CN117848388A - Vehicle navigation error calibration method and device, vehicle positioning equipment and storage medium - Google Patents
Vehicle navigation error calibration method and device, vehicle positioning equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a vehicle navigation error calibration method and device, vehicle positioning equipment and a storage medium, and relates to the technical field of vehicle navigation. The method not only calibrates the navigation error of the target vehicle, but also calibrates the error generated by the space structure and the motion state of the target vehicle. In the vehicle navigation error calibration process, the vehicle navigation error data at each moment is calibrated rapidly according to the prediction error data and the measurement error data of the vehicle navigation, so that the convergence speed of calibration is increased, and the calibration efficiency and accuracy are improved.
Description
Technical Field
The invention relates to the technical field of vehicle navigation, in particular to a vehicle navigation error calibration method and device, vehicle positioning equipment and a storage medium.
Background
In order to realize accurate navigation and route planning by combining with a high-precision map, an automatic driving vehicle is equipped with a high-precision combined navigation positioning system. The integrated navigation and positioning system is generally composed of a global navigation satellite system (Global Navigation Satellite System, abbreviated as GNSS) and an inertial measurement unit (Inertial Measurement Unit, abbreviated as IMU) and an odometer. Global positioning is carried out by receiving satellite signals through the GNSS, calibration is realized through the IMU and the odometer, and the positioning capability of a certain time precision is continuously maintained when the GNSS signals are lost.
The odometer measures the relative displacement or movement of the vehicle by integration based on the movement information of the vehicle. However, odometers have accumulated errors that can lead to positional drift over time. To solve this problem, the error of the odometer needs to be calibrated and compensated. At present, common calibration methods comprise a set known road mark calibration method and a kinematic incomplete constraint-based calibration method.
The method for calibrating the road mark point is set to have the condition limitations of requiring the vehicle to run along a straight line as much as possible, minimizing the change of elevation and the like. Because of special requirements on a calibration route and more limitations on a calibration site, a lot of inconvenience exists in the actual use process. The method uses the difference between acceleration and speed as filter observation based on the kinematics incomplete constraint calibration method, the convergence speed of the error parameter estimation value of the odometer in the calibration algorithm is slower, the calibration time is long, and the requirements of fast maneuvering on the vehicle are difficult to meet.
Disclosure of Invention
Therefore, the invention aims to provide a vehicle navigation error calibration method and device, vehicle positioning equipment and a storage medium, which can effectively improve calibration efficiency and accuracy.
In order to achieve the above object, the technical scheme adopted by the embodiment of the invention is as follows:
in a first aspect, the invention provides a vehicle navigation error calibration method, which is applied to vehicle positioning equipment, wherein the vehicle positioning equipment comprises an IMU and a GNSS receiver; the method comprises the following steps:
acquiring vehicle navigation measurement data of each moment of a target vehicle, vehicle navigation error data of each moment and vehicle navigation estimation data of each moment; the vehicle navigation measurement data comprise GNSS data, IMU data, vehicle running data and vehicle structure data; the vehicle-mounted navigation error data represents errors generated by navigation, space structure and motion state of the target vehicle;
respectively obtaining prediction error data and measurement error data at the same moment; the prediction error data are used for representing the prediction error between the vehicle navigation estimation data at adjacent moments; the measurement error data is used for representing errors between vehicle navigation measurement data at any moment and vehicle navigation estimation data at adjacent moment;
obtaining target error data at corresponding moments according to the prediction error data and the measurement error data;
determining the vehicle navigation estimated data of each moment one by utilizing the target error data of each moment and the corresponding vehicle navigation measurement data;
And when target error data corresponding to the target moment meet a preset convergence condition, taking the target error data as a vehicle calibration result of the target vehicle.
In an alternative embodiment, the mth time is the target time, and the N-1 time is the time immediately before the N time, where N is less than or equal to M; the step of obtaining the prediction error data comprises:
constructing a state transition matrix according to the vehicle navigation estimation data at the moment N-1;
and obtaining prediction error data at the time N according to a pre-constructed state equation of the target vehicle by utilizing the vehicle-mounted navigation error data at the time N-1 and the state transition matrix.
In an alternative embodiment, the vehicle navigation estimation data includes a speed estimation value of an inertial coordinate system, a latitude estimation value of the inertial coordinate system, a height estimation value of the inertial coordinate system and a latitude estimation value of a vehicle motion coordinate system; the step of constructing a state transition matrix according to the vehicle navigation estimation data at the time N-1 comprises the following steps:
and obtaining the state transition matrix according to the speed estimated value of the inertial coordinate system at the time N-1, the latitude estimated value of the inertial coordinate system, the altitude estimated value of the inertial coordinate system, the latitude estimated value of the vehicle motion coordinate system and the earth rotation angular velocity.
In an alternative embodiment, the mth time is the target time, and the N-1 time is the time immediately before the N time, where N is less than or equal to M; the step of obtaining the measurement error data comprises the following steps:
obtaining a measurement vector according to a preset constraint condition, the vehicle navigation estimation data at the time N-1 and the vehicle navigation measurement data at the time N;
obtaining a measurement matrix according to the vehicle navigation estimation data at the time N-1 and the vehicle navigation measurement data at the time N;
and obtaining the measurement error data of the N moments according to a pre-constructed observation equation of the target vehicle by using the measurement matrix and the measurement vector.
In an alternative embodiment, the vehicle navigation estimation data includes a coordinate transformation matrix estimation value, a speed estimation value of an inertial coordinate system, an angular speed estimation value of the inertial coordinate system, a rear wheel center lever arm estimation value of a vehicle rear wheel coordinate system, a rear wheel track estimation value of the vehicle rear wheel coordinate system and a rear wheel speed coefficient estimation value; the vehicle operation data includes a rear wheel speed of the target vehicle; the step of obtaining a measurement vector according to a preset constraint condition, the vehicle navigation estimation data at the time N-1 and the vehicle navigation measurement data at the time N comprises the following steps:
Obtaining a first measurement vector according to the coordinate transformation matrix estimated value at the time N-1, the speed estimated value of an inertial coordinate system, the angular speed estimated value of the inertial coordinate system, the rear wheel center lever arm estimated value of a vehicle rear wheel coordinate system and the rear wheel tread estimated value of the vehicle rear wheel coordinate system;
obtaining a second measurement vector according to the rear wheel speed at the moment N and the rear wheel speed coefficient estimated value at the moment N-1;
and determining the measurement vector according to the preset constraint condition, the first measurement vector and the second measurement vector.
