Digital road network aided inertial autonomous integrated navigation method and system based on index table

Abstract

This invention discloses a digital road network-assisted inertial autonomous navigation method based on an index table, relating to the field of inertial autonomous navigation in the aerospace strapdown inertial navigation technology. The method includes: acquiring a set of equally spaced road network data points based on high-precision road network location information and generating a road network index table; matching inertial navigation positioning points with the road network index table; and correcting navigation parameter errors based on navigation parameters output by the inertial navigation algorithm and the matching results between the inertial navigation positioning points and the road network index table. By combining road network location information with inertial navigation results for error correction, the accuracy of the inertial and odometry autonomous navigation system can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of inertial autonomous integrated navigation in the field of aerospace strapdown inertial navigation technology, and particularly relates to a digital road network assisted inertial autonomous integrated navigation method and system based on an index table. BACKGROUND

[0002] The strapdown inertial navigation system has the advantages of short reaction time, high reliability, good autonomy and all-weather application, and is widely used in military and civilian navigation fields such as aviation, aerospace and vehicle, and plays an important role in national defense and economic construction. The inertial navigation system can output carrier position, velocity, attitude and other information in real time, but it is difficult to overcome the drawback of navigation error accumulation over time. The odometer is a self-contained distance information measurement sensor, which can suppress the divergence of inertial navigation error when combined with the inertial navigation system, so that a high-precision autonomous positioning and orientation navigation system can be established by using the inertial and odometer integrated technology.

[0003] In the prior art, the inertial and odometer autonomous integrated navigation method can provide tens of meters of horizontal position accuracy and ten meters of elevation position accuracy, but the measurement error of the odometer is greatly affected by the environment, such as altitude, climate, temperature and humidity, road roughness, load, etc., which will affect the accuracy of the odometer measurement, thereby affecting the accuracy of the inertial and odometer autonomous integrated navigation system. SUMMARY

[0004] In view of the defects in the prior art, the purpose of the present application is to provide a digital road network assisted inertial autonomous integrated navigation method and system based on an index table, which can improve the accuracy of the inertial and odometer autonomous integrated navigation system.

[0005] To achieve the above purpose, the technical scheme adopted by the present application is:

[0006] On the one hand, the present application provides a digital road network assisted inertial autonomous integrated navigation method based on an index table, comprising:

[0007] According to the high-precision position information of the road network, a set of equally spaced road network data points is obtained, and a road network index table is generated;

[0008] The inertial navigation positioning point is matched with the road network index table;

[0009] According to the navigation parameters output by the inertial navigation algorithm and the matching result of the inertial navigation positioning point and the road network index table, the navigation parameter error is corrected.

[0010] In some optional schemes, according to the high-precision position information of the road network, a set of equally spaced road network data points is obtained, and a road network index table is generated, comprising:

[0011] According to the latitude and longitude of the high-precision combined navigation result of the actual road, the position information of the actual road is processed into data points at a set distance interval, and a road network data point set including road index values and data point index values is formed by adding data point index values;

[0012] A two-dimensional coordinate table is established at an inertial navigation maximum error distance interval within the latitude and longitude range determined by the road network data point set;

[0013] The coordinates in the table are matched with the road network data point set to obtain the data point index value of the nearest point of each coordinate point in the table and determine the corresponding road index value.

[0014] In some optional schemes, matching the inertial navigation positioning point with the road network index table includes:

[0015] Within the range of a circle with the inertial navigation positioning point as the center and the inertial navigation maximum error distance as the radius, coordinates in the two-dimensional table in the road network index table are matched;

[0016] According to the road index value corresponding to the coordinate point in the range, a to-be-selected road is determined;

[0017] Points in the to-be-selected road that are less than the inertial navigation maximum error distance from the positioning point are taken as to-be-selected points, and all to-be-selected points form a to-be-selected point set;

[0018] For a to-be-selected point, the trajectory data of the inertial navigation set mileage is compared with the road network data to determine the azimuth angle function of the trajectory data of the inertial navigation set mileage and the road network data;

[0019] All to-be-selected points in the to-be-selected point set in the to-be-selected road are traversed, and among the to-be-selected points in which the difference between the azimuth angle function of the trajectory data of the inertial navigation set mileage and the road network data is less than a set threshold, the to-be-selected point with the smallest difference between the azimuth angle functions is selected as a matching point.

