Online calibration method for odometer scale factor error in positioning and orienting system

Abstract

The invention provides an online calibration method for an odometer scale factor error in a positioning and orienting system. The method comprises the following steps: firstly, establishing an error model of the positioning and orienting system; then, according to the attitude information output by the strapdown inertial navigation system and the distance information output by the speedometer, the speed information of the carrier vehicle is obtained; thirdly, according to an error model of the positioning and orienting system, taking a speedometer scale factor error and the like as an error online estimation state, and constructing measurement of error online estimation by utilizing the obtained vehicle carrying speed information and vehicle carrying speed information output by the strapdown inertial navigation system; and finally, establishing a state equation and a measurement equation of online error estimation, estimating the system state, obtaining an estimated value of the scale factor error of the odometer, and realizing online calibration of the scale factor error of the odometer in the strapdown inertial navigation / odometer combined positioning and orientation system. Experimental results show that the method can automatically and effectively calibrate the scale factor error of the speedometer, and significantly improves the positioning and orientation precision of the strapdown inertial navigation / speedometer combination.

Description

Technical Field

[0001] The present invention relates to the technical field of combined positioning and orientation, and in particular to an online calibration method for an odometer scale factor error in a positioning and orientation system. Background Art

[0002] Military vehicles are typically equipped with onboard positioning and orientation systems, typically utilizing a combined strapdown inertial navigation (SINS) / odometer positioning and orientation system to achieve high-precision positioning and orientation while the vehicle is in motion. However, due to the influence of the external environment and the vehicle's own conditions, the odometer's scale factor typically varies significantly after factory calibration, resulting in a significant scale factor error. For example, factors such as tire inflation level, tire wear, ambient temperature, and road conditions can significantly affect the odometer's scale factor. This odometer's scale factor error significantly impacts the accuracy of the SINS / odometer combination positioning and orientation, necessitating high-precision online calibration of the odometer's scale factor error.

[0003] Existing methods for calibrating odometer scale factor errors typically involve pre-driving a vehicle along a fixed, pre-designed route. The odometer and strapdown inertial navigation system output data is recorded during the run, and an algorithm is then used to calculate an estimate of the odometer scale factor error. This method is simple and easy to implement, but requires the vehicle to be run before positioning and orientation to complete the odometer calibration. This method is clearly not conducive to the rapid response and maneuverability of military combat vehicles. Furthermore, as the vehicle travels, the ambient temperature and road conditions may change at any time, and the pre-calibrated odometer scale factor is likely to change significantly. Therefore, this method has limited adaptability to external environments and cannot be applied in a variety of environments. Summary of the Invention

[0004] In response to the shortcomings of the above-mentioned prior art, the present invention proposes an online calibration method for the odometer scale factor error in a positioning and orientation system. The method first performs error analysis and modeling on each sensor in the strapdown inertial navigation / odometer combined positioning and orientation system, then constructs a measurement using the output information of each sensor, and finally adopts an optimal estimation method based on Kalman filtering to perform real-time online estimation and calibration of the odometer scale factor error.

[0005] The present invention proposes an online calibration method for an odometer scale factor error in a positioning and orientation system, comprising the following steps:

[0006] Step 1: Construct the error model of the positioning and orientation system, including the strapdown inertial navigation system error model, the odometer error model, and the real-time velocity error model;

[0007] Step 2: Based on the error model of the positioning and orientation system, the state equation is established with the strapdown inertial navigation system error and the odometer scale factor error as the state of the error online estimation;

[0008] Step 3: Based on the attitude information output by the strapdown inertial navigation system and the distance information output by the odometer, the vehicle's speed information is calculated. Based on the obtained vehicle's speed information and the vehicle's speed output by the strapdown inertial navigation system, an online estimation error measurement is constructed to establish a measurement equation.