In an alternative embodiment, the vehicle navigation estimation data includes a coordinate transformation matrix estimation value, a speed estimation value of an inertial coordinate system, a rear wheel center lever arm estimation value of a vehicle rear wheel coordinate system, a rear wheel track estimation value of the vehicle rear wheel coordinate system, an angular speed estimation value of the inertial coordinate system and a rear wheel speed coefficient estimation value; the vehicle operation data includes a rear wheel speed of the target vehicle; the step of obtaining a measurement matrix according to the vehicle navigation estimation data at the time N-1 and the vehicle navigation measurement data at the time N comprises the following steps:
and obtaining the measurement matrix according to the estimated value of the coordinate transformation matrix at the time N-1, the estimated value of the speed of the inertial coordinate system, the estimated value of the lever arm of the center lever of the rear wheel, the estimated value of the rear wheel distance of the vehicle rear wheel coordinate system, the estimated value of the angular speed of the inertial coordinate system, the estimated value of the rear wheel speed coefficient of the vehicle rear wheel coordinate system and the rear wheel speed at the time N.
In an optional embodiment, the step of determining the vehicle navigation estimation data at each time one by using the target error data at each time and the corresponding vehicle navigation measurement data includes:
according to the target error data and the corresponding vehicle-mounted navigation measurement data at each moment, corrected vehicle-mounted navigation measurement data at each moment are obtained one by one;
and carrying out Kalman filtering according to the corrected vehicle navigation measurement data at each moment to obtain corresponding vehicle navigation estimation data.
In a second aspect, the invention provides a vehicle navigation error calibration device, which is applied to vehicle positioning equipment, wherein the vehicle positioning equipment comprises an IMU and a GNSS receiver; the device comprises:
the acquisition module is used for acquiring the vehicle-mounted navigation measurement data of each moment of the target vehicle, the vehicle-mounted navigation error data of each moment and the vehicle-mounted navigation estimation data of each moment; the vehicle navigation measurement data comprise GNSS data, IMU data, vehicle running data and vehicle structure data; the vehicle-mounted navigation error data represents errors generated by navigation, space structure and motion state of the target vehicle;
the calibration module is used for respectively obtaining the prediction error data and the measurement error data at the same moment; the prediction error data are used for representing the prediction error between the vehicle navigation estimation data at adjacent moments; the measurement error data is used for representing errors between vehicle navigation measurement data at any moment and vehicle navigation estimation data at adjacent moment; obtaining target error data at corresponding moments according to the prediction error data and the measurement error data; determining the vehicle navigation estimated data of each moment one by utilizing the target error data of each moment and the corresponding vehicle navigation measurement data;
And the determining module is used for taking the target error data as a vehicle calibration result of the target vehicle when the target error data corresponding to the target moment meet the preset convergence condition.
In a third aspect, the present invention provides a vehicle positioning device, the vehicle positioning device including an IMU for measuring IMU data, a GNSS receiver for receiving GNSS data, a memory for storing a computer program, and a processor for executing the vehicle navigation error calibration method according to any of the foregoing embodiments when the computer program is invoked.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the vehicle navigation error calibration method according to any one of the preceding embodiments.
Compared with the prior art, the vehicle navigation error calibration method, the device, the vehicle positioning equipment and the storage medium provided by the embodiment of the invention not only calibrate the navigation error of the target vehicle, but also calibrate the error generated by the space structure and the motion state of the target vehicle. In the vehicle navigation error calibration process, the vehicle navigation error data at each moment is calibrated rapidly according to the prediction error data and the measurement error data of the vehicle navigation, so that the convergence speed of calibration is increased, and the calibration and accuracy are improved.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 shows a schematic diagram of the kalman filtering principle provided by the embodiment of the invention.
Fig. 2 is a schematic flow chart of a vehicle navigation error calibration method according to an embodiment of the present invention.
Fig. 3 shows a schematic diagram of a japanese route for vehicle navigation error calibration.
Fig. 4 is a schematic block diagram of a vehicle navigation error calibration apparatus according to an embodiment of the present invention.
Fig. 5 shows a block schematic diagram of an on-board positioning device according to an embodiment of the present invention.
Icon: 100-vehicle-mounted positioning equipment; 110-memory; a 120-processor; 130-a communication module; 140-IMU; a 150-GNSS receiver; 200-a vehicle navigation error calibration device; 201-an acquisition module; 202-a calibration module; 203-a determination module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
It is noted that relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Along with the rapid development of the automatic driving technology, the application of the automatic driving, unmanned delivery trucks, unmanned buses in parks, unmanned sweeping vehicles and the like of the L3 level and above also brings higher requirements to high-precision navigation positioning and speed measuring equipment.
The inertial navigation has the characteristic of realizing autonomous navigation and positioning without depending on external positioning information. Since inertial navigation is to integrate a gyroscope and an accelerometer to obtain position, velocity and attitude information, inertial navigation errors accumulate over time. In order to achieve autonomous navigation for a long time, the IMU design must be very elegant to minimize the rate of error accumulation.
In order to limit inertial navigation errors to a reasonable range, inertial navigation parameters need to be modified frequently to remove accumulated inertial navigation errors. Therefore, an odometer is usually added in the integrated navigation positioning system, and the integrated navigation is assisted by combining the odometer observation information with the non-integrity constraint, so that the positioning accuracy of the GNSS-free signal scene is improved.
It is found that the accuracy of the integrated navigation device is directly affected by the accuracy of positioning, except for the influence of the period of each sensor. Inertial navigation generally uses the geometric center of an IMU as a reference datum for navigation, and the reference datum is the rotation center point of each wheel in wheel speed information of each wheel in vehicle operation data. In order to fuse various navigation information, error compensation must be performed on the navigation information, and the navigation information is converted into a statistical reference standard for data fusion.
