A high-precision navigation positioning method and system fusing multi-source data

Abstract

The application provides a high-precision navigation positioning method and system for fusing multi-source data, and relates to the technical field of positioning and navigation, and comprises the following steps: acquiring multi-source data of an object to be positioned, wherein the multi-source data comprises GNSS observation data, inertial navigation system observation data, visual image data and laser radar data; constructing a state vector of the object to be positioned based on the multi-source data and an optimized true value difference algorithm; determining a measurement equation of the object to be positioned based on the state vector, the multi-source data and a frequency weighting factor algorithm; and determining navigation positioning parameters of the object to be positioned based on a Kalman filtering parameter estimation algorithm, the measurement equation and the state vector, wherein the navigation positioning parameters comprise position parameters, velocity parameters and attitude parameters, so that the technical problem of low navigation positioning calculation efficiency and accuracy of the existing high-precision navigation positioning method for fusing multi-source data is solved.

Description

Technical Field

[0001] This invention relates to the field of positioning and navigation technology, and in particular to a high-precision navigation and positioning method and system that integrates multi-source data. Background Technology

[0002] With the upgrading of the automotive industry, autonomous driving technology, as a direction for automotive development, has become a research hotspot. High-precision location information acquisition is a prerequisite for realizing autonomous driving. With the continuous development of technologies such as satellite navigation, inertial navigation, visual navigation, and laser navigation, the methods for acquiring high-precision location information are becoming increasingly diversified.

[0003] Satellite navigation technology achieves real-time navigation and positioning by receiving signals from four or more navigation satellites. However, in environments with multiple obstructions, signal loss can easily occur, making navigation and positioning impossible. Inertial navigation technology is unaffected by the environment and can perform continuous navigation and positioning, but over time, errors accumulate, severely affecting positioning accuracy. Visual navigation determines the attitude transfer matrix by measuring the displacement and rotation of feature points in adjacent keyframes, thereby achieving navigation and positioning. The efficient extraction of feature points affects the accuracy of real-time navigation and positioning. Laser navigation relies on the high-precision ranging information of lidar for real-time navigation and positioning, but its positioning accuracy is greatly affected by lighting conditions and the sparseness of point clouds.

[0004] No effective solutions have yet been proposed to address the above problems. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a high-precision navigation and positioning method and system that integrates multi-source data, so as to alleviate the technical problem of low navigation and positioning solution efficiency and accuracy of existing high-precision navigation and positioning methods that integrate multi-source data.

[0006] In a first aspect, embodiments of the present invention provide a high-precision navigation and positioning method that integrates multi-source data, comprising: acquiring multi-source data of an object to be positioned, wherein the multi-source data includes: GNSS observation data, inertial navigation system observation data, visual image data, and lidar data; constructing a state vector of the object to be positioned based on the multi-source data and an optimized truth difference algorithm; determining a measurement equation of the object to be positioned based on the state vector, the multi-source data, and a frequency weighting factor algorithm; and determining navigation and positioning parameters of the object to be positioned based on a Kalman filter parameter estimation algorithm, the measurement equation, and the state vector, wherein the navigation and positioning parameters include: position parameters, velocity parameters, and attitude parameters.

[0007] Furthermore, based on the multi-source data and the optimized truth difference algorithm, the state vector of the object to be located is constructed, including: determining the observation model corresponding to the multi-source data, wherein the observation model includes: GNSS observation model, inertial navigation system observation model, visual observation model and laser observation model; determining the truth values ​​of the navigation and positioning parameters based on the observation model and the multi-source data; and constructing the state vector of the object to be located based on the truth values ​​of the navigation and positioning parameters.