[0020] In some optional schemes, characterized in that the azimuth angle function of the trajectory data of the inertial navigation set mileage and the road network data is:

[0021] wherein ψ INS is the azimuth angle function of the inertial navigation data, ψ MAP is the azimuth angle function of the road network data, Δλ INSi is the difference between the longitude of the i-th point and the i+1-th point in the inertial navigation data, ΔL INSi is the difference between the latitude of the i-th point and the i+1-th point in the inertial navigation data, Δλ MAPi is the difference between the longitude of the i-th point and the i+1-th point in the road network data, ΔL MAPi is the difference between the latitude of the i-th point and the i+1-th point in the road network data, i=1~n, and n is the number of data points in the set course.

[0022] In some alternative schemes, the threshold for the azimuth function difference is: |ψ INS -ψ MAP |≤0.5°.

[0023] In some alternative solutions, the feature is that, before matching the inertial navigation positioning point with the road network index table, the mileage already traveled by the inertial navigation positioning point is determined based on the inertial navigation positioning point, and matching is performed when the mileage already traveled by the inertial navigation positioning point reaches the minimum odometer requirement.

[0024] In some alternative schemes, navigation parameter errors are corrected based on the navigation parameters output by the inertial navigation algorithm and the matching results between the inertial navigation positioning points and the road network index table, including:

[0025] The number of inertial navigation pulses and the number of odometer pulses are collected in real time according to the preset sampling period. Inertial navigation calculation is performed to obtain the navigation parameters output by the inertial navigation algorithm. At the same time, the cumulative value of the inertial navigation displacement vector and the cumulative value of the odometer displacement vector under the carrier system are solved.

[0026] Establish the state differential equation based on the state vector;

[0027] The first measurement equation is established based on the odometer displacement increment and the inertial navigation displacement increment, and the second measurement equation is established based on the road network matching point and the inertial navigation positioning point.

[0028] Based on the established state differential equation and measurement equation, Kalman filtering is performed for inertial, odometer, and road network matching combined navigation to correct inertial navigation system parameter errors, odometer parameter errors, and device parameter errors in real time.

[0029] In some optional schemes, the number of inertial navigation (INS) pulses and the number of odometer pulses are collected in real time according to a preset sampling period. Inertial navigation calculations are then performed to obtain the navigation parameters output by the INS algorithm. Simultaneously, the cumulative values ​​of the INS displacement vector and the cumulative values ​​of the odometer displacement vector under the carrier system are calculated, including:

[0030] The distance increment within the sampling period is calculated based on the number of odometer pulses collected.

[0031] Based on the calculated distance increment, the odometer displacement vector for the current sampling period under the load system is obtained;

[0032] Calculate the cumulative value of the odometer displacement vector under the current sampling period in the system.

[0033] Calculate the inertial navigation displacement vector in the navigation system based on the inertial navigation velocity;

[0034] Based on the calculated inertial navigation displacement vector, the cumulative value of the inertial navigation displacement vector under the load system is calculated.

[0035] In some alternative schemes, based on the established state differential equations and measurement equations, Kalman filtering is performed for inertial, odometer, and road network matching combined navigation to correct inertial navigation system parameter errors, odometer parameter errors, and device parameter errors in real time, thereby achieving navigation data output, including:

[0036] Discretize the state differential equation to obtain the discrete form of the state equation, and then establish a Kalman filter;

[0037] Based on the state differential equation and the measurement equation, set the initial values ​​of the system noise matrix, the measurement noise matrix, the filter initial value, and the filter state error initial value;

[0038] Navigation calculations are performed in real time based on inertial navigation data and odometry data. Online matching is also performed, and the measured values ​​are input into a Kalman filter through measurement equations. After filtering and estimation, the estimated values ​​of each error state are obtained.