The vehicle running data is influenced by factors such as tire pressure, tire wear degree, vehicle load and the like, and the scale factor error, the vehicle installation error and the like are directly caused to change, so that the error of the odometer must be calibrated and compensated before autonomous navigation is calculated.
The conventional calibration at present is to set a known road mark point, the method requires that the vehicle runs along a straight line as much as possible, the elevation change is as small as possible, special requirements are provided for a calibration route, the limit on a calibration site is relatively large, and a plurality of inconveniences exist in the actual use process. The other calibration method is an odometer parameter identification method based on the kinematic incomplete constraint, the method uses the difference between acceleration and speed as a filter for observation, but the odometer error parameter estimation value in the actual dynamic test result of the method has slower convergence speed and long calibration time, and is difficult to meet the requirements of fast maneuvering vehicles.
Based on the above, the vehicle navigation error calibration method and device provided by the embodiment of the invention not only calibrate the navigation error of the target vehicle, but also calibrate the error generated by the space structure and the motion state of the target vehicle. In the vehicle navigation error calibration process, the vehicle navigation error data at each moment is calibrated rapidly according to the prediction error data and the measurement error data of the vehicle navigation, so that the convergence speed of calibration is increased, and the calibration efficiency and accuracy are improved.
Considering that the embodiments of the present invention relate to a kalman filter algorithm, the kalman filter algorithm will now be described. The kalman filter algorithm macroscopically includes a prediction step and an update step. The predicting step predicts the predicted state at the current time according to the state information at the previous time. The updating step is to compare the predicted state with the state information measured by the navigation system and correct the predicted state.
As shown in fig. 1, the angular velocity and acceleration of the vehicle obtained by the IMU through the gyroscope and the accelerometer are strapdown solved and input to the kalman filter, and meanwhile, the vehicle operation data, the vehicle structure data and the GNSS data are input to the kalman filter. And the Kalman filter executes a prediction step on the received vehicle navigation measurement data according to the motion constraint to obtain a filtering result, and then executes an updating step according to the filtering result to update the calibration parameters.
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Referring to fig. 2, fig. 2 is a schematic flow chart of a vehicle navigation error calibration method according to an embodiment of the invention. The method is applied to vehicle-mounted positioning equipment, wherein the vehicle-mounted positioning equipment comprises an IMU and a GNSS receiver; the method comprises the following steps:
Step S10, acquiring the vehicle navigation measurement data, the vehicle navigation error data and the vehicle navigation estimation data of the target vehicle at each moment.
The vehicle navigation measurement data comprise GNSS data, IMU data, vehicle running data and vehicle structure data. The vehicle-mounted navigation error data represents errors generated by navigation, space structure and motion state of the target vehicle.
In the embodiment of the invention, a controller area network (Controller Area Network, abbreviated as CAN) or a CAN (CAN with Flexible Data Rate, abbreviated as CANFD) interface with flexible data rate is utilized to access an automobile electronic control unit of a vehicle chassis, four-wheel speed, gear and wheel corner of a target vehicle are obtained through adapting to the chassis of the target vehicle (namely, a decoding algorithm of data acquired by the automobile electronic control unit of the target vehicle), and are sent to a vehicle-mounted positioning device through the CAN/CANFD, the vehicle-mounted positioning device analyzes the operation data of the target vehicle according to the chassis protocol of the target vehicle to obtain the vehicle operation data of each moment, and abnormal data such as vehicle slip, wheel speed jump, turning identification and the like are removed to obtain the vehicle operation data of each moment.
Step S20, respectively obtaining the prediction error data and the measurement error data at the same time.
The prediction error data are used for representing the prediction error between the vehicle navigation estimation data at adjacent moments. The measurement error data is used for representing the error between the vehicle navigation measurement data at any moment and the vehicle navigation estimation data at the adjacent moment.
Step S30, obtaining target error data at corresponding time according to the prediction error data and the measurement error data.
In the embodiment of the invention, the preset prediction error weight and the preset measurement error weight are obtained, and the target error data is obtained by calculation according to the prediction error data, the preset prediction error weight, the measurement error data and the preset measurement error weight. The calculation formula of the target error data is as follows:
C＝αA+βB
wherein C is target error data; alpha is a preset prediction error weight; a is prediction error data; beta is a preset measurement error weight; b is measurement error data.
Step S40, utilizing the target error data and the corresponding vehicle navigation measurement data at each moment to determine the vehicle navigation estimation data at each moment one by one.
And S50, when target error data corresponding to the target moment meet the preset convergence condition, taking the target error data as a vehicle calibration result of the target vehicle.
In the embodiment of the present invention, the preset convergence condition may be that target error data at each time within a preset time range is kept unchanged. And sequentially judging whether the target error data at each moment meets the preset convergence condition, if the target error data does not meet the preset convergence condition (namely, the target error data at each moment in the preset time range has fluctuation), taking the target error data as vehicle navigation error data, returning to the step S10 to continue execution until the target error data corresponding to the target moment meets the preset convergence condition, and taking the target error data corresponding to the target moment as a vehicle calibration result.
After the vehicle navigation error calibration is completed, writing the vehicle calibration result into a Flash area of the vehicle positioning equipment, and automatically loading the vehicle calibration result when the equipment is powered on again to compensate and correct the vehicle navigation measurement data.
In summary, the vehicle navigation error calibration method provided by the embodiment of the invention not only calibrates the navigation error of the target vehicle, but also calibrates the error generated by the space structure and the motion state of the target vehicle. In the vehicle navigation error calibration process, the vehicle navigation error data at each moment is calibrated rapidly according to the prediction error data and the measurement error data of the vehicle navigation, so that the convergence speed of calibration is increased, and the calibration efficiency and accuracy are improved.
