in an embodiment of the present application, and will be described with reference to the steps shown in fig. 3A.

In step 101, an observed position of a positioning target is determined based on a carrier phase.

In some embodiments, the above-described determination of the observed position of the positioning target based on the carrier phase may be achieved by: acquiring a first carrier phase acquired by a reference station and a second carrier phase of a positioning target; calculating the difference of the first carrier phase and the second carrier phase to obtain a vector taking the reference station as a starting point and a positioning target as an end point; and summing the vector and the first coordinate of the reference station, and determining the summation result as the observation position of the positioning target.

For example, both the terminal device and the reference station of the Positioning target are provided with a Global Positioning System (GPS) receiver, and the GPS receiver in the terminal device and the GPS receiver in the reference station simultaneously track and observe the same navigation satellite. The three-dimensional coordinates of the reference station are known (for example, the thousand-searching position is self-established in the whole country and 2200+ Beidou foundation signal enhancement station), because the atmospheric error, the satellite error and the like carried by the reference station and the terminal equipment are the same, the carrier phase observed values of the reference station and the terminal equipment of the positioning target are subtracted, and the error between the reference station and the terminal equipment can be counteracted, therefore, the reference station sends the measured carrier phase observed values to the terminal equipment, and the terminal equipment subtracts the two carrier phase observed values to obtain a carrier phase observation equation; and then, resolving the carrier phase observation equation by a positioning resolving algorithm (such as a Kalman filtering method, a least square method and the like) to obtain a vector taking the coordinate of the reference station as a starting point and the coordinate of the terminal equipment as an end point, summing the vector and the first coordinate of the reference station, and determining a summation result as the observation position of the positioning target. Wherein the first coordinate is a three-dimensional coordinate of the reference station.

In step 102, an identified position of the positioning target is determined based on the road elements identified from the image of the road environment and the positions of the road elements in the map.

In some embodiments, at least one road element is identified from an image of a road environment, a vector with the at least one road element as a starting point and a positioning target as an end point is determined; acquiring a second coordinate of at least one road element in the map; superposing vectors with the at least one road element as a starting point and the positioning target as an end point on the basis of the second coordinate to determine a third coordinate of the positioning target on the map; and taking the third coordinate as the identification position of the positioning target.

For example, the terminal device is provided with an image sensor, the image sensor in the terminal device collects an image of a road environment in real time, the terminal device identifies at least one road element (for example, a traffic light near a positioning target) from the image of the road environment, a three-dimensional coordinate system is established based on the image of the road environment, and the coordinates of the positioning target in the three-dimensional coordinate system and the coordinates of the traffic light in the three-dimensional coordinate system are subjected to subtraction to obtain a vector taking the traffic light as a starting point and the positioning target as an end point; acquiring second coordinates of the traffic light in a map (such as a high-precision map), wherein the second coordinates are three-dimensional coordinates of road elements in the high-precision map; superposing vectors with a traffic light as a starting point and a positioning target as an end point on the basis of the second coordinate to determine a third coordinate of the positioning target on the high-precision map, wherein the third coordinate is a three-dimensional coordinate of the positioning target in the high-precision map; and taking the third coordinate as the identification position of the positioning target.

It should be noted that the road elements may include signs related to the road, such as various types of lane lines (e.g., white solid line lane lines, yellow dotted line lane lines, left side edge lane lines, right side edge guide lines), turn lines, stop lines, road edge lines in the road environment, and traffic signs (e.g., slow traffic signs, stop prohibition traffic signs, traffic speed limit signs), street lamps, traffic lights, etc. disposed beside the road or on the road. Therefore, the above-mentioned examples of lane lines should not be construed as limiting the embodiments of the present application. Further, the number of road elements may be plural, and in general, the number of road elements is positively correlated with the accuracy of the identified position of the positioning target, that is, the greater the number of road elements, the higher the accuracy of the identified position of the positioning target.

In step 103, a deviation between the observed position of the positioning target and the identified position of the positioning target is determined based on the observed position of the positioning target and the identified position of the positioning target.

In some embodiments, referring to fig. 3B, fig. 3B is a flowchart of a positioning processing method provided in an embodiment of the present application, and illustrates that step 103 in fig. 3A may be implemented by performing step 1031. The description will be made in conjunction with the respective steps.

