Positioning method, device and equipment of Ackerman chassis carrier and medium

Abstract

The invention provides an Ackerman chassis carrier positioning method and device, equipment and a medium. The method comprises the steps that an Ackerman model of a target carrier is determined; constructing an incidence relation between an Ackerman model and extended Kalman filtering; on the basis of the incidence relation, carrier parameters of the target carrier are calibrated; and in the navigation positioning process, predicting the carrier parameter residual error of the target carrier in real time, and correcting the carrier parameters based on the prediction result. According to the invention, the self-positioning precision of the carrier can be improved.

Description

Technical Field

[0001] The present application relates to the field of navigation technology, and in particular to a positioning method, device, equipment and medium for an Ackerman chassis vehicle. Background Art

[0002] Ackerman chassis are widely used in passenger cars and commercial vehicles. With the commercialization of autonomous driving projects, the requirements for vehicles have been upgraded from simply being able to drive to more stable driving. The accuracy of the mathematical model of the vehicle chassis will directly affect the accuracy and safety of the autonomous driving system. Especially when sensors such as satellites, lidars, and cameras fail, the vehicle can only rely on the prediction models of the inertial measurement unit and wheel speedometer for dead reckoning.

[0003] The lever arm value between the inertial measurement unit and the wheel speed meter is used in dead reckoning. If the vehicle chassis model is inaccurate, the lever arm estimation accuracy will be reduced, which will lead to a decrease in the accuracy of dead reckoning, affect the accuracy of vehicle autonomous positioning, and even affect driving safety. Summary of the invention

[0004] In view of this, the embodiments of the present application provide a positioning method, device, electronic device and storage medium for an Ackerman chassis vehicle, which can improve the autonomous positioning accuracy of the vehicle.

[0005] The technical solution of the embodiment of the present application is implemented as follows:

[0006] In a first aspect, an embodiment of the present application provides a positioning method for an Ackerman chassis carrier, comprising the following steps:

[0007] Determine the Ackerman model of the target vehicle;

[0008] Constructing a correlation relationship between the Ackerman model and the extended Kalman filter;

[0009] Based on the association relationship, calibrate the vehicle parameters of the target vehicle;

[0010] During the navigation and positioning process, the vehicle parameter residuals of the target vehicle are predicted in real time, and the vehicle parameters are corrected based on the prediction results.

[0011] In a second aspect, an embodiment of the present application further provides a positioning device for an Ackerman chassis carrier, the device comprising:

[0012] A determination module, used for determining an Ackerman model of a target vehicle;

[0013] A construction module, used to construct an association relationship between the Ackerman model and the extended Kalman filter;

[0014] A calibration module, used for calibrating vehicle parameters of the target vehicle based on the association relationship;

[0015] The correction module is used to predict the vehicle parameter residuals of the target vehicle in real time during the navigation and positioning process, and to correct the vehicle parameters based on the prediction results.

[0016] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to execute the positioning method of the Ackerman chassis carrier described in any one of the first aspects.

[0017] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the positioning method of the Ackerman chassis carrier described in any one of the first aspects is executed.

[0018] The embodiments of the present application have the following beneficial effects:

[0019] (1) Improve positioning accuracy: By building a correlation between the Ackerman model and the extended Kalman filter, this method can make full use of the geometric characteristics of the Ackerman model and the powerful data fusion capability of the extended Kalman filter to effectively reduce positioning errors. The Ackerman model provides a theoretical speed prediction based on the vehicle steering angle, while the extended Kalman filter can integrate multiple sensor data such as IMU and GPS in real time to make the best estimate of the state, thereby significantly improving positioning accuracy.

[0020] (2) Enhanced dynamic response capability: During the navigation and positioning process, this method predicts the residual of vehicle parameters in real time and corrects the vehicle parameters based on the prediction results. This real-time correction mechanism enables the system to quickly adapt to dynamic changes of the vehicle, such as changes in vehicle speed and steering angle, thereby maintaining high positioning accuracy and dynamic response capability.