It should be noted that, before executing the method and the device for calibrating the vehicle navigation error provided by the embodiment of the invention, the vehicle positioning device needs to be installed on the target vehicle. The vehicle-mounted positioning device is rigidly connected with a hard structure of the body of the target vehicle through screws. The mounting position may be any position of the target vehicle, but it is necessary to avoid as much as possible the severe vibration, the large temperature change, and the like, and if necessary, a damper may be used. And after the calibration of the vehicle-mounted positioning equipment is finished, the vehicle-mounted positioning equipment cannot be removed, and if the vehicle-mounted positioning equipment is moved, the calibration needs to be reinstalled.
In the embodiment of the invention, a coordinate system based on a vehicle navigation error calibration method is defined as follows:
the inertial coordinate system t, the origin of coordinates is the geometric center of the IMU, the X axis points to the forward direction of the IMU, the Y axis points to the right perpendicular to the forward direction of the IMU, and the Z axis points to the geocentric direction perpendicular to the XY plane.
The vehicle motion coordinate system b, the origin of coordinates is the geometric center of the IMU, the X axis points to the vehicle forward direction, the Y axis points to the right perpendicular to the vehicle forward direction, and the Z axis points to the geocentric direction perpendicular to the XY plane.
The vehicle rear wheel coordinate system c, the origin of coordinates is the center of the rear wheel axle, the X axis points to the vehicle forward direction, the Y axis points to the right perpendicular to the vehicle forward direction, and the Z axis points to the geocentric direction perpendicular to the XY plane.
The northeast coordinate system n, the origin of coordinates is the geometric center of the IMU, the X axis points to the geographic north pole, the Y axis points to the east direction, and the Z axis points to the direction of the earth center perpendicular to the XY plane;
the earth center is fixed in a coordinate system e, the origin of the coordinates is the earth center, the X axis points to the intersection point of the equator and the primary meridian, the Y axis points to the 90-degree meridian on the equatorial plane, and the Z axis points to the geographic north pole.
Assuming that the in-vehicle positioning apparatus is mounted on the roof above the target vehicle steering wheel, in the vehicle motion coordinate system, the front-rear distance from the center of the rear axle in the X-axis direction to the IMU is 1.8 meters, the left-right distance from the center of the rear axle in the Y-axis direction to the IMU is 0.1 meters, and the up-down distance from the center of the rear axle in the Z-axis direction to the IMU is 0.7 meters. The coordinates of the rear wheel center lever arm are (-1.8,0.1,0.7) as defined by the vehicle motion coordinate system. The invention is not limited to the definition of the coordinate system, and can construct the coordinate system required by calibration according to the actual application scene.
In order to improve the rapidness of vehicle navigation error calibration, the embodiment of the invention does not need to use a special instrument to measure the installation error of the vehicle positioning equipment, and can estimate the vehicle structure data according to experience, wherein the vehicle structure data comprises a rear wheel center lever arm, a rear wheel speed coefficient and a rear wheel speed. The estimated vehicle structure data are stored in the vehicle-mounted positioning equipment through the upper computer, so that the time for measuring the vehicle structure data is shortened, automatic vehicle-mounted navigation error calibration is realized, and the filtering convergence speed can be effectively accelerated.
The vehicle running data and the vehicle structure data are converted into an inertial coordinate system by utilizing the rear wheel center lever arm and are fused and calculated with the IMU data. Therefore, the vehicle-mounted positioning equipment in the embodiment of the invention can be arranged at any position of the vehicle, the difficulty in mounting the vehicle-mounted positioning equipment is reduced, and the compatibility of the calibrated vehicle type is improved.
It should be noted that, in order to accelerate the convergence speed of the vehicle navigation error calibration, the track path needs to include a left-right turn and a straight after-turn during the track calibration, and the optimal calibration path may be a daily-shaped path in fig. 3. In the calibration process, the target vehicle starts from seven points and runs along the route of the solid arrow, and when the target vehicle walks to the uppermost turn of the Chinese character 'ri' route, the rest of the route is walked along the arrow of the broken line, and the target vehicle returns to the terminal to complete the calibration of the complete Chinese character 'ri' route once. If no ideal route exists, the vehicle navigation system must comprise a left turn, a right turn and a straight line after the turn, and the convergence speed of the wheel speed coefficient of the rear wheel can be accelerated through different turns, so that the accuracy and the instantaneity of the vehicle navigation error calibration are improved.
Alternatively, in practical application, the vehicle navigation error data at each moment is predicted by using kalman filtering. The M-th time is the target time, the N-1 time is the time before the N time, and N is less than or equal to M. For step S20 in fig. 2, for the acquisition of prediction error data therein, one possible implementation is provided below, in particular:
And constructing a state transition matrix according to the vehicle navigation estimation data at the moment N-1. And obtaining prediction error data at the time N according to a pre-constructed state equation of the target vehicle by utilizing the vehicle-mounted navigation error data at the time N-1 and the state transition matrix.
Optionally, M is a natural number greater than or equal to 1, and N is a natural number greater than or equal to 1.
In the embodiment of the present invention, it is assumed that the state equation of the target vehicle constructed in advance is:
wherein w is a noise vector of a preset process, and obeys normal distribution of zero mean value; x is x N-1 The navigation error data is the vehicle navigation error data at the moment N-1;is a state transition matrix; x is x N/N-1 The predicted N-time prediction error data is predicted N-time vehicle navigation error data.
Optionally, in practical application, the state transition matrix of the next moment is constructed iteratively by using the vehicle navigation estimation data of each moment, so as to improve the prediction accuracy of the vehicle navigation error data. The vehicle navigation estimation data includes a speed estimation value of an inertial coordinate system, a latitude estimation value of the inertial coordinate system, a height estimation value of the inertial coordinate system, and a latitude estimation value of a vehicle motion coordinate system. The method comprises the steps of constructing a state transition matrix according to vehicle navigation estimation data at the moment N-1, and comprises the following steps:
And obtaining a state transition matrix according to the speed estimated value of the inertial coordinate system at the moment N-1, the latitude estimated value of the inertial coordinate system, the height estimated value of the inertial coordinate system, the latitude estimated value of the vehicle motion coordinate system and the earth rotation angular velocity.