[0009] Step 4: Based on the obtained state equation and measurement equation, the Kalman filter is used to perform real-time estimation of the state vector X to obtain the estimated values ​​of the strapdown inertial navigation system error and the odometer scale factor error, thereby obtaining a real-time estimate of the odometer scale factor error, thereby realizing online calibration of the odometer scale factor error in the strapdown inertial navigation / odometer combined positioning and orientation system.

[0010] Furthermore, the error model of the strapdown inertial navigation system constructed in step 1 is:

[0011]

[0012] Among them, F INS is the error coefficient matrix, G INS is the noise driving array of the strapdown inertial navigation system, W INS is the strapdown inertial navigation system noise vector, X INS is the error vector of the strapdown inertial navigation system;

[0013] Where:

[0014]

[0015] Among them, φ E ,φ N ,φ U are the mathematical platform attitude errors of the strapdown inertial navigation system along the east, north and celestial directions respectively; δv E ,δv N ,δv U are the velocity errors of the strapdown inertial navigation system in the east, north and celestial directions respectively; δL, δλ, δh are the latitude, longitude and altitude errors of the strapdown inertial navigation system respectively; ε bx ,ε by ,ε bz are the gyro constant drift errors of the strapdown inertial navigation system installed along the x, y, and z axes of the vehicle coordinate system; are the constant biases of the accelerometers installed along the x, y, and z axes of the vehicle coordinate system in the strapdown inertial navigation system.

[0016] Furthermore, the odometry error model constructed in step 1 is:

[0017]

[0018] Where δK is the odometer scale factor error.

[0019] Furthermore, the steps of constructing the real-time velocity error model in step 1 include:

[0020] Step 1.1: Use the Northeast Sky geographic coordinate system as the positioning and orientation coordinate system, and select the right front upper coordinate system as the vehicle body coordinate system;

[0021] Step 1.2: Based on the odometer scale factor error δK, the vehicle speed in the vehicle coordinate system is obtained:

[0022]

[0023] Among them, v b Download the real speed of the vehicle in the vehicle coordinate system;

[0024] Step 1.3: Based on the vehicle attitude information output by the strapdown inertial navigation system, calculate the vehicle speed in the vehicle coordinate system using the following formula: Convert to the positioning and orientation coordinate system:

[0025]

[0026] in, The vehicle attitude matrix output by the strapdown inertial navigation system; Indicates the vehicle speed information in the positioning and orientation coordinate system;

[0027] Step 1.4: Rewrite the above equation based on the mathematical platform attitude error in the strapdown inertial navigation system as:

[0028]

[0029] Where φ is the mathematical platform attitude error, and φ=[φ E ,φ N ,φ U ] T ; Represents the true posture matrix of the vehicle;

[0030] Step 1.5: Expand the right side of the above equation and ignore the second-order small quantity related to the error, and we can get:

[0031]

[0032] Among them, v n Indicates the actual speed of the vehicle;

[0033] Step 1.6: Based on and v nThe error of real-time solution speed in positioning and orientation coordinate system is obtained by the following formula:

[0034]

[0035] in,

[0036] Step 1.7: After expansion, the real-time solution speed error equation can be obtained:

[0037]

[0038] Furthermore, the specific steps of step 2 include:

[0039] Step 2.1: Select the strapdown inertial navigation system error and the odometer scale factor error as the state of the online error estimation, and obtain the state vector X of the online error estimation:

[0040]

[0041] Step 2.2: Based on the error model of the positioning and orientation system established in step 1, construct the state equation for online error estimation:

[0042]

[0043] Among them, F is the state matrix, G is the system noise driving matrix, and W is the system noise vector.