[0021] (3) Improve calibration efficiency and accuracy: This method calibrates the vehicle parameters through iterative processing, ensuring the accuracy and reliability of the calibration results. The second iterative processing process is based on the convergence condition and the threshold condition, which can improve the calibration efficiency while ensuring accuracy. In addition, for the distance from the inertial measurement unit to the rear axle center, the first iterative processing process is also designed to obtain a more realistic distance value, further improving the positioning accuracy.

[0022] (4) Strong adaptability: This method does not rely on a specific vehicle model or sensor configuration and has strong versatility and adaptability. Whether it is different models of Ackerman chassis vehicles or sensor systems with different configurations, this method can be applied to achieve high-precision positioning by appropriately adjusting parameters and algorithm processes.

[0023] (5) Improve system stability: By predicting and correcting vehicle parameter residuals in real time, this method can effectively reduce positioning fluctuations caused by parameter errors and improve system stability and reliability. This is especially important for application scenarios that require long-term, high-precision positioning, such as autonomous driving and logistics transportation.

[0024] In summary, the embodiments of the present application have shown significant beneficial effects in improving positioning accuracy, enhancing dynamic response capabilities, improving calibration efficiency and accuracy, enhancing adaptability and improving system stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 It is a flowchart of steps S101-S104 provided in an embodiment of the present application;

[0027] Figure 2 It is a hardware connection relationship diagram provided in the embodiment of the present application;

[0028] Figure 3 is a diagram showing the structural relationship between the sensor installation position and the chassis provided in an embodiment of the present application;

[0029] Figure 4 It is a flowchart of steps S401-S403 provided in an embodiment of the present application;

[0030] Figure 5 It is a flowchart of steps S501-S502 provided in an embodiment of the present application;

[0031] Figure 6 It is a flowchart of steps S601-S602 provided in an embodiment of the present application;

[0032] Figure 7 It is a flowchart of steps S701-S702 provided in an embodiment of the present application;

[0033] Figure 8It is a flowchart of steps S801-S802 provided in an embodiment of the present application;

[0034] Fig. 9 It is a flowchart of steps S901-S902 provided in an embodiment of the present application;

[0035] Fig.10 It is a structural schematic diagram of a positioning device of an Ackerman chassis carrier provided in an embodiment of the present application;

[0036] Fig.11 It is a schematic diagram of the composition structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0037] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of sequence, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart under the guidance of the content of the present application, or remove one or more operations from the flowchart.

[0038] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0039] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0040] In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0041] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0043] See also Figure 1 , Figure 1 is a flow chart of steps S101-S104 of the positioning method of the Ackerman chassis carrier provided in the embodiment of the present application, which will be combined with Figure 1 Steps S101-S104 are shown for explanation.

[0044] In step S101 , the Ackerman model of the target vehicle is determined.

[0045] Here, the Ackerman structure chassis is similar to the real car chassis, where the front two wheels are used to control the direction and the rear two wheels are used to control the speed. When turning, due to the difference in the speed of the inner and outer wheels, it is necessary to ensure that the differential between the wheels is properly adjusted to maintain the stability and handling of the vehicle. The establishment of the Ackerman model involves parameters such as the front and rear wheel speeds (v_L, v_R), steering angle (θ), and vehicle size (such as wheelbase l and track 2d).

[0046] In the embodiments of the present application, it is applicable to various passenger cars and commercial vehicles with Ackerman chassis. The components involved include a wheel speedometer, an angular encoder, an inertial measurement unit and a computing unit. The wheel speedometer includes a left rear wheel speedometer and a right rear wheel speedometer. The angular encoder includes a left front wheel angular encoder and a right front wheel angular encoder. The wheel speedometer is used to collect speed data of each wheel of the target vehicle. The angular encoder is used to collect angular deviation data of each wheel of the target vehicle. The inertial measurement unit is used to collect acceleration and angular velocity data of the target vehicle. The computing unit is used to process the data collected by the wheel speedometer, the angular encoder and the inertial measurement unit. The hardware connection relationship diagram is shown in FIG. Figure 2 The structural relationship between the sensor installation position and the chassis is shown in Figure 3 As shown, l is the wheelbase, d is the rear axle track, and r is the distance from the sensitive center of the inertial measurement unit to the center of the rear axle.