In the embodiment of the invention, errors generated by the space structure and the motion state comprise an IMU installation error angle of an inertial coordinate system and a vehicle motion coordinate system, a rear wheel center lever arm error, a rear wheel track error and a rear wheel speed coefficient. Wherein the rear wheel center lever arm characterizes a three-dimensional lever arm from the rear wheel axle center to the geometrical center of the IMU.
Specifically, for 9-dimensional error vectors of an inertial coordinate system and an IMU installation error angle, a rear wheel center lever arm error, a rear wheel track error, and a rear wheel speed coefficient of a vehicle motion coordinate system, the corresponding state transition matrix may be expressed as:
wherein,is the projection of the velocity estimation value of the inertial coordinate system relative to the geocentric fixed coordinate system in the northeast coordinate system, namely the velocity estimation value is obtained by the coordinate transformation from the inertial coordinate system to the northeast coordinate system through the geocentric fixed coordinate system。/>Is->North speed of>Is->East speed of>Is->Is a ground direction speed of the vehicle. / >For the height estimate of the inertial coordinate system, +.>For latitude estimation of inertial coordinate system, +.>For the radius of the mortise circle corresponding to the latitude estimation value of the inertial coordinate system, < >>For the meridian radius corresponding to the latitude estimate of the inertial coordinate system, < >>Is the latitude estimated value omega of the vehicle motion coordinate system ie For the earth rotation angular velocity, G, P and Q are temporary variables.
Alternatively, in practical application, the kalman filter is used to calculate the error between the vehicle navigation measurement data at each time and the vehicle navigation estimation data at the adjacent time. The M-th time is the target time, the N-1 time is the time before the N time, and N is less than or equal to M. For step S20 in fig. 2, for obtaining metrology error data therein, one possible implementation is provided below, in particular:
and obtaining a measurement vector according to the preset constraint condition, the vehicle navigation estimation data at the time N-1 and the vehicle navigation measurement data at the time N. And obtaining a measurement matrix according to the vehicle navigation estimation data at the time N-1 and the vehicle navigation measurement data at the time N. And obtaining measurement error data at the moment N according to a pre-constructed observation equation of the target vehicle by using the measurement matrix and the measurement vector.
Optionally, M is a natural number greater than or equal to 1, and N is a natural number greater than or equal to 1.
In the embodiment of the present invention, it is assumed that the observation equation of the target vehicle constructed in advance is:
Z＝Hx+v
wherein Z is a measurement vector, H is a measurement matrix, x is measurement error data, v is a measurement noise vector, and the normal distribution obeys zero mean.
Assume that the preset constraint includes constraint 1, constraint 2, and constraint 3. Wherein constraint 1 is the speed of the vehicle when stationaryObtain->Constraint 2 is that the land vehicle is in the wheel axis direction of the wheels and the vertical direction of the road surface or the track surface, and the speed of the vehicle is zero; constraint 3 is that the vehicle has only forward speed.
As one possible implementation, the vehicle navigation estimation data includes a coordinate transformation matrix estimation value, a speed estimation value of an inertial coordinate system, an angular speed estimation value of the inertial coordinate system, a rear wheel center lever arm estimation value of a vehicle rear wheel coordinate system, a rear wheel tread estimation value of the vehicle rear wheel coordinate system, and a rear wheel speed coefficient estimation value, and the vehicle operation data includes a rear wheel speed of the target vehicle. Obtaining a measurement vector according to a preset constraint condition, vehicle navigation estimation data at the time N-1 and vehicle navigation measurement data at the time N, wherein the measurement vector comprises the following steps:
and obtaining a first measurement vector according to the coordinate transformation matrix estimated value at the time N-1, the speed estimated value of the inertial coordinate system, the angular speed estimated value of the inertial coordinate system, the rear wheel center lever arm estimated value of the vehicle rear wheel coordinate system and the rear wheel track estimated value of the vehicle rear wheel coordinate system. And obtaining a second measurement vector according to the rear wheel speed at the moment N and the rear wheel speed coefficient estimated value at the moment N-1. And determining a measurement vector according to the preset constraint condition, the first measurement vector and the second measurement vector.
In the embodiment of the invention, the rear wheel speed coefficient includes a left rear wheel speed coefficient and a right rear wheel speed coefficient, and the coordinate transformation matrix estimation value includes a coordinate transformation matrix estimation value from an inertial coordinate system to a vehicle motion coordinate system and a coordinate transformation matrix estimation value from a northeast coordinate system to the inertial coordinate system. Assuming that a left rear wheel measurement vector and a right rear wheel measurement vector are respectively constructed for the left rear wheel and the right rear wheel, the measurement vectors include a left rear wheel measurement vector and a right rear wheel measurement vector.
Specifically, the formulas of the left rear wheel measurement vector and the right rear wheel measurement vector are respectively:
wherein Z is Left side For measuring vector of left rear wheel, Z Right side For the measurement vector of the right rear wheel,for coordinate transformation matrix estimation from inertial coordinate system to vehicle motion coordinate system, +.>Is a coordinate transformation matrix estimated value from the northeast coordinate system to the inertial coordinate system. />The projection of the velocity estimation value of the inertial coordinate system relative to the northeast coordinate system in the northeast coordinate system is obtained by changing the velocity estimation value from the inertial coordinate system to the northeast coordinate system.
For the angular velocity estimation value of the inertial coordinate system, the ∈characterization takes an antisymmetric matrix, ++>The projection of the rear wheel center lever arm estimation value relative to the inertial coordinate system on the vehicle motion coordinate system is the vehicle rear wheel coordinate system. / >Is the projection of the rear wheel track of the vehicle rear wheel coordinate system relative to the vehicle rear wheel coordinate system in the vehicle motion coordinate system. v bl For the second pulse of the left rear wheel, v br For the right rear wheel second pulse, < >>For the wheel speed coefficient estimation value of the left rear wheel, +.>And the estimated value is the wheel speed coefficient of the right rear wheel.
It is worth mentioning that,first measurement vector being left rear wheel measurement vector, < >>The second measurement vector is the left rear wheel measurement vector. /> First measurement vector, which is right rear wheel measurement vector, ">The second measurement vector is the right rear wheel measurement vector.