[0039] The estimated values ​​of each error state are used to correct the errors in the inertial navigation system parameters, odometer parameters, and device parameters, resulting in the corrected navigation parameters.

[0040] On the other hand, this solution provides a digital road network-assisted inertial autonomous integrated navigation system based on an index table, including:

[0041] The road network index module is used to obtain equally spaced road network data point sets based on high-precision location information of the road network and generate a road network index table.

[0042] The matching module is used to match inertial navigation positioning points with the road network index table;

[0043] The error correction module is used to correct navigation parameter errors based on the navigation parameters output by the inertial navigation algorithm and the matching results between the inertial navigation positioning points and the road network index table.

[0044] Compared with existing technologies, the advantages of this invention are as follows: This solution acquires equally spaced road network data point sets based on high-precision road network location information and generates a road network index table; it matches inertial navigation positioning points with the road network index table; and it corrects navigation parameter errors based on the navigation parameters output by the inertial navigation algorithm and the matching results between the inertial navigation positioning points and the road network index table. By combining road network location information with inertial navigation results for error correction, the accuracy of the inertial and odometer-based autonomous integrated navigation system can be improved. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating the digital road network-assisted inertial autonomous navigation method based on an index table in an embodiment of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0048] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0049] like Figure 1 As shown, on one hand, the present invention provides a digital road network-assisted inertial autonomous integrated navigation method based on an index table, comprising the following steps:

[0050] S1: Based on the high-precision location information of the road network, obtain a set of road network data points at equal intervals and generate a road network index table.

[0051] Step S1 specifically includes:

[0052] S11: Based on the latitude and longitude of the actual road high-precision integrated navigation results, the location information of the actual road is processed into data points at set intervals, and the data point index values ​​are added to form a road network data point set including road index values ​​and data point index values.

[0053] In this embodiment, the distance is set to 10m, and the road network data point set includes road indexes, point indexes, latitude, and longitude. After generating the road network data point set, the latitude and longitude values ​​are multiplied by 10. 5 The data type is changed from double to integer, so that the distance obtained from latitude and longitude is in units of 1m.

[0054] S12: Within the latitude and longitude range determined by the road network data point set, establish a two-dimensional coordinate table with the maximum error distance of inertial navigation as the interval.

[0055] In this embodiment, the maximum error distance of inertial navigation is 100m.

[0056] S13: Match the coordinates in the table with the road network data point set, obtain the data point index value of the nearest point of each coordinate point in the table, and determine the corresponding road index value.

[0057] In this embodiment, the distance between the coordinate point and the nearest point does not exceed 100m. If it exceeds 100m, the coordinate point is considered not to be on any road.

[0058] In this embodiment, after determining the road index table, a configuration file is generated, which includes: latitude and longitude range, number of roads, proximity point threshold, number of online points for initial road screening, number of online points for trajectory matching, inertial navigation error threshold, heading angle error threshold, rematch distance threshold, shortest matching mileage threshold, matching coefficient threshold, etc.

[0059] S2: Match the inertial navigation positioning points with the road network index table.

[0060] Step S2 specifically includes:

[0061] S21: Within the range of a circle centered on the inertial navigation positioning point and with the maximum inertial navigation error distance as the radius, match the coordinates in the two-dimensional table of the road network index table.

[0062] S22: Determine the road to be selected based on the road index value corresponding to the coordinate point within the range.

[0063] S23: Select points in the road to be selected whose distance from the positioning point is less than the maximum error distance of inertial navigation as points to be selected, and all points to be selected constitute the set of points to be selected.

[0064] S24: For a point to be selected, compare the trajectory data of the inertial navigation set mileage in front of the point with the road network data to determine the azimuth function of the trajectory data of the inertial navigation set mileage and the road network data.