[0047] In step S102, an association relationship between the Ackerman model and the extended Kalman filter is constructed.

[0048] Here, the extended Kalman filter (EKF) is a Kalman filter method suitable for nonlinear systems. In the Ackerman chassis vehicle positioning, the state equation and observation equation of the Ackerman model are combined with the EKF algorithm to achieve accurate estimation of the vehicle state. The state equation is usually abstracted from the physical meaning of the Ackerman model and represents the dynamic characteristics of the vehicle over time; the observation equation reflects the vehicle state information obtained by sensors and other measurement methods.

[0049] In step S103, based on the association relationship, the vehicle parameters of the target vehicle are calibrated.

[0050] Here, after determining the correlation between the Ackerman model and the EKF, the relevant parameters of the vehicle need to be calibrated. This includes but is not limited to vehicle size (wheelbase, track), wheel speed, steering angle, etc. The calibration process aims to ensure that these parameters match the physical characteristics of the actual vehicle, thereby improving the accuracy and reliability of the positioning system.

[0051] In step S104, during the navigation and positioning process, the vehicle parameter residuals of the target vehicle are predicted in real time, and the vehicle parameters are corrected based on the prediction results.

[0052] Here, during the navigation and positioning process, due to the influence of various external factors (such as uneven road surface, wind resistance, etc.) and internal factors (such as sensor noise, mechanical wear, etc.), there may be a deviation between the actual state of the vehicle and the state predicted by the model. In order to reduce this deviation, it is necessary to predict the residual of the vehicle parameters in real time and correct the vehicle parameters based on the prediction results. The EKF algorithm is used to predict and update the vehicle state. In the prediction stage, the vehicle state at the current moment is predicted based on the state estimate value at the previous moment and the state equation of the Ackerman model; in the update stage, the prediction result is corrected based on the observation value and observation equation at the current moment. The residual of the vehicle parameter is calculated, that is, the difference between the actual observation value and the model prediction value. According to the size and direction of the residual, the vehicle parameters are corrected to reduce the deviation.

[0053] The above method, by combining the Ackerman model with the extended Kalman filter algorithm, achieves accurate estimation and real-time correction of the vehicle state. This method not only improves the accuracy and reliability of vehicle positioning, but also has certain robustness and adaptability, and can cope with the challenges of various complex environments and working conditions.

[0054] In some embodiments, see Figure 4 , Figure 4 It is a flowchart of steps S401-S403 provided in an embodiment of the present application. The construction of the association relationship between the Ackerman model and the extended Kalman filter can be achieved through steps S401-S403, which will be explained in combination with each step.

[0055] In step S401 , the steering angle of the Ackerman steering axis center is calculated.

[0056] In step S402, the distance from the sensitive center of the inertial measurement unit to the center of the rear axle is calculated.

[0057] In step S403, based on the steering angle and the distance, a correlation relationship between the Ackerman model and the extended Kalman filter is determined.

[0058] Combining steps S401-S403, it can be seen that a key feature of the Ackerman chassis is that the front wheels maintain a certain angle relationship when turning. This relationship is usually described by a virtual steering axis center (Instant Center of Rotation, ICR). The steering axis center is the point around which the front wheels theoretically rotate when turning. It is located in front of the vehicle and changes with the steering angle. Calculating this steering angle is a key step in understanding the dynamic behavior of the Ackerman chassis.

[0059] The calculation of the steering angle usually depends on the actual steering angle of the front wheels (obtained by the steering sensor) and the vehicle's geometric parameters (such as wheelbase, track width, etc.). In the Ackerman model, this steering angle is directly related to the vehicle's steering input and determines the vehicle's overall steering behavior.