As one possible implementation, the vehicle navigation estimation data includes a coordinate transformation matrix estimation value, a speed estimation value of an inertial coordinate system, a rear wheel center lever arm estimation value of a vehicle rear wheel coordinate system, a rear wheel track estimation value of the vehicle rear wheel coordinate system, an angular speed estimation value of the inertial coordinate system, and a rear wheel speed coefficient estimation value, and the vehicle operation data includes a rear wheel speed of the target vehicle. Obtaining a measurement matrix according to the vehicle navigation estimation data at the time N-1 and the vehicle navigation measurement data at the time N, wherein the measurement matrix comprises the following steps:
and obtaining a measurement matrix according to the coordinate transformation matrix estimated value at the time N-1, the speed estimated value of the inertial coordinate system, the rear wheel center lever arm estimated value, the rear wheel track estimated value of the vehicle rear wheel coordinate system, the angular speed estimated value of the inertial coordinate system, the rear wheel speed coefficient estimated value of the vehicle rear wheel coordinate system and the rear wheel speed at the time N.
In the embodiment of the present invention, the measurement matrix includes a left rear wheel measurement matrix and a right rear wheel measurement matrix, and the formula of the left rear wheel measurement matrix corresponding to the left rear wheel measurement vector may be expressed as:
wherein H is Left side For the left rear wheel measurement matrix, A and B are temporary variables.
And obtaining the vehicle navigation measurement data of the left rear wheel through the left rear wheel measurement vector and the left rear wheel measurement matrix by using an observation equation. The vehicle navigation measurement data of the left rear wheel can be expressed as:
wherein x is Left side For the vehicle navigation measurement data of the left rear wheel,is the projection of the velocity error of the inertial frame relative to the northeast frame in the northeast frame. />Is the projection of the attitude error of the inertial coordinate system relative to the northeast coordinate system in the northeast coordinate system. />The angle of the IMU installation error of the inertial coordinate system and the vehicle motion coordinate system is the projection of the IMU installation error of the vehicle motion coordinate system relative to the inertial coordinate system in the inertial coordinate system. δb g Zero bias error of gyro->Error for rear wheel center lever arm->K is the wheel tread error of the rear wheel ws,bl Is the wheel speed coefficient error of the left rear wheel.
The formula of the right rear wheel measurement matrix corresponding to the right rear wheel measurement vector can be expressed as:
wherein H is Right side For the right rear wheel measurement matrix, C and D are temporary variables.
And obtaining the vehicle navigation measurement data of the left rear wheel through the left rear wheel measurement vector and the left rear wheel measurement matrix by using an observation equation. The vehicle navigation measurement data of the left rear wheel can be expressed as:
wherein x is Right side K is vehicle navigation measurement data of the right rear wheel ws,br Is the wheel speed coefficient error of the right rear wheel.
Optionally, in practical application, the vehicle navigation error data at each moment can be dynamically subjected to kalman filtering through a state equation and an observation equation, so that accuracy and instantaneity of the vehicle navigation estimation data are improved. The substeps of step S40 in fig. 2 may include:
according to the target error data and the corresponding vehicle-mounted navigation measurement data at each moment, corrected vehicle-mounted navigation measurement data at each moment are obtained one by one; and carrying out Kalman filtering according to the corrected vehicle navigation measurement data at each moment to obtain corresponding vehicle navigation estimation data.
In the embodiment of the invention, corresponding vehicle-mounted navigation measurement data is corrected by utilizing the target error data obtained at each moment to obtain corrected vehicle-mounted navigation measurement data, and then a prediction step and an updating step of Kalman filtering are executed according to the corrected vehicle-mounted navigation measurement data to obtain vehicle-mounted navigation estimation data corresponding to the vehicle-mounted navigation measurement data.
Based on the same inventive concept, the embodiment of the invention also provides a vehicle navigation error calibration device. The basic principle and the technical effects are the same as those of the above embodiments, and for brevity, reference is made to the corresponding matters in the above embodiments where the description of the present embodiment is omitted.
Referring to fig. 4, fig. 4 is a block diagram illustrating a vehicle navigation error calibration apparatus 200 according to an embodiment of the invention. The vehicle navigation error calibration device 200 comprises an acquisition module 201, a calibration module 202 and a determination module 203.
An acquiring module 201, configured to acquire vehicle navigation measurement data at each time of a target vehicle, vehicle navigation error data at each time, and vehicle navigation estimation data at each time; the vehicle navigation measurement data comprise GNSS data, IMU data, vehicle running data and vehicle structure data; the vehicle-mounted navigation error data represents errors generated by navigation, space structure and motion state of a target vehicle;
the calibration module 202 is configured to obtain prediction error data and measurement error data at the same time respectively; the prediction error data is used for representing the prediction error between the vehicle navigation estimation data at adjacent moments; the measurement error data is used for representing errors between the vehicle navigation measurement data at any moment and the vehicle navigation estimation data at the adjacent moment; obtaining target error data at corresponding time according to the prediction error data and the measurement error data; determining the vehicle navigation estimated data of each moment one by utilizing the target error data of each moment and the corresponding vehicle navigation measurement data;
The determining module 203 is configured to, when the target error data corresponding to the target moment meets the preset convergence condition, take the target error data as a vehicle calibration result of the target vehicle.
In summary, the vehicle navigation error calibration device provided by the embodiment of the invention not only calibrates the navigation error of the target vehicle, but also calibrates the error generated by the space structure and the motion state of the target vehicle. In the vehicle navigation error calibration process, the vehicle navigation error data at each moment is calibrated rapidly according to the prediction error data and the measurement error data of the vehicle navigation, so that the convergence speed of calibration is increased, and the calibration efficiency and accuracy are improved.
Optionally, the mth time is a target time, the N-1 time is a time immediately before the N time, and N is less than or equal to M. The calibration module 202 is specifically configured to construct a state transition matrix according to the vehicle navigation estimation data at time N-1. And obtaining prediction error data at the time N according to a pre-constructed state equation of the target vehicle by utilizing the vehicle-mounted navigation error data at the time N-1 and the state transition matrix.