In step 1031, the deviation between the observed position of the positioning target and the identified position of the positioning target is determined under the constraint that the sum of the square of the observation error, the square of the observation initial error, and the square of the deviation initial error is the minimum.

Wherein the observation error is a difference between the observed position of the positioning target and the first predicted position of the positioning target; the deviation initial error is the difference between the deviation initial value and the deviation estimation initial value; the observation initial error is a difference value between the observation initial value and the observation estimation initial value; the first predicted position of the positioning target is a positioning result obtained by fusing the observation position of the positioning target and the identification position of the positioning target.

For example, the deviation between the observed position of the positioning target and the identified position of the positioning target is determined according to the constraints when the sum of the squares of the observed errors, the squares of the observed initial errors, the squares of the deviation initial errors, and the squares of the deviation errors is minimal

The calculation formula (1) of (a) is:

wherein i is a natural number greater than zero; z is a radical of_iThe observation position of the positioning target is determined for the ith moment based on the carrier phase; h (x)_i,b_i) The positioning result at the ith moment is obtained by prediction, and the observation position and the recognition position of the positioning target at the ith moment are fused for prediction, namely the first prediction position; x is the number of₀Is an initial value of the deviation;

estimating an initial value for the deviation; b₀Is an observation initial value;

an initial value is estimated for the observation.

Wherein, h (x)_i,b_i) The observation model may be a geometric model, and the predicted observation position is based on a deviation between an observation position of the positioning target at the ith time and a recognition position of the positioning target at the ith time, for example, a deviation vector representing a deviation between the observation position of the positioning target at the ith time and the recognition position of the positioning target at the ith time is superimposed on the observation position of the positioning target at the ith time to obtain the predicted observation position. The method can also be realized by a neural network model, the identification position of the positioning target and the observation position of the positioning target at the ith moment, the deviation between the identification position and the observation position of the positioning target and the updated observation position of the positioning target are used as sample characteristics to train the neural network model, and the first prediction position is obtained through the trained neural network model. x is the number of₀、b₀Is an initial value that is set in advance,

is a result predicted from the initial value.

In the embodiment of the application, only the observation error at the current moment is considered when determining the deviation between the observation position of the positioning target and the recognition position of the positioning target, so that the deviation between the observation position of the positioning target and the recognition position of the positioning target can be simply and conveniently determined.

In some examples, covariance normalization processing is performed on the square of the observation error, the square of the observation initial error, and the square of the deviation initial error, respectively, to obtain a covariance normalized observation error square, an observation initial error square, and a deviation initial error square; and determining the deviation between the observed position of the positioning target and the identified position of the positioning target by taking the sum of the square of the observation error, the square of the observation initial error and the square of the deviation initial error after the covariance normalization processing as a constraint condition.

Taking the example of a covariance normalization of the observed error, the covariance is used to describe the correlation between the observed position and the first predicted position, e.g. calculating the observed position z_iAnd a first predicted position h (x)_i,b_i) Of (c) is a covariance of_iAnd h (x)_i,b_i) Covariance of cov (z)_i，h(x_i,b_i) Is calculated as in (2)

Wherein k is a natural number greater than 1,

is z_iThe average value of (a) of (b),

is h (x)_i,b_i) Is measured. The covariance of the deviation initial value and the deviation estimation initial value, the covariance of the observation initial value and the observation estimation initial value, and the like can be obtained similarly.

According to the embodiment of the application, due to the fact that the unit of paired data such as the observed position and the first predicted position, the covariance of the deviation initial value and the deviation estimation initial value, and the observation initial value and the observation estimation initial value is not uniform, the amplitude influence caused by unit change between the observed position and the first predicted position is eliminated through the normalization processing of the covariance, and the correlation relation between the paired data is obtained through pure reaction.

In some examples, the observation errors corresponding to the target time and each historical time before the target time are obtained, and the squares of the observation errors corresponding to the target time and each historical time are added to obtain the square of the observation error at the first time; determining a corresponding value when the deviation between the observation position of the positioning target and the recognition position of the positioning target meets the following constraint conditions: the sum of the square of the observation error, the square of the observation initial error, and the square of the deviation initial error at the first time is minimized.