[0060] Inertial Measurement Unit (IMU) is a key sensor used in navigation systems to measure vehicle acceleration, angular velocity and other parameters. Its sensitive center is the geometric center of the measuring element inside the IMU and is also the reference point for the IMU output data. In order to accurately fuse the IMU data into the vehicle's motion model, it is necessary to know the distance between the IMU's sensitive center and the center of the vehicle's rear axle. The calculation of this distance depends on the installation position of the IMU on the vehicle and the vehicle's geometric dimensions. Usually, this distance is given directly by measurement or vehicle design parameters.

[0061] Once the steering angle of the steering axis center and the distance from the IMU sensitive center to the rear axle center are available, this information can be integrated into the Ackerman model and the association relationship with the extended Kalman filter can be constructed. Under the framework of the EKF, the state equation of the Ackerman model will include state variables such as the vehicle's position, velocity, and steering angle, which will be updated based on the kinematic characteristics of the Ackerman chassis. At the same time, the IMU's measurement data (such as acceleration and angular velocity) will be input into the EKF's observation equation as observations. The establishment of the association relationship involves linking the state variables of the Ackerman model with the IMU's measurement data through mathematical formulas, so that the EKF algorithm can use these data to accurately estimate the vehicle state. Specifically, the steering angle and the distance from the IMU to the rear axle will be used to calculate the displacement, speed change, etc. of the vehicle during the turn, and these calculation results will serve as an important basis for the EKF state update.

[0062] The above method can effectively construct the correlation between the Ackerman model and the extended Kalman filter by calculating the steering angle of the Ackerman steering axis center and the distance from the IMU sensitive center to the rear axle center, thereby achieving high-precision vehicle positioning.

[0063] In some embodiments, see Figure 5 , Figure 5 It is a flowchart diagram of steps S501-S502 provided in an embodiment of the present application. The calculation of the steering angle of the center of the Ackerman steering axis can be achieved through steps S501-S502, which will be explained in combination with each step.

[0064] In step S501, the wheelbase, rear axle track, rear axle left wheel speed and rear axle right wheel speed of the target vehicle are determined.

[0065] In step S502, the steering angle of the Ackerman steering axis center is calculated according to the wheelbase, the rear axle track, the rear axle left wheel speed and the rear axle right wheel speed.

[0066] For example, the steering angle ψ of the Ackerman steering axis center can be calculated by the following formula:

[0067] ψ=arctan((2l / d)·(v3-v2) / (v2+v3));

[0068] Among them, v2 and v3 are the speeds of the left and right wheels on the rear axle respectively.

[0069] In some embodiments, see Figure 6 , Figure 6 It is a flowchart of steps S601-S602 provided in an embodiment of the present application. The calculation of the distance from the sensitive center of the inertial measurement unit to the center of the rear axle can be achieved through steps S601-S602, which will be explained in combination with each step.

[0070] In step S601, the longitudinal distance component, the lateral distance component, the longitudinal residual and the lateral residual from the inertial measurement unit of the target vehicle to the rear axle center are determined.

[0071] In step S602, the distance from the sensitive center of the inertial measurement unit to the center of the rear axle is calculated based on the longitudinal distance component, the lateral distance component, the longitudinal residual and the lateral residual.

[0072] For example, the distance r from the sensitive center of the inertial measurement unit to the center of the rear axle can be calculated by the following formula:

[0073]

[0074] Among them, the longitudinal distance component from the inertial measurement unit to the center of the rear axle is r1, and the lateral distance component is r2, and the initial value of the distance is 0; Δx and Δy are the longitudinal and lateral residuals from the inertial measurement unit to the center of the rear axle, respectively.

[0075] In some embodiments, the method further comprises:

[0076] When the iteration condition is met, a first iteration is performed on the distance from the sensitive center of the inertial measurement unit to the center of the rear axle to obtain a true value of the distance from the sensitive center of the inertial measurement unit to the center of the rear axle, wherein the iteration condition is that the wheel angle deviation of the target vehicle is greater than or equal to a preset value.