Optionally, the vehicle navigation estimation data includes a speed estimation value of an inertial coordinate system, a latitude estimation value of the inertial coordinate system, a height estimation value of the inertial coordinate system, and a latitude estimation value of a vehicle motion coordinate system. The calibration module 202 is specifically configured to obtain a state transition matrix according to the speed estimated value of the inertial coordinate system at time N-1, the latitude estimated value of the inertial coordinate system, the altitude estimated value of the inertial coordinate system, the latitude estimated value of the vehicle motion coordinate system, and the earth rotation angular velocity.
Optionally, the mth time is a target time, the N-1 time is a time immediately before the N time, and N is less than or equal to M. At the time immediately before the time N, N is less than or equal to M. The calibration module 202 is specifically configured to obtain a measurement vector according to a preset constraint condition, vehicle navigation estimation data at time N-1, and vehicle navigation measurement data at time N. And obtaining a measurement matrix according to the vehicle navigation estimation data at the time N-1 and the vehicle navigation measurement data at the time N. And obtaining measurement error data at the moment N according to a pre-constructed observation equation of the target vehicle by using the measurement matrix and the measurement vector.
Optionally, the vehicle navigation estimation data includes a coordinate transformation matrix estimation value, a speed estimation value of an inertial coordinate system, an angular speed estimation value of the inertial coordinate system, a rear wheel center lever arm estimation value of a vehicle rear wheel coordinate system, a rear wheel track estimation value of the vehicle rear wheel coordinate system, and a rear wheel speed coefficient estimation value, and the vehicle operation data includes a rear wheel speed of the target vehicle.
The calibration module 202 is specifically configured to obtain a first measurement vector according to the estimated value of the coordinate transformation matrix at time N-1, the estimated value of the speed of the inertial coordinate system, the estimated value of the angular speed of the inertial coordinate system, the estimated value of the rear wheel center lever arm of the vehicle rear wheel coordinate system, and the estimated value of the rear wheel track of the vehicle rear wheel coordinate system; obtaining a second measurement vector according to the rear wheel speed at the moment N and the rear wheel speed coefficient estimated value at the moment N-1; and determining a measurement vector according to the preset constraint condition, the first measurement vector and the second measurement vector.
Optionally, the vehicle navigation estimation data includes a coordinate transformation matrix estimation value, a speed estimation value of an inertial coordinate system, a rear wheel center lever arm estimation value of a vehicle rear wheel coordinate system, a rear wheel track estimation value of the vehicle rear wheel coordinate system, an angular speed estimation value of the inertial coordinate system, and a rear wheel speed coefficient estimation value, and the vehicle operation data includes a rear wheel speed of the target vehicle.
The calibration module 202 is specifically configured to obtain a measurement matrix according to the estimated value of the coordinate transformation matrix at time N-1, the estimated value of the speed of the inertial coordinate system, the estimated value of the lever arm of the center lever of the rear wheel, the estimated value of the rear wheel track of the rear wheel coordinate system of the vehicle, the estimated value of the angular speed of the inertial coordinate system, the estimated value of the rear wheel speed coefficient of the rear wheel coordinate system of the vehicle, and the wheel speed of the rear wheel at time N.
Optionally, the calibration module 202 is specifically configured to obtain corrected vehicle navigation measurement data at each moment one by one according to the target error data and the corresponding vehicle navigation measurement data at each moment. And carrying out Kalman filtering according to the corrected vehicle navigation measurement data at each moment to obtain corresponding vehicle navigation estimation data.
Referring to fig. 5, fig. 5 is a block schematic diagram of an in-vehicle positioning apparatus 100 according to an embodiment of the invention. The in-vehicle positioning device 100 includes a memory 110, a processor 120, a communication module 130, an IMU140, and a GNSS receiver 150. The memory 110, the processor 120, the communication module 130, the IMU140, and the GNSS receiver 150 are electrically connected directly or indirectly to each other to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines.
Wherein the memory 110 is used for storing programs or data. The Memory 110 may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.
The processor 120 is used to read/write data or programs stored in the memory 110 and perform corresponding functions. For example, the vehicle navigation error calibration method disclosed in the above embodiments may be implemented when the computer program stored in the memory 110 is executed by the processor 120.
The communication module 130 is used for establishing a communication connection between the in-vehicle positioning device 100 and other communication terminals through a network, and for transceiving data through the network.
The IMU140 is used to measure angular velocity and acceleration of the vehicle through gyroscopes and accelerometers. The GNSS receiver 150 is operable to receive GNSS data and to determine a position, a velocity and a time of the receiver from the navigation data transmitted from the satellites.
It should be understood that the configuration shown in fig. 5 is merely a schematic diagram of the configuration of the in-vehicle positioning device 100, and that the in-vehicle positioning device 100 may also include more or fewer components than those shown in fig. 5, or have a different configuration than that shown in fig. 5. The components shown in fig. 5 may be implemented in hardware, software, or a combination thereof.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by the processor 120, implements the vehicle navigation error calibration method disclosed in the above embodiments.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present invention may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The vehicle navigation error calibration method is characterized by being applied to vehicle positioning equipment, wherein the vehicle positioning equipment comprises an IMU and a GNSS receiver; the method comprises the following steps:
acquiring vehicle navigation measurement data of each moment of a target vehicle, vehicle navigation error data of each moment and vehicle navigation estimation data of each moment; the vehicle navigation measurement data comprise GNSS data, IMU data, vehicle running data and vehicle structure data; the vehicle-mounted navigation error data represents errors generated by navigation, space structure and motion state of the target vehicle;
respectively obtaining prediction error data and measurement error data at the same moment; the prediction error data are used for representing the prediction error between the vehicle navigation estimation data at adjacent moments; the measurement error data is used for representing errors between vehicle navigation measurement data at any moment and vehicle navigation estimation data at adjacent moment;
obtaining target error data at corresponding moments according to the prediction error data and the measurement error data;
determining the vehicle navigation estimated data of each moment one by utilizing the target error data of each moment and the corresponding vehicle navigation measurement data;
And when target error data corresponding to the target moment meet a preset convergence condition, taking the target error data as a vehicle calibration result of the target vehicle.