[0044] Furthermore, the specific steps of step 3 include:

[0045] Step 3.1: Calculate the speed of the vehicle in the positioning and orientation coordinate system using the following formula:

[0046]

[0047] Among them, T D is the sampling time interval of the odometer; △S i For odometer in T D The distance traveled is increased by the internal measurement;

[0048] Step 3.2: Obtain the online estimation of the odometry scale factor error, Z, using the following formula:

[0049]

[0050] Among them, v′ n The vehicle speed actually output by the strapdown inertial navigation system;

[0051] Step 3.3: Error δv based on the output velocity of the strapdown inertial navigation systemn and the error in real-time solution speed Rewrite the above formula as:

[0052]

[0053] Among them, δv n =[δv E ,δv N ,δv U ] T ,

[0054] Step 3.4: Based on the above formula, express the measurement Z as:

[0055]

[0056] Step 3.5: Substitute the real-time velocity error equation obtained in step 1.7 into the above equation to obtain the measured Z:

[0057]

[0058] Step 3.6: Based on the state vector X of the online error estimation and the above formula, establish the measurement equation for the online error estimation:

[0059] Z=HX+V

[0060] Where H is the measurement matrix and V is the measurement white noise vector.

[0061] Therefore, the present invention adopts the above-mentioned online calibration method of the odometer scale factor error in the positioning and orientation system, which has the following beneficial effects:

[0062] The method proposed in the present invention constructs an error model for a strapdown inertial navigation / odometer combined positioning and orientation system by analyzing it, using the odometer scale factor error and other factors as the state for online error estimation and establishing a state equation. It then uses the vehicle attitude and speed information output by the strapdown inertial navigation system and the vehicle distance information output by the odometer to construct a measurement for online error estimation and establish a measurement equation. Furthermore, a Kalman filter is used to autonomously calibrate the odometer scale factor error online. This effectively addresses the issue of the odometer scale factor being significantly affected by the external environment and the vehicle's own conditions, which in turn affects the accuracy of the combined positioning and orientation system. This method does not rely on any external information or equipment and has the advantages of being easy to implement, having good environmental adaptability, strong autonomy, good anti-interference capabilities, and high calibration accuracy, making it suitable for widespread application.

[0063] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1Schematic diagram of the principle of online calibration of odometer scale factor error.

[0065] Figure 2 is the vehicle motion trajectory.

[0066] Figure 3 This is the online calibration result curve of the odometer scale factor error. DETAILED DESCRIPTION

[0067] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art will make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the application.

[0068] The vehicle-mounted positioning and orientation system targeted by the present invention is mainly composed of a strapdown inertial navigation system, an odometer and a positioning and orientation computer, and selects the northeastern sky geographic coordinate system as the positioning and orientation coordinate system (denoted as coordinate system x n y n z n , abbreviated as n system), select the right front upper coordinate system as the vehicle body coordinate system (recorded as coordinate system x b y b z b , abbreviated as b system). Among them, the strapdown inertial navigation system outputs the position, speed and attitude of the vehicle in the northeast sky geographic coordinate system in real time, and the odometer outputs the vehicle's position, speed and attitude in real time along the longitudinal axis y b Direction driving information.

[0069] Based on this, the present invention proposes an online calibration method for the odometer scale factor error in the positioning and orientation system, the principle diagram of which is as follows: Figure 1 As shown, the method includes the following steps:

[0070] Step 1: Construct the error model of the positioning and orientation system, including the strapdown inertial navigation system error model, the odometer error model, and the real-time solution of the velocity error equation;

[0071] Step 2: Based on the error model of the positioning and orientation system, the state equation is established with the strapdown inertial navigation system error and the odometer scale factor error as the state of the odometer scale factor error online estimation;

[0072] Step 3: Based on the attitude information output by the strapdown inertial navigation system and the distance information output by the odometer, the vehicle's speed information is calculated. Based on the obtained vehicle's speed information and the vehicle's speed output by the strapdown inertial navigation system, an online estimation error measurement is constructed to establish a measurement equation.