[0065] In some optional embodiments, the azimuth function of the trajectory data and road network data for the inertial navigation set mileage is as follows:

[0066] Where, ψ INS Let ψ be the azimuth function of the inertial navigation data. MAP Let Δλ be the azimuth function of the road network data. INSi Let ΔL be the difference in longitude between point i and point i+1 in the inertial navigation data. INSi Let Δλ be the difference in latitude between point i and point i+1 in the inertial navigation data. MAPi Let ΔL be the difference in longitude between point i and point i+1 in the road network data. MAPi The difference in latitude between point i and point i+1 in the road network data, where i = 1 to n, and n is the number of data points within the set time period.

[0067] S25: Traverse the set of candidate points in all candidate roads, and select the candidate point with the smallest difference in azimuth function between the trajectory data of the inertial navigation set mileage and the road network data as the matching point if the difference is less than the set threshold.

[0068] In some optional embodiments, the threshold for the azimuth function difference is: |ψ INS -ψ MAP |≤0.5°.

[0069] In some optional embodiments, before matching the inertial navigation positioning point with the road network index table, the mileage already traveled by the inertial navigation positioning point is determined based on the inertial navigation positioning point. When the mileage already traveled by the inertial navigation positioning point reaches the minimum odometer requirement, matching is performed.

[0070] S3: Correct navigation parameter errors based on the navigation parameters output by the inertial navigation algorithm and the matching results between the inertial navigation positioning points and the road network index table.

[0071] Step S3 specifically includes:

[0072] S31: Collect the number of inertial navigation pulses and the number of odometer pulses in real time according to the preset sampling period, perform inertial navigation calculation, obtain the navigation parameters output by the inertial navigation algorithm, and simultaneously solve the cumulative value of the inertial navigation displacement vector and the cumulative value of the odometer displacement vector under the carrier system.

[0073] Step S31 specifically includes:

[0074] S311: Calculate the distance increment within the sampling period based on the number of odometer pulses collected.

[0075] In this embodiment, the calculation formula is:

[0076] Among them, among them, This represents the distance increment during the k-th sampling period. In this context, k represents the kth sampling period, k = 1 to a, a is the number of sampling periods, and N is the number of sampling periods. Odom K represents the number of odometer pulse outputs. Odom This is the odometer equivalent.

[0077] S312: Based on the calculated distance increment, obtain the odometer displacement vector for the current sampling period under the load system.

[0078] In this embodiment, in, The odometer displacement vector for the current sampling period under the load system.

[0079] S313: Calculate the cumulative value of the odometer displacement vector under the current sampling period in the system.

[0080] In this embodiment,

[0081] in, The cumulative value of the displacement vector of the odometer under the load system. This is the cumulative value of the displacement vector of the odometer under the load system in the previous sampling period.

[0082] S314: Calculate the inertial navigation displacement vector in the navigation system based on the inertial navigation velocity.

[0083] In this embodiment,

[0084] in, Let Δt be the inertial navigation displacement vector. s The sampling period is Δt. s In this context, 's' represents the sampling period count. Speed ​​under navigation system The speed in the navigation system during the current sampling period. The speed is the speed under the navigation system in the previous sampling period.

[0085] S315: Based on the calculated inertial navigation displacement vector, calculate the cumulative value of the inertial navigation displacement vector under the load system.

[0086] In this embodiment,

[0087] in, The cumulative value of the inertial navigation displacement vector under the load system. This represents the cumulative value of the inertial navigation displacement vector under the load system in the previous cycle. The attitude under the navigation system in the current cycle.

[0088] S32: Establish the state differential equation based on the state vector.

[0089] In this embodiment, the differential equation is: in, Here is the state differential equation, F(t) is the state transition matrix, w(t) is the system noise, and the state vector is:

[0090]

[0091] Where X is the state vector, φ is the attitude error state vector, and δv n Let δP be the velocity error state vector, and δP be the position error state vector. This is the gyroscope drift error vector. Let ξ be the accelerometer bias error vector. od =[δK od ,δα θ ,δα ψ ], δK od δα represents the odometer scale error. θ For pitch installation angle error, δα ψ The yaw rate is the heading installation angle error, and T is the Kalman filter discretization period.