[0008] Furthermore, determining the true values ​​of the navigation and positioning parameters based on the observation model and the multi-source data includes: determining the first navigation and positioning parameters corresponding to the GNSS observation model, the visual observation model, and the laser observation model, respectively, based on the observation model and the multi-source data; determining the variances of the first navigation and positioning parameters corresponding to the GNSS observation model, the visual observation model, and the laser observation model, based on the first navigation and positioning parameters corresponding to the GNSS observation model, the visual observation model, and the laser observation model; and determining the first navigation and positioning parameter corresponding to the minimum value of the variances as the true value of the navigation and positioning parameters.

[0009] Furthermore, the state vector is x = [δP, δv, δR, δb]. a ,δb g ],in, δP represents the position error, P represents the true position, and P INS Here, δv is the inertial navigation position estimate, δv is the velocity error, and v is the true velocity. INS Here, δR is the inertial navigation velocity estimate, δR is the attitude error, and R is the true attitude. INS For inertial navigation attitude estimation, ln is the logarithmic mapping, () V δb is the vector corresponding to the antisymmetric matrix. a For accelerometer zero bias error, b a For true acceleration with zero bias, δb is the zero bias estimate of the accelerometer. g b represents the zero bias error of the gyroscope. g For a true gyroscope with zero bias, This is the zero-bias estimate for the gyroscope.

[0010] Furthermore, based on the state vector, the multi-source data, and the frequency weighting factor algorithm, the measurement equation of the object to be located is determined, including: calculating the frequency weighting number based on the sampling frequency of the multi-source data; calculating the accuracy factor based on the multi-source data; calculating the weighted accuracy factor based on the frequency weighting number and the accuracy factor; and determining the measurement equation of the object to be located based on the weighted accuracy factor.

[0011] Furthermore, the navigation positioning parameters of the object to be located are matched with the road network information of the high-precision map to perform high-precision navigation for the object to be located.

[0012] Secondly, embodiments of the present invention also provide a high-precision navigation and positioning system that integrates multi-source data, comprising: an acquisition unit for acquiring multi-source data of an object to be positioned, wherein the multi-source data includes: GNSS observation data, inertial navigation system observation data, visual image data, and lidar data; a construction unit for constructing a state vector of the object to be positioned based on the multi-source data and an optimized truth difference algorithm; a determination unit for determining a measurement equation of the object to be positioned based on the state vector, the multi-source data, and a frequency weighting factor algorithm; and a positioning unit for determining navigation and positioning parameters of the object to be positioned based on a Kalman filter parameter estimation algorithm, the measurement equation, and the state vector, wherein the navigation and positioning parameters include: position parameters, velocity parameters, and attitude parameters.

[0013] Furthermore, the construction unit is configured to: determine the observation model corresponding to the multi-source data, wherein the observation model includes: a GNSS observation model, an inertial navigation system observation model, a visual observation model, and a laser observation model; determine the true values ​​of the navigation and positioning parameters based on the observation model and the multi-source data; and construct the state vector of the object to be located based on the true values ​​of the navigation and positioning parameters.

[0014] Thirdly, embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the method described in the first aspect above, and the processor is configured to execute the program stored in the memory.

[0015] Fourthly, embodiments of the present invention also provide a computer-readable storage medium on which a computer program is stored.

[0016] In this embodiment of the invention, multi-source data of the object to be located is acquired, including GNSS observation data, inertial navigation system observation data, visual image data, and lidar data. Based on the multi-source data and an optimized truth difference algorithm, a state vector of the object to be located is constructed. Based on the state vector, the multi-source data, and a frequency weighting factor algorithm, a measurement equation of the object to be located is determined. Based on a Kalman filter parameter estimation algorithm, the measurement equation, and the state vector, navigation and positioning parameters of the object to be located are determined, including position parameters, velocity parameters, and attitude parameters. This achieves the goal of high-precision and high-efficiency calculation of navigation and positioning parameters from fused multi-source data, thereby solving the technical problem of low efficiency and accuracy in existing high-precision navigation and positioning methods that fuse multi-source data. This results in improved accuracy and efficiency in high-precision navigation and positioning based on fused multi-source data.