For example, the observed error O corresponding to the target time I and each historical time before the target time I_IThe calculation formula (3) is:

the observation error O corresponding to the target time I and each of the historical times prior to the target time I_IThe squares of the observation errors corresponding to the target time I and each historical time before the target time I are summed to obtain the square of the observation error at the first time. The observation error at the first time is characterized by the observation error determined in consideration of all the historical times from the 0 th time to the I th time.

In the embodiment of the application, the observation errors at all times are considered when determining the deviation between the observation position of the positioning target and the identification position of the positioning target, so that the accuracy of determining the deviation between the observation position of the positioning target and the identification position of the positioning target is higher.

In some embodiments, the deviation may also be determined from the motion information. And determining the deviation between the observed position of the positioning target and the identified position of the positioning target by taking the sum of the square of the observation error, the square of the observation initial error, the square of the deviation initial error and the square of the motion error as a constraint condition.

Wherein the observation error is a difference between the observed position of the positioning target and a first predicted position of the positioning target; the deviation initial error is the difference between the deviation initial value and the deviation estimation initial value; the observation initial error is a difference value between the observation initial value and the observation estimation initial value; the motion error is a difference between the observed position of the positioning target and a second predicted position of the positioning target; the second predicted position is a predicted position at the second time, and is predicted from the motion information and the observed position at the historical time at the second time. The second time refers to a target time, and the historical time of the second time refers to the previous time of the target time.

For example, the deviation between the observed position of the positioning target and the identified position of the positioning target is determined with the constraint that the sum of the square of the observed error, the square of the observed initial error, the square of the deviation error, and the square of the motion error is minimal

The calculation formula (4) is:

wherein z is_iDetermining an observation position of a positioning target based on the carrier phase at the ith moment; h (x)_i,b_i) The positioning result is a predicted positioning result at the ith moment, and is predicted by fusing an observation position and an identification position of a positioning target at the ith moment, namely a first predicted position; x is the number of_iThe observation position of the positioning target updated at the ith moment; f (x)_i-1) For the motion prediction model, i.e. based on the motion information and the optimal positioning result x at the i-1 th moment_i-1Recursion is carried out to obtain the optimal positioning result x at the i-1 th moment_iI.e., the second predicted position, where the motion information may be an acceleration of the positioning target measured according to an Inertial Measurement Unit (IMU) and an angular velocity of the target vehicle measured by a gyro, so that the positioning point at the i-th time is predicted according to the acceleration and the angular velocity of the positioning target and the positioning point at the i-1 th time; x is the number of₀Is an initial value of the deviation;

estimating an initial value for the deviation; b is a mixture of₀Is an observation initial value;

an initial value is estimated for the observation.

In some embodiments, the deviation between the observed position of the positioning target and the identified position of the positioning target is determined, for example, based on a constraint that the sum of the square of the observed error, the square of the observed initial error, and the square of the deviation error is minimal at the first time instant

The calculation formula (5) is:

wherein z is_iIs the observed position of the positioning target by the RTK at the ith time. h (x)_i,b_i) Is a predicted positioning result at the ith time and is predicted by fusing the observed position and the recognized position of the positioning target at the ith time, namely a first predicted position h (x)_i,b_i) It can also be obtained by: taking the identification position of the positioning target and the observation position of the positioning target at the historical time (the historical time refers to all times before the ith time and includes the ith time) and the deviation between the identification position and the observation position of the positioning target and the updated observation position of the positioning target as sample characteristics, so as to train the neural network model,h (x) is obtained through prediction of a trained neural network model_i,b_i)。

In step 104, the observed position of the positioning target is corrected based on the deviation between the observed position of the positioning target and the identified position of the positioning target to obtain an updated observed position of the positioning target.

In some embodiments, the determined optimal deviation between the observed position of the positioning target and the identified position of the positioning target

And overlapping the observation position of the positioning target to obtain an updated observation position of the positioning target.

For example, assume that the observed position (i.e., three-dimensional coordinates) of the positioning target is (x)₁，y₁，z₁) Optimum deviation of

(i.e., vector) is (x)₂，y₂，z₂) The updated observed position (i.e. three-dimensional coordinates) of the positioning target is obtained as (x)₁+x₂，y₁+y₂，z₁+z₂)。

In the embodiment of the application, the observation position of the positioning target is corrected based on the deviation between the high-precision map and the observation position of the positioning target by the RTK, so that the updated observation position of the positioning target is obtained, and the accuracy of positioning the positioning target is improved.