[0073] Step 4: Based on the obtained state equation and measurement equation, the Kalman filter is used to perform real-time estimation of the state vector X to obtain the estimated values ​​of the strapdown inertial navigation system error and the odometer scale factor error, thereby obtaining a real-time estimate of the odometer scale factor error, thereby realizing online calibration of the odometer scale factor error in the strapdown inertial navigation / odometer combined positioning and orientation system.

[0074] The key technologies of the present invention are introduced below:

[0075] 1. Error model of positioning and orientation system

[0076] For the strapdown inertial navigation / odometer combined positioning and orientation system, its error includes the strapdown inertial navigation system error and the odometer error. Among them, the error of the strapdown inertial navigation system is mainly caused by the measurement error of the inertial sensor gyroscope and accelerometer. Since the measurement error of the inertial sensor leads to the mathematical platform attitude error, velocity error and position error of the strapdown inertial navigation system, the strapdown inertial navigation system error usually includes gyroscope error, accelerometer error, mathematical platform attitude error, velocity error and position error. The specific model equations of the above errors are introduced in detail in many documents, such as "Qin Yongyuan. Inertial Navigation 3rd Edition) Beijing: Science Press 2020" and "Yan Gongmin, Weng Jun. Strapdown Inertial Navigation Algorithm and Integrated Navigation Principle (2nd Edition)", therefore, it will not be repeated in the present invention. Here, only the general form of the strapdown inertial navigation system error equation is given, as shown in the following formula (2).

[0077] (1) Error equation of strapdown inertial navigation system

[0078] The mathematical platform attitude errors of the strapdown inertial navigation system along the east, north and celestial directions are defined as φ E ,φ N ,φ U , the velocity errors along the east, north and celestial directions are δv E ,δv N ,δv U , the latitude, longitude and altitude errors are δL, δλ, δh respectively, and the gyro constant drift errors installed along the x, y, and z axes of the vehicle coordinate system are ε bx ,ε by ,ε bz The constant bias of the accelerometers installed along the x, y, and z axes of the vehicle coordinate system are Then the error vector X of the strapdown inertial navigation system is INS for:

[0079]

[0080] According to the specific model equation of the strapdown inertial navigation system error, the error vector X INS Satisfies the following differential equation:

[0081]

[0082] Among them, F INS is the error coefficient matrix, G INS is the noise driving array of the strapdown inertial navigation system, W INS is the strapdown inertial navigation system noise vector.

[0083] The specific model equation based on the strapdown inertial navigation system error can be directly written as F INS , G INS 、W INS The specific form is not described in detail in the present invention.

[0084] (2) Odometer scale factor error

[0085] As a distance measurement sensor with strong autonomy and good anti-interference performance, the odometer measures the distance traveled by counting the number of wheel axle rotations during vehicle driving. The odometer measurement error mainly comes from the scale factor error, which is usually affected by factors such as tire wear, inflation level, and external temperature. It can usually be regarded as a random constant during each driving process. Therefore, the odometer scale factor error δK satisfies:

[0086]

[0087] Due to the error of the odometer scale factor, the vehicle speed obtained by real-time solution in the vehicle coordinate system also has a certain error. Assume that the actual vehicle speed in the vehicle coordinate system is v b , the vehicle solution speed is Obviously, there is the following relationship between the two:

[0088]

[0089] Using the vehicle attitude information output by the strapdown inertial navigation system, the above equation can be transformed from the vehicle coordinate system to the positioning and orientation coordinate system. Let the vehicle attitude matrix output by the strapdown inertial navigation system be: Multiply both sides of the above formula by the left We can get:

[0090]

[0091] in, Indicates the vehicle speed information in the positioning and orientation coordinate system;

[0092] Since there is a mathematical platform attitude error φ (φ=[φ E ,φ N ,φ U ]T ), after introducing φ, the above formula is transformed into:

[0093]

[0094] in, Represents the true posture matrix of the vehicle;

[0095] Expand the right side of the above equation and ignore the second-order small quantity about the error, and we can get:

[0096]

[0097] Among them, v n Indicates the actual speed of the vehicle;

[0098] The error of real-time solution speed in the set orientation coordinate system is Obviously there is According to the above formula and after sorting and transformation, we can get:

[0099]

[0100] Since the positioning and orientation coordinate system is the northeast celestial geographic coordinate system, Therefore, expanding the above formula, we can get:

[0101]

[0102] The above formula is the real-time solution of the speed error equation.