[0017] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a high-precision navigation and positioning method that integrates multi-source data, provided in an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of a high-precision navigation and positioning method system that integrates multi-source data, provided by an embodiment of the present invention.

[0022] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1:

[0025] According to an embodiment of the present invention, a high-precision navigation and positioning embodiment that integrates multi-source data is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0026] Figure 1 This is a flowchart illustrating high-precision navigation and positioning based on multi-source data according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0027] Step S102: Obtain multi-source data of the object to be located, wherein the multi-source data includes: GNSS observation data, inertial navigation system observation data, visual image data, and lidar data;

[0028] Step S104: Based on the multi-source data and the optimized truth difference algorithm, construct the state vector of the object to be located;

[0029] Step S106: Based on the state vector, the multi-source data, and the frequency weighting factor algorithm, determine the measurement equation of the object to be located;

[0030] Step S108: Based on the Kalman filter parameter estimation algorithm, the measurement equation, and the state vector, determine the navigation and positioning parameters of the object to be located, wherein the navigation and positioning parameters include: position parameters, velocity parameters, and attitude parameters.

[0031] In this embodiment of the invention, multi-source data of the object to be located is acquired, including GNSS observation data, inertial navigation system observation data, visual image data, and lidar data. Based on the multi-source data and an optimized truth difference algorithm, a state vector of the object to be located is constructed. Based on the state vector, the multi-source data, and a frequency weighting factor algorithm, a measurement equation of the object to be located is determined. Based on a Kalman filter parameter estimation algorithm, the measurement equation, and the state vector, navigation and positioning parameters of the object to be located are determined, including position parameters, velocity parameters, and attitude parameters. This achieves the goal of high-precision and high-efficiency calculation of navigation and positioning parameters from fused multi-source data, thereby solving the technical problem of low efficiency and accuracy in existing high-precision navigation and positioning methods that fuse multi-source data. This results in improved accuracy and efficiency in high-precision navigation and positioning based on fused multi-source data.

[0032] In this embodiment of the invention, step S104 includes the following steps:

[0033] Step S201: Determine the observation model corresponding to the multi-source data, wherein the observation model includes: GNSS observation model, inertial navigation system observation model, visual observation model and laser observation model;

[0034] Step S202: Based on the observation model and the multi-source data, determine the true values ​​of the navigation and positioning parameters;

[0035] Step S203: Based on the true values ​​of the navigation and positioning parameters, construct the state vector of the object to be located.

[0036] Specifically, in this embodiment of the invention, step S202 includes the following steps:

[0037] Based on the observation model and the multi-source data, the first navigation and positioning parameters corresponding to the GNSS observation model, the visual observation model and the laser observation model are determined respectively;

[0038] Based on the first navigation and positioning parameters corresponding to the GNSS observation model, the visual observation model, and the laser observation model, the variances of the first navigation and positioning parameters corresponding to the GNSS observation model, the visual observation model, and the laser observation model are determined.

[0039] The first navigation and positioning parameter corresponding to the minimum value of the variance is determined as the true value of the navigation and positioning parameter.

[0040] In this embodiment of the invention, after acquiring GNSS observation data, inertial navigation system observation data, visual image data, and lidar data, a state vector is constructed by optimizing the truth difference method.

[0041] First, determine the observation model for each navigation and positioning observation data:

[0042] GNSS phase observation model:

[0043]

[0044] In the formula, This is GNSS phase observation data; dt is the geometric distance from the receiver to the satellite; c is the speed of light in a vacuum; dt r dt represents the receiver clock bias. s λ is the satellite clock bias; λ is the carrier wavelength. For carrier phase integer ambiguity; b r For the uncorrected phase hardware delay at the receiver end; b s This refers to the uncorrected phase hardware delay at the satellite end. This is the ionospheric delay. This is the tropospheric delay. This is carrier phase observation noise.