In some embodiments, referring to fig. 3C, fig. 3C is a schematic flowchart of a positioning processing method provided in this embodiment, and shows that, after step 104 in fig. 3A, step 1041 to step 1043 may also be executed, which will be described with reference to each step.

In step 1041, when the deviation between the observed position of the positioning target and the identified position of the positioning target exceeds the deviation threshold, generating early warning information to indicate that the obtained updated observed position of the positioning target is inaccurate.

In some examples, the deviation threshold is used to represent whether the deviation between the observed position of the positioning target and the identified position of the positioning target exceeds a normal deviation range, and the early warning information may be prompt information such as inaccuracy of a high-precision map, so that the prompt information is displayed through the terminal device, a user can know that the updated observed position of the positioning target is inaccurate, and the user can conveniently perform corresponding operation of overcoming inaccuracy of the high-precision map.

In step 1042, the map is determined to be inaccurate when the deviation between the observed position of the positioning target and the identified position of the positioning target exceeds a deviation threshold.

In the embodiment of the application, the high-precision map of each place where the positioning target passes through and the deviation between the observation positions of the RTK to the positioning target can be rapidly detected and sensed, and for places with large deviation, the early warning of inaccurate high-precision map can be rapidly given, so that the detection cost is reduced.

In step 1043, the map is updated with the constraint that the deviation between the observed position of the positioning target and the identified position of the positioning target is smaller than the deviation threshold.

In some embodiments, for the deviation between the high-precision map and the observed position of the positioning target by the RTK, when the deviation between the observed position of the positioning target and the identified position of the positioning target is less than the deviation threshold, a new, more accurate version of the high-precision map is updated to reduce the deviation, thereby reducing the effect of the deviation on the high-precision positioning to improve the accuracy of the determined observed position of the positioning target.

Next, an exemplary application of the embodiment of the present application in a practical application scenario will be described. Taking a map navigation client as an example, acquiring a positioning request of a user for a target vehicle, and determining positions of the target vehicle in real time according to a sensing result (namely, a road element position identified from an image of a road environment) based on an image sensor (such as a camera) and a road element position in a high-precision map; determining the position of the target vehicle in real time and providing the deviation between the observed positions of the target vehicle in real time based on the carrier phase; and correcting the observation position of the target vehicle in real time based on the deviation so that the user can obtain the updated observation position of the target vehicle. According to the embodiment of the application, under the condition that a certain deviation exists between the high-precision map and the RTK positioning technology, accurate and stable high-precision positioning output can still be ensured; and the technology is used for rapidly detecting and sensing the deviation between the high-precision map of each place where the target vehicle passes and the RTK positioning technology, so that the early warning of the inaccurate high-precision map can be rapidly given for places with large deviation, and the detection cost is reduced.

Referring to fig. 4, fig. 4 is a schematic view of an application scenario of the positioning processing method provided in the embodiment of the present application. A specific implementation scenario of the positioning processing method provided in the embodiment of the present application will be described below with reference to fig. 4.

The positioning and online deviation estimation module has three inputs, including the RTK observation position of the target vehicle, the road element position information such as a lane line sensed by the image sensor, and the road element position in the high-precision map. And carrying out positioning and online deviation estimation through the three inputs to output an optimal positioning result of the target vehicle. The specific procedure of the positioning and online deviation estimation module is as follows, first, the position of the target vehicle is determined in real time based on the image sensing result (i.e. the road element identified from the image of the road environment) of the image sensor (e.g. camera, optical sensor, etc.) and the position of the road element in the high-precision map; secondly, acquiring an observation position of the target vehicle provided in real time based on the carrier phase, and determining the deviation between the position of the target vehicle and the observation position based on the carrier phase in real time; then, the observed position of the target vehicle is corrected based on the deviation between the position of the target vehicle and the observed position, and finally, the updated observed position of the target vehicle is output. The location and online bias estimation module may be implemented by:

the functions performed by the positioning and online bias estimation modules are converted into the following mathematical model,

the calculation formula (6) is:

wherein x is_0:kRepresenting the optimal positioning result (namely the updated observation position of the target vehicle) output by the positioning and online deviation estimation module at each time from the 0 th time to the k th time, b_0:kRepresents a deviation between the observed position of the target vehicle and a high-precision map (the high-precision map and the position of the target vehicle calculated from the image sensing result) at each of the times from 0 th to k-th, and z_1:kRepresents an observed position, u, of the target vehicle at each of the 1 st to k-th times RTK_0:k-1Representing motion information (e.g., acceleration and angular velocity of the target vehicle measured by the customary measurement unit when the customary measurement unit is used) at each of time 0 to time k-1.