[0103] 2. Online estimation filter based on Kalman filtering

[0104] When designing an odometer scale factor error online estimation filter, the present invention selects a strapdown inertial navigation system error and an odometer scale factor error as states for online error estimation based on a Kalman filtering method and an error model of a positioning and orientation system, and establishes a corresponding state equation. According to attitude information output by the strapdown inertial navigation system and distance information output by the odometer, speed information of a vehicle is obtained by solution, and a measurement for online error estimation is constructed using the vehicle speed obtained by solution and the vehicle speed output by the strapdown inertial navigation system, and a corresponding measurement equation is derived. Kalman filtering is then used to estimate the state of the online error estimation, thereby obtaining an estimated value of the odometer scale factor error, and further realizing online calibration of the odometer scale factor error.

[0105] (1) Online estimated state equation

[0106] According to the previous analysis, the strapdown inertial navigation system error and the odometer scale factor error are selected as the state of error online estimation, that is, the mathematical platform attitude error φ of the strapdown inertial navigation system along the east, north and celestial directions E ,φ N ,φU , velocity error δv along the east, north and celestial directions E ,δv N ,δv U , latitude, longitude and altitude errors δL, δλ, δh, gyro constant drift error ε installed along the x, y, z axes of the vehicle coordinate system bx ,ε by ,ε bz , the constant bias of the accelerometer installed along the x, y, and z axes of the vehicle coordinate system And the odometry scale factor error δK. Therefore, the online estimated state vector X is:

[0107]

[0108] Obviously, according to the error model of the positioning and orientation system, that is, formula (2), formula (3), and formula (9), it can be seen that the state vector X satisfies the following differential equation:

[0109]

[0110] Here, F is the state matrix, G is the system noise driving matrix, and W is the system noise vector.

[0111] The specific forms of the above matrices and vectors can be directly obtained by writing them out according to the error model equation of the positioning and orientation system.

[0112] (2) Measurement equation for online estimation

[0113] Based on the attitude information output by the strapdown inertial navigation system and the distance information output by the odometer, the speed information of the vehicle is calculated. The vehicle speed obtained by the calculation and the vehicle speed output by the strapdown inertial navigation system are used to construct an online estimation measurement of the error, and the corresponding measurement equation is derived.

[0114] Assume that the vehicle attitude matrix output by the strapdown inertial navigation system is The odometer is measured at each sampling time interval T D The measured distance increment is △S i , then the speed of the vehicle solution in the positioning and orientation coordinate system is for:

[0115]

[0116] Assume that the actual output speed of the strapdown inertial navigation system is v′ n , then the measurement Z for online estimation of the odometer scale factor error is:

[0117]

[0118] Among them, v′ nIt represents the actual vehicle speed output by the strapdown inertial navigation system. Due to the error in the output of the strapdown inertial navigation system, v′ n With v n The values ​​are different;

[0119] Assume that the error of the output velocity of the strapdown inertial navigation system is δv n , the error of the solution speed is Obviously, according to the above expression of the measurement vector Z:

[0120]

[0121] Among them, δv n =[δv E ,δv N ,δv U ] T ,

[0122] Therefore, the measurement Z can be expressed as:

[0123]

[0124] Substituting the real-time velocity error equation shown in equation (9) into the above equation, we can get

[0125]

[0126] At this time, combined with the online estimated state vector X, according to the above expression of measurement Z, the measurement equation for online error estimation can be written as:

[0127] Z=HX+V (17)

[0128] Where H is the measurement matrix, V is the measurement white noise vector;

[0129] The specific forms of the matrices and vectors in the above formula can be directly obtained by combining the online estimated state vector X.