[0092] In this embodiment, the system noise is specifically as follows:

[0093]

[0094] in, Let be the attitude cosine matrix in the k-th sampling period. This is random noise from the gyroscope. This is random noise from the accelerometer.

[0095] In this embodiment, the attitude error state vector is specifically φ = [φ E φ N φ U ] T , where φ E For eastward attitude error, φ N For the northward attitude error, φ U The attitude error is the upward direction; the velocity error state vector is specifically... in, For eastward velocity error, For northbound velocity error, The upward velocity error is represented by δP; the position error state vector is specifically δP = [δL δλ δh]. T Where δL is the latitude error, δλ is the longitude error, and δh is the altitude error; the gyroscope drift error vector is specifically... in, This refers to the x-axis gyroscope error. This refers to the y-axis gyroscope error. The z-axis gyroscope error; the accelerometer bias error vector is specifically... in, For x-axis accelerometer error, For y-axis accelerometer error, This represents the error of the z-axis accelerometer.

[0096] S33: Establish the first measurement equation based on the odometer displacement increment and the inertial navigation displacement increment, and establish the second measurement equation based on the road network matching point and the inertial navigation positioning point.

[0097] In some optional embodiments, the first measurement equation is: z1 = H1(t)X + v(t);

[0098] Where z1 is the first measurement equation, and H1(t) is the first measurement matrix, specifically: H1(t) = [O 3×15 I 3×3 O 3×3 v(t) represents the measurement noise, and t represents time.

[0099] The calculation method for the measured values ​​in the first measurement equation is as follows:

[0100] in, This is the first measured value. The cumulative value of the displacement vector of the odometer under the load system. This represents the cumulative value of the inertial navigation displacement vector under the load system.

[0101] The second measurement equation is: z2=H2(t)X+v(t);

[0102] Where z2 is the second measurement equation, and H2(t) is the second measurement matrix, specifically H2(t) = [O 3×6 I 3×3 O 3×12 ];

[0103] The measurement values ​​in the measurement equation are calculated as follows:

[0104] in, This is the second measurement value. The position value of the inertial navigation positioning point. This represents the location value of the road network matching point.

[0105] S34: Based on the established state differential equation and measurement equation, perform Kalman filtering for inertial, odometer, and road network matching combined navigation to correct inertial navigation system parameter errors, odometer parameter errors, and device parameter errors in real time.

[0106] Step S34 specifically includes:

[0107] S341: Discretize the state differential equation to obtain the discrete form of the state equation, and establish a Kalman filter.

[0108] In this embodiment, the discrete-form state equation is specifically: X k+1 =Φ k+1，k X k +W k ;in,

[0109] X kΦ is a discrete state vector. k+1，k For t k Time to t k+1 The state transition matrix at time W k Let w(τ) be the noise sequence of the discretization process, and t be the noise in continuous form. k Let F(t) represent the time at time k. k Let be the state transition matrix, T be the Kalman filter discretization period, τ be the time integral, and I be the identity matrix. The Kalman filter discretization period is preferably set to 0.1 seconds.

[0110] S342: Based on the state differential equation and the measurement equation, set the initial values ​​of the system noise matrix, the measurement noise matrix, the filter initial value, and the filter state error initial value.

[0111] S343: Performs navigation calculations in real time based on inertial navigation system data and odometer data, performs online matching, and inputs the measured values ​​into a Kalman filter through measurement equations. After filtering and estimation, the estimated values ​​of each error state are obtained.

[0112] In this embodiment, the estimated value of the error state includes the attitude error estimate. Speed ​​error estimate Position error estimate Estimated values ​​of odometer scale coefficient error and installation angle error

[0113] in, For latitude error, For longitude error, For height error, This is due to the odometer scale error. To account for installation error in pitch angle, This refers to the installation error of the heading angle.