It should be noted that the positioning and online deviation estimation module performs correction processing on the observed position of the target vehicle, and may consider the motion information, or may not consider the motion information. The deviation between the observation position of the target vehicle and the recognition position of the target vehicle calculated by the high-precision map and the image perception result can be determined only through the RTK at each time from the 1 st time to the kth time, and the observation position of the target vehicle is corrected by the RTK based on the deviation; the deviation between the observation position of the target vehicle, the recognition position of the target vehicle calculated by the high-precision map and the image sensing result, and the motion information may be determined by integrating the RTK, and the observation position of the target vehicle may be corrected by the RTK based on the deviation. Therefore, an estimation of the deviation between the observed position of the target vehicle and a high-precision map (the high-precision map and the position of the target vehicle calculated from the image sensing results) and an optimal positioning result (i.e., the updated observed position of the target vehicle) by the RTK can be obtained by the positioning and online deviation estimation module.

For the above problem of solving the optimization of the positioning result, the problem of solving the optimization of the positioning result can be equivalent to solving the least square optimization problem of equation (7) by using the derivation of the bayesian formula, as shown in equation (7):

wherein z is_iIs the observed position of the target vehicle at the i-th instant RTK. h (x)_i，b_i) The target vehicle identification method is a prediction result (namely a first prediction position) of an observation model, namely the observation model fuses an observation position of RTK on the target vehicle and an identification position of the target vehicle obtained by calculation of a high-precision map and an image perception result, and a predicted positioning result. x is the number of_iThe observation position of the target vehicle updated for the ith time. f (x)_i-1) Is a motion prediction model, and the principle is that the optimal positioning result x at the i-1 th time is obtained according to the motion information_i-1Recursion is carried out to obtain the optimal positioning result x at the i-1 th moment_i. It should be noted that the motion information here may be the acceleration of the target vehicle measured by an conventional Measurement Unit (IM U), or may be the angular velocity of the target vehicle measured by a gyro; x is the number of₀Is an initial value of deviation;

an initial value is estimated for the observation.

Wherein, R, Q, P₀And P_b0In order to perform the covariance normalization process, since the units of squares in the formula (7) are not uniform, the normalization process is performed by covariance operation.

In some embodiments, each time new RTK input data is received, the positioning and online bias estimation module reconstructs an optimization problem in equation (6) above, which is equivalent to the least squares optimization problem in equation (7), to obtain the current optimal bias

Then the deviation is compared with RThe TK positioning results are superposed to obtain the optimal positioning result

In some embodiments, z_iThe observed position of the road element by the image sensor (i.e. the image perception) may also be the ith time. At this time, h (x)_i，b_i) The predicted result (i.e., the first predicted position) of the observation model is the recognition position of the road element predicted by the observation model based on the high-precision map and the observation position of the road element by the image sensor at the i-th time. x is the number of_iThe observed position of the road element updated for the ith time. f (x)_i-1) Is a motion prediction model, and since motion is relative, the principle is the same as the motion prediction model for the observed position of the RTK in equation (7). x is the number of₀Is an initial value of deviation;

an initial value is estimated for the observation.

Therefore, the current optimal deviation is obtained based on the input data of the image sensing result; since the road element and the target vehicle are corresponding, the deviation of the road element is the deviation of the target vehicle, and the observed position of the RTK on the target vehicle is corrected based on the deviation to update the observed position of the target vehicle.

In the embodiment of the application, aiming at RTK input data or image sensor input data, the positioning and online deviation estimation module can output the current optimal deviation and the optimal positioning result in real time, and can eliminate the positioning deviation so as to improve the positioning precision.