[0130] Once the state equations and measurement equations for online estimation of the odometer scale factor error are obtained, a Kalman filter can be used to estimate the state vector X in real time based on the output information of the strapdown inertial navigation system and the odometer at each moment during the vehicle's driving process. This Kalman filter calculation provides real-time estimates of the state variables—the strapdown inertial navigation system error and the odometer scale factor error—and thus a real-time estimate of the odometer scale factor error. This allows for online calibration of the odometer scale factor error in a combined strapdown inertial navigation / odometer positioning and orientation system.

[0131] Example

[0132] In order to verify the effect of the method proposed in the present invention, the following simulation experiments were carried out.

[0133] Assume that in the vehicle-mounted strapdown inertial navigation / odometer combined positioning and orientation system, the strapdown inertial navigation gyro constant drift is 0.01° / h, and the random walk is The accelerometer constant error is 10 -4 g, the random walk is The odometer scale factor error is 0.5%; the initial alignment accuracy of the strapdown inertial navigation is 2', the azimuth accuracy is 5', the initial velocity error is 0m / s, and the initial position error is 10m; during the online calibration of the odometer scale factor error, the vehicle is always in motion, and there is no special requirement for the motion form. The calibration time is 600s, and the vehicle motion trajectory is as follows: Figure 2 Based on Figure 2 The vehicle trajectory shown in FIG. 1 is used to calibrate the scale factor error of the odometer in the positioning and orientation system using the online calibration method proposed in the present invention. The calibration results are shown in FIG. Figure 3 shown.

[0134] according to Figure 3 It can be seen that after 600 seconds of calibration, the odometer's scale factor error has been effectively calibrated, with a calibration result of approximately 0.52%. This result is very close to the true value of the odometer's scale factor error (0.5%), and the calibration rate reaches over 96%. Furthermore, the calibration results show that around the 100th second of calibration, the calibration results have already approached steady state, demonstrating that the calibration method proposed in this invention not only has high calibration accuracy but also fast calibration speed. In particular, this calibration method has no special requirements for the vehicle's motion mode. Whether the vehicle is moving at a constant speed, accelerating, decelerating, turning, climbing, or descending, it can achieve rapid and effective calibration of the odometer's scale factor error, making it very suitable for practical engineering applications.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. An online calibration method for the odometer scale factor error in a positioning and orientation system, characterized in that: The following steps are involved: Step 1: Construct the error model of the positioning and orientation system, including the strapdown inertial navigation system error model, the odometer error model, and the real-time velocity error model; Step 2: Based on the error model of the positioning and orientation system, the state equation is established with the strapdown inertial navigation system error and the odometer scale factor error as the state of the error online estimation; Step 3: Based on the attitude information output by the strapdown inertial navigation system and the distance information output by the odometer, the vehicle's speed information is calculated. Based on the obtained vehicle's speed information and the vehicle's speed output by the strapdown inertial navigation system, an online estimation error measurement is constructed to establish a measurement equation. Step 4: Based on the obtained state equation and measurement equation, the Kalman filter is used to perform real-time estimation of the state vector X to obtain the estimated values ​​of the strapdown inertial navigation system error and the odometer scale factor error, thereby obtaining a real-time estimate of the odometer scale factor error, thereby realizing online calibration of the odometer scale factor error in the strapdown inertial navigation / odometer combined positioning and orientation system.