[0114] S344: Use the estimated values ​​of each error state to correct the inertial navigation system parameter errors, odometer parameter errors, and device parameter errors to obtain the corrected navigation parameters.

[0115] In this embodiment, the system noise matrix Q k The initial values ​​of the measurement noise matrix, the initial filter value X0, and the initial filter state error are all initial values ​​for the Kalman filter. The initial state vector of the Kalman filter is set to 0, i.e., X o =0 32×1 .

[0116] The initial covariance matrix P of the Kalman filter o It is a diagonal matrix, and the diagonal elements are defined as shown in Table 1.

[0117] Table 1

[0118] Serial number Diagonal serial number Value 1. 1～3 [10 / 6378137 10 / 6378137 10]^2 2. 4～6 [0.1 0.1 0.1]^2 3. 7～9 [[300 300 300]*pi / 180 / 3600]^2 4. 10 (10*pi / 180 / 3600)^2 5. 11～13 [[20 20 20]*1.0e-6*g0]^2 6. 14～16 [[20 20 20]*1.0e-6]^2 7. 17～19 [[0.003 0.003 0.003]*pi / 180 / 3600]^2 8. 20～22 [[20 20 20]*1.0e-6]^2 9. 23～25 [0.003 0.003 0.003]^2 10. 26～28 [0.1 0.1 0.1]^2 11. 29～31 [2 2 2]^2 12. 32 (1.0e-4)^2

[0119] System noise matrix Q k Since it is a diagonal matrix, the initialization process is as follows:

[0120]

[0121]

[0122] Where Q is the noise matrix of the entire filtering system, Q 12 For Q 11 and Q 22 The diagonal matrix formed, Q 11 Let Q be the gyroscope noise matrix. 22 The noise matrix is ​​added, and RWC is a 6*1 vector, representing the velocity random walk coefficients of the three accelerometers. And the random walk coefficients of the angles of the 3 gyroscopes INSNoisePSD represents the power spectral density of the process noise of the device state, specifically: [2.66e-142.66e-142.66e-14000 5.87e-225.87e-225.87e-22 0 0 0]; aidNoisePSD represents the power spectral density of the process noise of the auxiliary navigation system, specifically: [1e-012 1e-020 1e-020 1e-012 1e-012 1e-012 0 0 0 0]. Measurement noise matrix R k It is a diagonal matrix, and the diagonal elements are shown in Table 2 below.

[0123] Table 2

[0124] Rk(1,1) (0.1)^2 Rk(2,2) (0.1)^2 Rk(3,3) (0.1)^2

[0125] The derivation and state vector update calculation process of the Kalman filter are as follows:

[0126] a: Calculate the state and predict in one step

[0127] b: Calculate the one-step prediction mean square error matrix P k / k-1 :

[0128] c: Calculate the filter gain matrix K k :

[0129] d: Calculate the optimal state estimate

[0130] e: Calculate the mean square error matrix P of the state estimate k :Pk =(IK k H k )P k / k-1 .

[0131] The obtained estimates are used to correct the errors in the inertial navigation system parameters, odometer parameters, and device parameters. The correction formula is as follows:

[0132] Among them, H k For the measurement matrix, R k To measure the noise matrix, These are the attitude matrix, velocity, and position obtained through real-time navigation calculations based on inertial navigation data and odometry data, respectively. This is the estimated attitude error value. This is the speed error estimate. This refers to the odometer scale coefficient. This refers to the odometer scale error. Latitude For accuracy, The height is indicated by the ∧ symbol added to each parameter, which represents the estimated value.

[0133] On the other hand, the present invention also provides a digital road network-assisted inertial autonomous integrated navigation system based on an index table, comprising:

[0134] The road network index module is used to obtain equally spaced road network data point sets based on high-precision location information of the road network and generate a road network index table.

[0135] The matching module is used to match inertial navigation positioning points with the road network index table;

[0136] The error correction module is used to correct navigation parameter errors based on the navigation parameters output by the inertial navigation algorithm and the matching results between the inertial navigation positioning points and the road network index table.