In some examples, there are two common methods when solving the problem of optimization: one is a filtering method and one is an optimization method. When using the filtering method, the x and b predictions and their values that were optimal at the previous time are keptThe covariance. Recursion is made to the current observation time by motion information and then correction update is made with the current observation (e.g., Extended Kalman Filter (EKF) method). When the optimization method is used, the least square optimization problem can be constructed, and the optimal x is obtained by solving the least square optimization problem_iAnd b_iThe solution of (c). It should be noted that the least square optimization problem may only include some states (x) closest to the current time_iAnd b_i) X may be included at all times from the beginning to the present_iAnd b_iThe state of (c).

Referring to fig. 5, fig. 5 is a schematic diagram illustrating a comparison between a positioning result obtained by directly fusing a high-precision map and an RTK and a positioning result obtained by eliminating a bias between the high-precision map and the RTK by using the method provided by the embodiment of the present application. Wherein 501 is an RTK observation position (i.e., an observation position of an RTK on a target vehicle), 502 is a positioning point obtained by a direct fusion method in the related art, that is, an observation position of an RTK on a target vehicle and an identification position of the target vehicle calculated based on a high-precision map and an image sensing result are directly fused to obtain a positioning point, and 503 is a positioning point output by the positioning and online deviation estimation module provided in the embodiment of the present application, that is, an updated observation position obtained after the RTK observation position is corrected by using the positioning processing method provided in the embodiment of the present application; 504 and 505 are lane lines on the left and right sides of one lane in the high-precision map. It is worth mentioning that, since the real position of the target vehicle is close to the center of the lane in the lateral direction, it can be seen that the positioning point obtained by the positioning processing method provided by the embodiment of the present application is closer to the real position of the target vehicle.

It should be noted that, for the offset between the high-precision map and the RTK positioning technology (or other high-precision GPS), a new, more accurate high-precision map version can be continuously issued by continuously updating the high-precision map offline in a synchronous manner to continuously reduce the offset, so as to continuously reduce the influence of the offset on the high-precision positioning. The method can also be realized by continuously monitoring the deviation of different places in the high-precision map in other manual modes.

Continuing with the exemplary structure of the positioning processing device 455 provided by the embodiments of the present application implemented as software modules, in some embodiments, as shown in fig. 2, the software modules stored in the positioning processing device 455 of the memory 450 may include:

an observation module 4551 configured to determine an observation position of the positioning target based on a carrier phase; a positioning module 4552 configured to determine an identified position of the positioning target based on a road element identified from an image of a road environment and a position of the road element in a map; a location and online bias estimation module 4553, configured to determine a bias between the observed position of the positioning target and the identified position of the positioning target based on the observed position of the positioning target and the identified position of the positioning target; and correcting the observation position based on the deviation to obtain an updated observation position of the positioning target.

In the above scheme, the observation module 4551 is further configured to acquire a first carrier phase acquired by a reference station and a second carrier phase of the positioning target; performing difference calculation on the first carrier phase and the second carrier phase to obtain a vector taking the reference station as a starting point and the positioning target as an end point; and summing the vector and the first coordinate of the reference station, and determining the summation result as the observation position of the positioning target.

In the above solution, the positioning module 4552 is further configured to identify at least one road element from the image of the road environment, and determine a vector with the at least one road element as a starting point and the positioning target as an end point; acquiring a second coordinate of the at least one road element in the map; superimposing the vector with the at least one road element as a starting point and the positioning target as an end point on the basis of the second coordinate to determine a third coordinate of the positioning target on the map; and taking the third coordinate as the identification position of the positioning target.

In the above solution, the location and online bias estimation module 4553 is further configured to determine a bias between the observed position of the positioning target and the identified position of the positioning target under the constraint condition that a sum of a square of an observation error, a square of an observation initial error, and a square of a bias initial error is minimum; wherein the observation error is a difference between an observed position of the positioning target and a first predicted position of the positioning target; the deviation initial error is the difference value between the deviation initial value and the deviation estimation initial value; the observation initial error is a difference value between the observation initial value and the observation estimation initial value; and the first predicted position of the positioning target is a positioning result obtained by fusing the observation position of the positioning target and the identification position of the positioning target.