[0077] Here, with the filtering process, r gradually approaches the true value, that is, the iteration is the actual distance from the inertial measurement unit to the center of the rear axle. It should be noted that if the wheel angle deviation δ is too small, the iteration accuracy of r will drop significantly. Therefore, it is necessary to judge δ in advance, and when it is greater than a certain threshold, r is iterated. Therefore, the iteration condition is set to "the wheel angle deviation of the target vehicle is greater than or equal to the preset value". This preset value is set according to the specific characteristics of the vehicle and the requirements of the positioning system. When the wheel angle deviation exceeds this value, we believe that the vehicle's posture change is large enough to require updating or recalculating the distance from the IMU sensitive center to the rear axle center.

[0078] In some embodiments, see Figure 7 , Figure 7 It is a flowchart of steps S701-S702 provided in an embodiment of the present application. The determination of the association between the Ackerman model and the extended Kalman filter based on the steering angle and the distance can be achieved through steps S701-S702, which will be explained in combination with each step.

[0079] In step S701 , an Ackerman model derived speed is determined based on the steering angle and the distance, wherein the Ackerman derived speed represents a speed at an inertial measurement unit calculated by an Ackerman model.

[0080] In step S702, a velocity observation of an extended Kalman filter is determined based on a difference between a forward velocity component of the vehicle calculated by an inertial measurement unit and a velocity derived from the Ackerman model.

[0081] Here, first, based on the Ackerman steering model and the known steering angle and the distance from the IMU sensitive center to the rear axle center, we can derive the theoretical speed at the IMU location. This derivation process usually involves geometric analysis and vehicle kinematic models. In the Ackerman model, it is assumed that the turning radius of all wheels intersects at a common point (i.e., the center of the steering axis), so the turning radius of the vehicle can be calculated by the steering angle and wheelbase. However, here we are more concerned about the speed at the IMU location, which usually needs to be derived through the vehicle's kinematic equations and geometric relationships. Assuming that the vehicle turns at a constant speed and ignoring nonlinear factors such as tire side slip, the speed at the IMU location can be approximately calculated by the rear axle center speed and steering angle and the distance from the IMU to the rear axle. It should be noted that the calculation here may need to be adjusted according to the specific vehicle model and IMU installation location.

[0082] The extended Kalman filter requires an observation to update the state estimate. In this scenario, the observation can be the difference between the forward velocity component of the vehicle solved by the IMU and the velocity derived from the Ackerman model. The observation can be defined as: the difference between the forward velocity component of the vehicle solved by the inertial measurement unit and the velocity derived from the Ackerman model. This difference reflects the difference between the IMU measurement and the Ackerman model prediction, which may be caused by a variety of factors, including sensor noise, vehicle dynamic changes, model errors, etc.

[0083] In the update step of the extended Kalman filter, the observation z is used to correct the state estimate. Specifically, the observation is used to calculate the Kalman gain, and then the Kalman gain and the observation are used to update the state estimate (including speed, position, etc.) and the covariance matrix. This process is iterated continuously, and each iteration updates the state estimate based on the new observation, thereby improving positioning accuracy and dynamic response capability.

[0084] For example, z v =v imu -r·cos(ψ)·(v3-v2) / d;

[0085] Among them, z v is the velocity observation of the extended Kalman filter, v imu is the forward velocity component of the vehicle solved by the inertial measurement unit.

[0086] In some embodiments, see Figure 8 , Figure 8 It is a flow chart of steps S801-S802 provided in an embodiment of the present application. The vehicle parameters include a longitudinal distance component and a lateral distance component from the inertial measurement unit to the center of the rear axle. The calibration of the vehicle parameters of the target vehicle can be achieved through steps S801-S802, which will be explained in combination with each step.

[0087] In step S801, a second iterative process is performed on the vehicle parameters to obtain a first predicted value of the vehicle parameters.

[0088] In step S802, if the first prediction value satisfies the verification condition, the vehicle parameters of the target vehicle are calibrated based on the first prediction value, and the calibration is ended, wherein the verification condition is determined based on at least one of a convergence condition and a threshold condition.

[0089] Here, the second iterative process is a process of further optimizing the vehicle parameters through an iterative algorithm based on the initial estimation or measurement of the vehicle parameters. The iterative algorithm can be based on a variety of mathematical models and optimization techniques, such as the least squares method, the gradient descent method, the genetic algorithm, and the like.