[0045] Inertial navigation observation model:

[0046] Attitude differential equation:

[0047]

[0048] In the formula, for The antisymmetric matrix; The attitude matrix is ​​in quaternion form, rotated from the carrier coordinate system to the Earth-centered Earth-fixed system. The gyroscope outputs angular velocity. for The conjugate quaternion, This is the Earth's rotational angular velocity.

[0049] Velocity differential equation:

[0050]

[0051] In the formula, The specific force output by the accelerometer; v e g is the ground speed; e This is the gravity vector in the geocentric-geocentric coordinate system.

[0052] Position differential equation:

[0053]

[0054] Laser observation model:

[0055]

[0056]

[0057] In the formula, y R,k y P,k These are the attitude increment and the position increment, respectively. The optimal attitude estimate at time k-1; R k Let κ be the pose matrix at time k; R,k The noise level is the attitude increment observation; Δt is the time increment; P k The position observation at time k; η is the optimal position estimate at time k-1; P,k is the position increment observation noise; exp() is the exponential mapping; ()^ is the antisymmetric matrix of the vector.

[0058] Visual observation model:

[0059] The attitude and position models in the visual observation model are consistent with those in the laser observation model, while the velocity model is:

[0060]

[0061] In the formula, y v,k For velocity increment; v k The velocity observation at time k; γ v,k This is noise from velocity increment observations.

[0062] Then, based on the GNSS phase observation model, laser observation model, and visual observation model, the velocity, position, and attitude corresponding to each observation are solved as v. s P s R s Where s = (GNSS, LADIR, VO), v s P represents the velocity corresponding to the GNSS phase observation model, laser observation model, and visual observation model. s R represents the position of the velocity corresponding to the GNSS phase observation model, laser observation model, and visual observation model. s Given the attitudes of the GNSS phase observation model, laser observation model, and visual observation model, solve for the corresponding variance D. s The meaning of 's' is the same as above. By taking the velocity, position, and attitude corresponding to the minimum variance as the true velocity, position, and attitude (i.e., the true values ​​of navigation and positioning parameters), the true values ​​can be determined quickly and effectively.

[0063] Finally, after determining the true values ​​of the navigation and positioning parameters, a state vector is constructed using position error, velocity error, attitude error, accelerometer bias error, and gyroscope bias error. The key lies in determining the true values. Optimization is performed among parameter values ​​calculated from various observation data to select the values ​​that are closer to the actual true values. This effectively reduces state vector errors and improves time update accuracy. The state vector can be represented as: x=[δP,δv,δR,δb] a ,δb g ],in, δP represents the position error; P represents the true position; P INS δv is the inertial navigation position estimate; v is the velocity error; v is the true velocity; v INS δR is the inertial navigation velocity estimate; δR is the attitude error; R is the true attitude; R INS For inertial navigation attitude estimation; ln is the logarithmic mapping; () V The vector corresponding to the antisymmetric matrix; δb a b is the accelerometer zero bias error; a The actual acceleration is zero bias; This is the accelerometer zero bias estimate; δb g b is the gyroscope's zero bias error; g This is a true gyroscope with zero bias; This is the zero-bias estimate for the gyroscope.

[0064] In this embodiment of the invention, step S106 includes the following steps:

[0065] The frequency weighting is calculated based on the sampling frequency of the multi-source data;

[0066] Based on the multi-source data, the accuracy factor is calculated;

[0067] The weighted precision factor is calculated based on the frequency weighting and the precision factor.

[0068] Based on the weighted accuracy factor, the measurement equation of the object to be located is determined.