In the above solution, the positioning and online bias estimation module 4553 is further configured to perform covariance normalization processing on the square of the observation error, the square of the observation initial error, and the square of the bias initial error, respectively, so as to obtain the square of the observation error, the square of the observation initial error, and the square of the bias initial error after the covariance normalization processing; and determining the deviation between the observation position of the positioning target and the identification position of the positioning target by using the constraint condition that the sum of the square of the observation error, the square of the observation initial error and the square of the deviation initial error after the covariance normalization processing is minimum.

In the above solution, the location and online bias estimation module 4553 is further configured to obtain an observation error corresponding to a target time and each historical time before the target time, and sum squares of the observation errors corresponding to the target time and each historical time to obtain a square of an observation error at a first time; determining a corresponding value when a deviation between the observation position of the positioning target and the recognition position of the positioning target meets the following constraint condition: the sum of the square of the observation error, the square of the observation initial error, and the square of the deviation initial error at the first time is minimized.

In the above solution, the location and online bias estimation module 4553 is further configured to determine a bias between the observed position of the positioning target and the identified position of the positioning target under the constraint condition that a sum of a square of the observation error, a square of the observation initial error, a square of the biased initial error, and a square of the motion error is minimum; wherein the observation error is a difference between an observed position of the positioning target and a first predicted position of the positioning target; the deviation initial error is the difference value between the deviation initial value and the deviation estimation initial value; the observation initial error is a difference value between an observation initial value and an observation estimation initial value; the motion error is a difference between an observed position of the positioning target and a second predicted position of the positioning target; the second predicted position is a predicted position at a second time, and is predicted according to motion information and an observed position of a historical time at the second time.

In the foregoing solution, the positioning processing apparatus provided in this embodiment of the present application further includes: an early warning module 4554, configured to generate early warning information when a deviation between the observed position of the positioning target and the identified position of the positioning target exceeds a deviation threshold, so as to prompt that the obtained updated observed position of the positioning target is inaccurate.

Embodiments of the present application provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, so that the computer device executes the positioning processing method described in the embodiment of the present application.

Embodiments of the present application provide a computer-readable storage medium storing executable instructions, wherein the executable instructions, when executed by a processor, cause the processor to execute a positioning processing method provided by embodiments of the present application, for example, the positioning processing method as shown in fig. 3A, 3B, and 3C.

In some embodiments, the computer-readable storage medium may be memory such as FRAM, ROM, PROM, EP ROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or may be various devices including one or any combination of the above memories.

In some embodiments, executable instructions may be written in any form of programming language (including compiled or interpreted languages), in the form of programs, software modules, scripts or code, and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

By way of example, executable instructions may, but need not, correspond to files in a file system, and may be stored in a portion of a file that holds other programs or data, such as in one or more scripts in a hypertext Markup Language (H TML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code).

By way of example, executable instructions may be deployed to be executed on one computing device or on multiple computing devices at one site or distributed across multiple sites and interconnected by a communication network.

In summary, according to the embodiment of the present application, the observed position of the positioning target is corrected based on the deviation between the observed position of the positioning target and the identified position of the positioning target to obtain the updated observed position of the positioning target, and the deviation between the high-precision map and the RTK is considered, so that the observed position of the positioning target can be corrected by the deviation to eliminate the influence of the deviation on the high-precision positioning, thereby improving the positioning precision; since the observation position and the first prediction position are different from each other in unit, the covariance between the deviation initial value and the deviation estimation initial value, the amplitude influence between the observation initial value and the observation estimation initial value caused by unit change is eliminated through the normalization processing of the covariance, and the correlation relationship between the paired data is obtained through pure reaction; when the deviation between the observation position of the positioning target and the identification position of the positioning target is smaller than the deviation threshold value, a new and more accurate high-precision map version is updated and released to reduce the deviation, so that the influence of the deviation on high-precision positioning is reduced, and the accuracy of the determined observation position of the positioning target is improved.

The above description is only an example of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, and improvement made within the spirit and scope of the present application are included in the protection scope of the present application.

Claims

1. A method of positioning processing, comprising:

determining an observed position of the positioning target based on the carrier phase;

2. The method of claim 1, wherein determining the observed position of the positioning target based on the carrier phase comprises:

acquiring a first carrier phase acquired by a reference station and a second carrier phase of the positioning target;

performing difference calculation on the first carrier phase and the second carrier phase to obtain a vector taking the reference station as a starting point and the positioning target as an end point;

and summing the vector and the first coordinate of the reference station, and determining the summation result as the observation position of the positioning target.