[0137] In summary, this invention acquires equally spaced road network data point sets based on high-precision road network location information and generates a road network index table; it then matches inertial navigation positioning points with the road network index table; and corrects navigation parameter errors based on the navigation parameters output by the inertial navigation algorithm and the matching results between the inertial navigation positioning points and the road network index table. By combining road network location information with inertial navigation results for error correction, the accuracy of the inertial and odometer-based autonomous integrated navigation system can be improved.

[0138] This method employs a space-for-time tradeoff strategy. To achieve real-time road network matching on embedded system platforms with limited computing power, a targeted road network matching algorithm was designed. First, a set of road network points is generated. Within a pre-determined latitude and longitude range, two-dimensional coordinates are established at specific intervals. All coordinates are traversed, and the road index value corresponding to each road coordinate point and the point index value of the nearest point on the road are calculated, i.e., an index table.

[0139] During navigation, the system calculates the most frequent road index value corresponding to the online navigation point based on the nearest point index table, thus determining the current road index and point index. Based on the road index and point index, the point set is expanded according to the error range to obtain the set of points to be matched. This set is then traversed, and an evaluation function is established using the azimuth change of adjacent points to find the offline point most similar to the navigation trajectory, i.e., the matching point. Finally, the position of the inertial autonomous navigation system is corrected based on the matching point, correcting position errors and improving the accuracy of the autonomous integrated navigation positioning.

[0140] In the description of this application, it should be noted that the terms "upper," "lower," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and 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, and therefore should not be construed as a limitation of this application. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" 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; they can refer to the internal communication between two elements. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0141] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0142] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A digital road network-assisted inertial autonomous integrated navigation method based on an index table, characterized in that, include: Based on the high-precision location information of the road network, obtain equally spaced sets of road network data points and generate a road network index table; Match the inertial navigation positioning points with the road network index table; Based on the navigation parameters output by the inertial navigation algorithm and the matching results between the inertial navigation positioning points and the road network index table, the navigation parameter errors are corrected. Based on high-precision location information of the road network, obtain equally spaced road network data point sets and generate a road network index table, including: Based on the latitude and longitude of the high-precision integrated navigation results of actual roads, the location information of actual roads is processed into data points at intervals of a set distance, and the data point index values ​​are added to form a road network data point set including road index values ​​and data point index values; Within the latitude and longitude range determined by the road network data point set, a two-dimensional coordinate table is established with the maximum error distance of inertial navigation as the interval; Match the coordinates in the table with the road network data point set, obtain the data point index value of the nearest point of each coordinate point in the table, and determine the corresponding road index value; Matching inertial navigation positioning points with the road network index table includes: Within the range of a circle centered on the inertial navigation positioning point and with the maximum inertial navigation error distance as the radius, match the coordinates in the two-dimensional table of the road network index table; The road to be selected is determined based on the road index value corresponding to the coordinate point within the range; Points in the road to be selected whose distance from the positioning point is less than the maximum error distance of inertial navigation are selected as points to be selected, and all points to be selected constitute the set of points to be selected; For a point to be selected, the trajectory data of the inertial navigation set mileage in front of the point is compared with the road network data to determine the azimuth function of the trajectory data of the inertial navigation set mileage and the road network data. Iterate through the set of candidate points in all candidate roads, and select the candidate point with the smallest difference in azimuth function between the trajectory data of the inertial navigation set mileage and the road network data as the matching point if the difference is less than the set threshold.

2. The digital road network-assisted inertial autonomous integrated navigation method based on an index table as described in claim 1, characterized in that, The azimuth function of the trajectory data and road network data for inertial navigation mileage setting is as follows: ; in, For inertial navigation data azimuth angle function, This is the azimuth function for road network data. For the first inertial navigation data Point and the The difference in longitude of the points For the first inertial navigation data Point and the The difference in latitude of the points The first in the road network data Point and the The difference in longitude of the points The first in the road network data Point and the The difference in latitude of the points , To set the number of data points within the process.