[0069] In this embodiment of the invention, when using multi-source observation data for real-time navigation and positioning, inertial navigation data is used as the time update, while GNSS observation data, lidar data, and visual image data are used as the measurement update. The accuracy and stability of the measurement update significantly affect the final navigation and positioning accuracy. By determining the optimal measurement equation through the frequency-weighted accuracy factor method and performing the measurement update, the optimal accuracy and stability of the observation data can be selected, thereby suppressing the error drift of the time update and effectively improving the navigation and positioning accuracy. The main steps are as follows:

[0070] The first step is to calculate the frequency weighting:

[0071]

[0072] In the formula, β is the amplification factor; K w The sampling frequency of the observation data is w = (GNSS, LADIR, VO), which represents the sampling frequency of GNSS observation data, the sampling frequency of lidar observation data, and the sampling frequency of visual images, respectively; K0 is the reference frequency.

[0073] The second step is to calculate the precision factor:

[0074]

[0075] In the formula, For the precision factor, where These represent GNSS observation data, lidar observation data, and visual image observation data, respectively; n is the number of consecutive samples. For observation data, among which This is consistent with the meaning expressed above.

[0076] The third step is to determine the optimal measurement equation using a frequency-weighted accuracy factor:

[0077] Based on the frequency weighting factor calculated in the first step and the precision factor calculated in the second step, calculate the frequency weighting precision factor:

[0078]

[0079] In the formula, C υ υ is the frequency-weighted accuracy factor, where υ = (GNSS, LADIR, VO), representing GNSS observation data, lidar observation data, and visual image observation data, respectively.

[0080] Fourth, based on the frequency-weighted accuracy factor, the measurement equation corresponding to the observation data with the smallest frequency-weighted accuracy factor is taken as the measurement equation for navigation and positioning solution:

[0081]

[0082] In the formula, Let k be the observation vector at time k; Let k be the observation matrix at time k; Let k be the noise matrix at time k; Let ε be the observation noise at time k; ε = (GNSS, LADIR, VO), representing GNSS observation data, lidar observation data, and visual image observation data, respectively. ε is the observation data corresponding to the minimum frequency weighted accuracy factor.

[0083] The following is a detailed explanation of step S108.

[0084] Navigation and positioning parameters are estimated using the classical Kalman filter parameter estimation method based on the measurement equations and state vectors. The accuracy of the parameters calculated by the measurement equations directly affects the accuracy of the state vector correction. Optimizing the measurement equations using the frequency-weighted accuracy factor method and constructing a dynamic measurement equation Kalman filter for parameter estimation can effectively improve the final accuracy of the navigation and positioning parameters.

[0085] The detailed processing flow is as follows: using the inertial navigation observation model as the state equation, determine the state vector x at time k. k By adaptively determining the dynamic measurement equation, an effective observation z can be obtained. k , will x k and z k The input is fed into a Kalman filter, and the optimal estimate x is finally obtained. k Then iterates to time k+1 and repeats the above operation, continuously outputting navigation and positioning parameters as time progresses.

[0086] Finally, after determining the navigation and positioning parameters, the position, velocity, and attitude of the object to be located are output in real time, and the road network information of the high-precision map is matched with the navigation and positioning parameters in real time to correct the positioning parameters in real time, thereby achieving high-precision navigation.

[0087] Real-time high-precision navigation and positioning is a crucial condition for achieving autonomous driving. Currently, automobiles are equipped with various sensors such as GNSS, inertial navigation devices, LiDAR, and cameras for navigation and positioning. GNSS technology achieves real-time navigation and positioning by receiving signals from four or more navigation satellites, but in environments with multiple obstructions, signal loss can easily occur, leading to navigation and positioning failure. Inertial navigation technology is unaffected by the environment and can perform continuous navigation and positioning, but over time, errors accumulate, severely impacting positioning accuracy. Visual navigation determines the attitude transfer matrix by analyzing the displacement and rotation of feature points in adjacent keyframes, thereby achieving navigation and positioning; the efficient extraction of feature points affects the accuracy of real-time navigation and positioning. LiDAR navigation relies on high-precision ranging information from LiDAR for real-time navigation and positioning, but its positioning accuracy is significantly affected by lighting conditions and sparse point clouds. This invention proposes a high-precision navigation and positioning method and system that integrates multi-source data. Based on GNSS observation data, INS data, visual image data, and lidar data, a state vector is constructed using an optimized true value difference method. The optimal measurement equation is adaptively determined using a frequency-weighted accuracy factor method for measurement updates, ensuring the selection of observation data with optimal accuracy and stability, thereby suppressing error drift during time updates. A Kalman filter based on the dynamic measurement equation is constructed to solve navigation and positioning parameters in real time, significantly improving the accuracy of the solution. Finally, high-precision map information is used to correct the positioning parameters to achieve real-time high-precision navigation.