3. The method of claim 1, wherein determining the identified location of the positioning target based on a road element identified from the image of the road environment and a location of the road element in a map comprises:

identifying at least one road element from the image of the road environment, and determining a vector taking the at least one road element as a starting point and the positioning target as an end point;

acquiring a second coordinate of the at least one road element in the map;

superposing the vector with the at least one road element as a starting point and the positioning target as an end point on the basis of the second coordinate to determine a third coordinate of the positioning target on the map;

and taking the third coordinate as the identification position of the positioning target.

4. The method of claim 1, wherein determining the deviation between the observed position of the positioning target and the identified position of the positioning target based on the observed position of the positioning target and the identified position of the positioning target comprises:

determining the deviation between the observed position of the positioning target and the identified position of the positioning target by taking the sum of the square of the observation error, the square of the observation initial error and the square of the deviation initial error as a constraint condition;

wherein the observation error is a difference between an observed position of the positioning target and a first predicted position of the positioning target; the deviation initial error is the difference value between the deviation initial value and the deviation estimation initial value; the observation initial error is a difference value between an observation initial value and an observation estimation initial value;

and the first predicted position of the positioning target is a positioning result obtained by fusing the observation position of the positioning target and the identification position of the positioning target.

5. The method of claim 4, wherein determining the deviation between the observed position of the positioning target and the identified position of the positioning target subject to the constraint of a least sum of a square of the observed error, a square of the observed initial error, and a square of the deviation initial error comprises:

respectively carrying out covariance normalization processing on the square of the observation error, the square of the observation initial error and the square of the deviation initial error to obtain the square of the observation error, the square of the observation initial error and the square of the deviation initial error after the covariance normalization processing;

and determining the deviation between the observed position of the positioning target and the identified position of the positioning target by taking the sum of the square of the observation error, the square of the observation initial error and the square of the deviation initial error after the covariance normalization processing as a constraint condition.

6. The method of claim 4, wherein determining the deviation between the observed position of the positioning target and the identified position of the positioning target subject to the constraint of a least sum of a square of the observed error, a square of the observed initial error, and a square of the deviation initial error comprises:

acquiring observation errors corresponding to a target moment and each historical moment before the target moment, and summing the squares of the observation errors corresponding to the target moment and each historical moment to obtain the square of the observation error at the first moment;

determining a corresponding value when a deviation between the observation position of the positioning target and the recognition position of the positioning target meets the following constraint condition: the sum of the square of the observation error, the square of the observation initial error, and the square of the deviation initial error at the first time is minimized.

7. The method of claim 1, wherein determining a deviation between the observed location of the positioning target and the identified location of the positioning target based on the observed location of the positioning target and the identified location of the positioning target comprises:

determining the deviation between the observed position of the positioning target and the identified position of the positioning target by taking the sum of the square of the observation error, the square of the observation initial error, the square of the deviation initial error and the square of the motion error as a constraint condition;

wherein the observation error is a difference between an observed position of the positioning target and a first predicted position of the positioning target; the deviation initial error is the difference value between the deviation initial value and the deviation estimation initial value; the observation initial error is a difference value between an observation initial value and an observation estimation initial value; the motion error is a difference between an observed position of the positioning target and a second predicted position of the positioning target;

the second predicted position is a predicted position at a second time, and is predicted according to motion information and an observed position of a historical time at the second time.

8. The method of claim 1, further comprising:

and when the deviation between the observation position of the positioning target and the identification position of the positioning target exceeds a deviation threshold value, generating early warning information to prompt that the obtained updated observation position of the positioning target is inaccurate.

9. The method of claim 8, further comprising:

determining that the map is inaccurate when a deviation between the observed location of the positioning target and the identified location of the positioning target exceeds a deviation threshold;

and updating the map by taking the condition that the deviation between the observation position of the positioning target and the identification position of the positioning target is smaller than the deviation threshold value as a constraint condition.

10. A positioning processing apparatus, comprising:

a positioning and online deviation estimation module for determining a deviation between the observed position of the positioning target and the identified position of the positioning target based on the observed position of the positioning target and the identified position of the positioning target;