3. The digital road network-assisted inertial autonomous integrated navigation method based on an index table as described in claim 2, characterized in that, The threshold for the difference of azimuth angle functions is: .

4. The digital road network-assisted inertial autonomous integrated navigation method based on an index table as described in claim 1, characterized in that, Before matching the inertial navigation positioning point with the road network index table, the mileage already traveled by the inertial navigation positioning point is determined based on the inertial navigation positioning point. Matching is performed when the mileage already traveled by the inertial navigation positioning point reaches the minimum odometer requirement.

5. The digital road network-assisted inertial autonomous integrated navigation method based on an index table as described in claim 1, characterized in that, Based on the navigation parameters output by the inertial navigation algorithm and the matching results between the inertial navigation positioning points and the road network index table, the navigation parameter errors are corrected, including: The number of inertial navigation pulses and the number of odometer pulses are collected in real time according to the preset sampling period. Inertial navigation calculation is performed to obtain the navigation parameters output by the inertial navigation algorithm. At the same time, the cumulative value of the inertial navigation displacement vector and the cumulative value of the odometer displacement vector under the carrier system are solved. Establish the state differential equation based on the state vector; The first measurement equation is established based on the odometer displacement increment and the inertial navigation displacement increment, and the second measurement equation is established based on the road network matching point and the inertial navigation positioning point. Based on the established state differential equation and measurement equation, Kalman filtering is performed for inertial, odometer, and road network matching combined navigation to correct inertial navigation system parameter errors, odometer parameter errors, and device parameter errors in real time.

6. The digital road network-assisted inertial autonomous integrated navigation method based on an index table as described in claim 5, characterized in that, The inertial navigation system (INS) pulse count and odometer pulse count are collected in real time according to a preset sampling period. Inertial navigation calculations are performed to obtain the navigation parameters output by the INS algorithm. Simultaneously, the cumulative values ​​of the INS displacement vector and the odometer displacement vector under the onboard system are calculated, including: The distance increment within the sampling period is calculated based on the number of odometer pulses collected. Based on the calculated distance increment, the odometer displacement vector for the current sampling period under the load system is obtained; Calculate the cumulative value of the odometer displacement vector under the current sampling period in the system. Calculate the inertial navigation displacement vector in the navigation system based on the inertial navigation velocity; Based on the calculated inertial navigation displacement vector, the cumulative value of the inertial navigation displacement vector under the load system is calculated.

7. The digital road network-assisted inertial autonomous integrated navigation method based on an index table as described in claim 5, characterized in that, Based on the established state differential equation and measurement equation, Kalman filtering is performed for inertial, odometer, and road network matching combined navigation to correct inertial navigation system parameter errors, odometer parameter errors, and device parameter errors in real time, thereby achieving navigation data output, including: Discretize the state differential equation to obtain the discrete form of the state equation, and then establish a Kalman filter; Based on the state differential equation and the measurement equation, set the initial values ​​of the system noise matrix, the measurement noise matrix, the filter initial value, and the filter state error initial value; Navigation calculations are performed in real time based on inertial navigation data and odometry data. Online matching is also performed, and the measured values ​​are input into a Kalman filter through measurement equations. After filtering and estimation, the estimated values ​​of each error state are obtained. The estimated values ​​of each error state are used to correct the errors in the inertial navigation system parameters, odometer parameters, and device parameters, resulting in the corrected navigation parameters.

8. A navigation system based on an index table-assisted digital road network-assisted inertial autonomous integrated navigation method, characterized in that, include: The road network index module is used to obtain equally spaced road network data point sets based on high-precision location information of the road network and generate a road network index table. The matching module is used to match inertial navigation positioning points with the road network index table; The error correction module is used to correct navigation parameter errors based on the navigation parameters output by the inertial navigation algorithm and the matching results between the inertial navigation positioning points and the road network index table.

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