[0088] Example 2:

[0089] This invention also provides a high-precision navigation and positioning system that integrates multi-source data. This high-precision navigation and positioning system that integrates multi-source data is used to execute the high-precision navigation and positioning method that integrates multi-source data provided in the above-described embodiments of this invention. The following is a detailed description of the high-precision navigation and positioning system that integrates multi-source data provided in this invention.

[0090] like Figure 2 As shown, Figure 2 This is a schematic diagram of the high-precision navigation and positioning system that integrates multi-source data. The high-precision navigation and positioning system that integrates multi-source data includes:

[0091] The acquisition unit 10 is used to acquire multi-source data of the object to be located, wherein the multi-source data includes: GNSS observation data, inertial navigation system observation data, visual image data, and lidar data;

[0092] Construction unit 20 is used to construct the state vector of the object to be located based on the multi-source data and the optimized truth difference algorithm;

[0093] The determining unit 30 is used to determine the measurement equation of the object to be located based on the state vector, the multi-source data and the frequency weighting factor algorithm;

[0094] The positioning unit 40 is used to determine the navigation and positioning parameters of the object to be positioned based on the Kalman filter parameter estimation algorithm, the measurement equation and the state vector, wherein the navigation and positioning parameters include: position parameters, velocity parameters and attitude parameters.

[0095] In this embodiment of the invention, multi-source data of the object to be located is acquired, including GNSS observation data, inertial navigation system observation data, visual image data, and lidar data. Based on the multi-source data and an optimized truth difference algorithm, a state vector of the object to be located is constructed. Based on the state vector, the multi-source data, and a frequency weighting factor algorithm, a measurement equation of the object to be located is determined. Based on a Kalman filter parameter estimation algorithm, the measurement equation, and the state vector, navigation and positioning parameters of the object to be located are determined, including position parameters, velocity parameters, and attitude parameters. This achieves the goal of high-precision and high-efficiency calculation of navigation and positioning parameters from fused multi-source data, thereby solving the technical problem of low efficiency and accuracy in existing high-precision navigation and positioning methods that fuse multi-source data. This results in improved accuracy and efficiency in high-precision navigation and positioning based on fused multi-source data.

[0096] Example 3:

[0097] This invention also provides an electronic device, including a memory and a processor. The memory is used to store a program that supports the processor in executing the method described in Embodiment 1 above, and the processor is configured to execute the program stored in the memory.

[0098] See Figure 3 This invention also provides an electronic device 100, including: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected through the bus 62; the processor 60 is used to execute executable modules, such as computer programs, stored in the memory 61.

[0099] The memory 61 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 63 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0100] Bus 62 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0101] The memory 61 is used to store programs. After receiving an execution instruction, the processor 60 executes the program. The method executed by the device for defining the flow process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0102] Processor 60 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 60 or by instructions in software form. Processor 60 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 61. Processor 60 reads the information in memory 61 and, in conjunction with its hardware, completes the steps of the above method.

[0103] Example 4:

[0104] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the method described in Embodiment 1 above.