2. The online calibration method for the odometer scale factor error in a positioning and orientation system according to claim 1, characterized in that: The error model of the strapdown inertial navigation system constructed in step 1 is: Among them, F INS is the error coefficient matrix, G INS is the noise driving array of the strapdown inertial navigation system, W INS is the strapdown inertial navigation system noise vector, X INS is the error vector of the strapdown inertial navigation system; Where: Among them, φ E ,φ N ,φ U are the mathematical platform attitude errors of the strapdown inertial navigation system along the east, north and celestial directions respectively; δv E ,δv N ,δv U are the velocity errors of the strapdown inertial navigation system in the east, north and celestial directions respectively; δL, δλ, δh are the latitude, longitude and altitude errors of the strapdown inertial navigation system respectively; ε bx ,ε by ,ε bz are the gyro constant drift errors of the strapdown inertial navigation system installed along the x, y, and z axes of the vehicle coordinate system; are the constant biases of the accelerometers installed along the x, y, and z axes of the vehicle coordinate system in the strapdown inertial navigation system.

3. The online calibration method for the odometer scale factor error in a positioning and orientation system according to claim 1, characterized in that: The odometry error model constructed in step 1 is: Where δK is the odometer scale factor error.

4. The online calibration method for the odometer scale factor error in a positioning and orientation system according to claim 3, characterized in that: The steps for constructing the real-time velocity error model in step 1 include: Step 1.1: Use the Northeast Sky geographic coordinate system as the positioning and orientation coordinate system, and select the right front upper coordinate system as the vehicle body coordinate system; Step 1.2: Based on the odometer scale factor error δK, the vehicle speed in the vehicle coordinate system is obtained: Among them, v b Download the real speed of the vehicle in the vehicle coordinate system; Step 1.3: Based on the vehicle attitude information output by the strapdown inertial navigation system, calculate the vehicle speed in the vehicle coordinate system using the following formula: Convert to the positioning and orientation coordinate system: in, The vehicle attitude matrix output by the strapdown inertial navigation system; Indicates the vehicle speed information in the positioning and orientation coordinate system; Step 1.4: Rewrite the above equation based on the mathematical platform attitude error in the strapdown inertial navigation system as: Where φ is the mathematical platform attitude error, and φ=[φ E ,φ N ,φ U ] T ; Represents the true posture matrix of the vehicle; Step 1.5: Expand the right side of the above equation and ignore the second-order small quantity related to the error, and we can get: Among them, v n Indicates the actual speed of the vehicle; Step 1.6: Based on and v n The error of real-time solution speed in positioning and orientation coordinate system is obtained by the following formula: in, Step 1.7: After expansion, the real-time solution speed error equation can be obtained:

5. The online calibration method for the odometer scale factor error in a positioning and orientation system according to claim 4, characterized in that: The specific steps of step 2 include: Step 2.1: Select the strapdown inertial navigation system error and the odometer scale factor error as the state of the online error estimation, and obtain the state vector X of the online error estimation: Step 2.2: Based on the error model of the positioning and orientation system established in step 1, construct the state equation for online error estimation: Among them, F is the state matrix, G is the system noise driving matrix, and W is the system noise vector.

6. The online calibration method for the odometer scale factor error in a positioning and orientation system according to claim 5, characterized in that: The specific steps of step 3 include: Step 3.1: Calculate the speed of the vehicle in the positioning and orientation coordinate system using the following formula: Among them, T D is the sampling time interval of the odometer; △S i For odometer in T D The distance traveled is increased by the internal measurement; Step 3.2: Obtain the online estimation of the odometry scale factor error, Z, using the following formula: Among them, v′ n The vehicle speed actually output by the strapdown inertial navigation system; Step 3.3: Error δv based on the output velocity of the strapdown inertial navigation system n and the error in real-time solution speed Rewrite the above formula as: in, Step 3.4: Based on the above formula, express the measurement Z as: Step 3.5: Substitute the real-time velocity error equation obtained in step 1.7 into the above equation to obtain the measured Z: Step 3.6: Based on the state vector X of the online error estimation and the above formula, establish the measurement equation for the online error estimation: Z=HX+V Where H is the measurement matrix and V is the measurement white noise vector.

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