2. The method of claim 1, wherein an mth time is a target time and an N-1 time is a time immediately preceding an N time, the N being less than or equal to the M; the step of obtaining the prediction error data comprises:
constructing a state transition matrix according to the vehicle navigation estimation data at the moment N-1;
and obtaining prediction error data at the time N according to a pre-constructed state equation of the target vehicle by utilizing the vehicle-mounted navigation error data at the time N-1 and the state transition matrix.
3. The method of claim 2, wherein the vehicle navigation estimation data includes a velocity estimation value of an inertial coordinate system, a latitude estimation value of the inertial coordinate system, a height estimation value of the inertial coordinate system, and a latitude estimation value of a vehicle motion coordinate system; the step of constructing a state transition matrix according to the vehicle navigation estimation data at the time N-1 comprises the following steps:
and obtaining the state transition matrix according to the speed estimated value of the inertial coordinate system at the time N-1, the latitude estimated value of the inertial coordinate system, the altitude estimated value of the inertial coordinate system, the latitude estimated value of the vehicle motion coordinate system and the earth rotation angular velocity.
4. The method of claim 1, wherein an mth time is a target time and an N-1 time is a time immediately preceding an N time, the N being less than or equal to the M; the step of obtaining the measurement error data comprises the following steps:
obtaining a measurement vector according to a preset constraint condition, the vehicle navigation estimation data at the time N-1 and the vehicle navigation measurement data at the time N;
obtaining a measurement matrix according to the vehicle navigation estimation data at the time N-1 and the vehicle navigation measurement data at the time N;
and obtaining the measurement error data of the N moments according to a pre-constructed observation equation of the target vehicle by using the measurement matrix and the measurement vector.
5. The method of claim 4, wherein the vehicle navigation estimation data includes a coordinate transformation matrix estimation value, a speed estimation value of an inertial coordinate system, an angular speed estimation value of the inertial coordinate system, a rear wheel center lever arm estimation value of a vehicle rear wheel coordinate system, a rear wheel track estimation value of a vehicle rear wheel coordinate system, and a rear wheel speed coefficient estimation value; the vehicle operation data includes a rear wheel speed of the target vehicle; the step of obtaining a measurement vector according to a preset constraint condition, the vehicle navigation estimation data at the time N-1 and the vehicle navigation measurement data at the time N comprises the following steps:
Obtaining a first measurement vector according to the coordinate transformation matrix estimated value at the time N-1, the speed estimated value of an inertial coordinate system, the angular speed estimated value of the inertial coordinate system, the rear wheel center lever arm estimated value of a vehicle rear wheel coordinate system and the rear wheel tread estimated value of the vehicle rear wheel coordinate system;
obtaining a second measurement vector according to the rear wheel speed at the moment N and the rear wheel speed coefficient estimated value at the moment N-1;
and determining the measurement vector according to the preset constraint condition, the first measurement vector and the second measurement vector.
6. The method of claim 4, wherein the vehicle navigation estimation data includes a coordinate transformation matrix estimation value, a speed estimation value of an inertial coordinate system, a rear wheel center lever arm estimation value of a vehicle rear wheel coordinate system, a rear wheel tread estimation value of a vehicle rear wheel coordinate system, an angular speed estimation value of an inertial coordinate system, and a rear wheel speed coefficient estimation value; the vehicle operation data includes a rear wheel speed of the target vehicle; the step of obtaining a measurement matrix according to the vehicle navigation estimation data at the time N-1 and the vehicle navigation measurement data at the time N comprises the following steps:
and obtaining the measurement matrix according to the estimated value of the coordinate transformation matrix at the time N-1, the estimated value of the speed of the inertial coordinate system, the estimated value of the lever arm of the center lever of the rear wheel, the estimated value of the rear wheel distance of the vehicle rear wheel coordinate system, the estimated value of the angular speed of the inertial coordinate system, the estimated value of the rear wheel speed coefficient of the vehicle rear wheel coordinate system and the rear wheel speed at the time N.
7. The method of claim 1, wherein the step of determining the estimated data of the car navigation at each time one by one using the target error data and the corresponding measurement data of the car navigation at each time comprises:
according to the target error data and the corresponding vehicle-mounted navigation measurement data at each moment, corrected vehicle-mounted navigation measurement data at each moment are obtained one by one;
and carrying out Kalman filtering according to the corrected vehicle navigation measurement data at each moment to obtain corresponding vehicle navigation estimation data.
8. The vehicle navigation error calibration device is characterized by being applied to vehicle-mounted positioning equipment, wherein the vehicle-mounted positioning equipment comprises an IMU and a GNSS receiver; the device comprises:
the acquisition module is used for acquiring the vehicle-mounted navigation measurement data of each moment of the target vehicle, the vehicle-mounted navigation error data of each moment and the vehicle-mounted navigation estimation data of each moment; the vehicle navigation measurement data comprise GNSS data, IMU data, vehicle running data and vehicle structure data; the vehicle-mounted navigation error data represents errors generated by navigation, space structure and motion state of the target vehicle;
the calibration module is used for respectively obtaining the prediction error data and the measurement error data at the same moment; the prediction error data are used for representing the prediction error between the vehicle navigation estimation data at adjacent moments; the measurement error data is used for representing errors between vehicle navigation measurement data at any moment and vehicle navigation estimation data at adjacent moment; obtaining target error data at corresponding moments according to the prediction error data and the measurement error data; determining the vehicle navigation estimated data of each moment one by utilizing the target error data of each moment and the corresponding vehicle navigation measurement data;
And the determining module is used for taking the target error data as a vehicle calibration result of the target vehicle when the target error data corresponding to the target moment meet the preset convergence condition.
9. An in-vehicle positioning device, characterized in that the in-vehicle positioning device comprises an IMU for measuring IMU data, a GNSS receiver for receiving GNSS data, a memory for storing a computer program, and a processor for executing the in-vehicle navigation error calibration method according to any of the claims 1-7 when the computer program is invoked.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the car navigation error calibration method according to any one of claims 1-7.
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