[0105] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0106] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0107] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0108] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0109] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0110] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A high-precision navigation positioning method for fusing multi-source data, characterized in that, The method comprises the following steps: acquiring multi-source data of an object to be positioned, wherein the multi-source data comprises GNSS observation data, inertial navigation system observation data, visual image data and laser radar data; constructing a state vector of the object to be positioned based on the multi-source data and an optimal true value difference algorithm; determining a measurement equation of the object to be positioned based on the state vector, the multi-source data and a frequency weighting factor algorithm, comprising: calculating a frequency weighting number based on a sampling frequency of the multi-source data; calculating an accuracy factor based on the multi-source data; calculating a weighted accuracy factor based on the frequency weighting number and the accuracy factor; and determining the measurement equation of the object to be positioned based on the weighted accuracy factor; determining navigation positioning parameters of the object to be positioned based on a Kalman filter parameter estimation algorithm, the measurement equation and the state vector, wherein the navigation positioning parameters comprise position parameters, velocity parameters and attitude parameters.

2. The method of claim 1, wherein, The method comprises the following steps: determining an observation model corresponding to the multi-source data, wherein the observation model comprises a GNSS observation model, an inertial navigation system observation model, a visual observation model and a laser observation model; determining navigation positioning parameter true values based on the observation model and the multi-source data; constructing a state vector of the object to be positioned based on the navigation positioning parameter true values.

3. The method of claim 2, wherein, The method comprises the following steps: determining first navigation positioning parameters corresponding to the GNSS observation model, the visual observation model and the laser observation model based on the observation model and the multi-source data; determining variances of the first navigation positioning parameters corresponding to the GNSS observation model, the visual observation model and the laser observation model based on the first navigation positioning parameters corresponding to the GNSS observation model, the visual observation model and the laser observation model; determining the first navigation positioning parameter corresponding to the minimum value in the variances as the navigation positioning parameter true values.

4. The method of claim 2, further comprising: The state vector is wherein, , is a position error, is a true position, is an inertial navigation position estimate, is a velocity error, is a true velocity, is an inertial navigation velocity estimate, is an attitude error, is a true attitude, is an inertial navigation attitude estimate, ln is a logarithmic map, is a vector corresponding to a skew-symmetric matrix, is an accelerometer bias error, is a true accelerometer bias, is an accelerometer bias estimate, is a gyroscope bias error, is a true gyroscope bias, is a gyroscope bias estimate.

5. The method of claim 1, wherein, matching the navigation positioning parameters of the object to be positioned with road network information of a high-precision map to perform high-precision navigation for the object to be positioned. The method comprises the following steps:

6. A high-precision navigation positioning system that fuses multi-source data, characterized in that, an acquisition unit configured to acquire multi-source data of an object to be positioned, wherein the multi-source data comprises GNSS observation data, inertial navigation system observation data, visual image data and laser radar data; a construction unit configured to construct a state vector of the object to be positioned based on the multi-source data and an optimal true value difference algorithm; ​ The determining unit is configured to determine a measurement equation of the object to be positioned based on the state vector, the multi-source data, and a frequency weighting factor algorithm, including: calculating a frequency weighting number based on a sampling frequency of the multi-source data; calculating a precision factor based on the multi-source data; calculating a weighted precision factor based on the frequency weighting number and the precision factor; and determining the measurement equation of the object to be positioned based on the weighted precision factor. The positioning unit is configured to determine a navigation positioning parameter of the object to be positioned based on a Kalman filtering parameter estimation algorithm, the measurement equation, and the state vector, where the navigation positioning parameter includes a position parameter, a velocity parameter, and an attitude parameter.

7. The system of claim 6, wherein, The constructing unit is configured to: determine an observation model corresponding to the multi-source data, where the observation model includes a GNSS observation model, an inertial navigation system observation model, a visual observation model, and a laser observation model; determine a true value of the navigation positioning parameter based on the observation model and the multi-source data; construct the state vector of the object to be positioned based on the true value of the navigation positioning parameter.

8. An electronic device, comprising: The computer program is configured to perform the steps of the method of any one of claims 1 to 5 when executed by the processor.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program is configured to perform the steps of the method of any one of claims 1 to 5 when executed by the processor.

Images