[0090] During the calibration process, the vehicle parameters need to be predicted. The vehicle parameters here refer to the longitudinal distance component r1 and the lateral distance component r2 from the inertial measurement unit to the center of the rear axle. During the initialization of the Kalman filter, these two distances have been introduced into the state variables, and the initial values ​​of the distances are both 0.

[0091] The prediction is made in an iterative way. The iterative method is as follows:

[0092] The longitudinal distance at time k+1 = the longitudinal distance at time k + the estimated value of the longitudinal closed-loop feedback of the current Kalman filter;

[0093] The lateral distance at time k+1 = the lateral distance at time k + the estimated value of the current Kalman filter lateral closed-loop feedback.

[0094] In some embodiments, see Fig. 9 , Fig. 9 It is a flow chart of steps S901-S902 provided in an embodiment of the present application, wherein the vehicle parameter residuals include longitudinal residuals and lateral residuals from the inertial measurement unit to the center of the rear axle, and the real-time prediction of the vehicle parameter residuals of the target vehicle and the correction of the vehicle parameters based on the prediction results can be achieved through steps S901-S902, which will be explained in conjunction with each step.

[0095] In step S901, a third iteration process is performed on the vehicle parameter residual to obtain a second predicted value of the vehicle parameter residual;

[0096] In step S902, the vehicle parameter is calibrated based on the second prediction value.

[0097] Here, in the navigation process, since most of the distance has been predicted and compensated in the early stage, only residuals exist. The vehicle parameter residuals refer to the "longitudinal residual Δx and lateral residual Δy from the inertial measurement unit to the rear axle center". During the initialization of the Kalman filter, these two residuals have been introduced into the state variables, and the initial values ​​of the residuals are all 0.

[0098] During the navigation process, the iteration method is similar to the above iteration method and will not be repeated here.

[0099] In the embodiment of the present application, road tests of different vehicle models are carried out, which are divided into two parts: calibration test and navigation test.

[0100] 1) Calibration test

[0101] The inertial measurement unit and the rear axle center are brought to the same horizontal plane by means of a special test fixture and instrument, and the real distance between the two is measured. The vehicle is driven along an "8" route for 5 minutes, and the predicted distance between the inertial measurement unit and the rear axle center is obtained, as shown in Table 1.

[0102] Table 1 Comparison of lever arm prediction accuracy

[0103]

[0104] It can be seen from Table 1 that the predicted value of the lever arm by the model described in the embodiment of the present application is closer to the true value, and the residual is better than 3 cm.

[0105] 2) Navigation test

[0106] The vehicle drives freely. After using multi-information fusion navigation for 5 minutes, all peripheral sensors except the inertial measurement unit, wheel speed meter and angle encoder are disconnected, and the system enters the dead reckoning stage. In this stage, the vehicle stops after driving 50 meters, and the difference between the dead reckoning position and the real position is compared (the real position is obtained through differential satellites. When comparing the position error, the dead reckoning position and the differential satellite position have been transferred to the same output point). Tests were carried out using a small van, a single-bridge tractor, and a double-bridge tractor, and 3 verifications were performed under each condition.

[0107] Table 2 Comparison of dead reckoning capabilities

[0108]

[0109] It can be seen from Table 2 that the dead reckoning accuracy of the model described in this proposal is better than that of the differential model, and the stability of the fusion positioning module of the autonomous driving vehicle is improved.

[0110] In summary, the embodiments of the present application are applicable to various types of Ackerman chassis vehicles, and can improve the accuracy of autonomous navigation in the event of failure of satellites, lidars, and cameras, and further enhance the stability of autonomous driving vehicles.

[0111] In summary, the embodiments of the present application have the following beneficial effects:

[0112] (1) Improve positioning accuracy: By building a correlation between the Ackerman model and the extended Kalman filter, this method can make full use of the geometric characteristics of the Ackerman model and the powerful data fusion capability of the extended Kalman filter to effectively reduce positioning errors. The Ackerman model provides a theoretical speed prediction based on the vehicle steering angle, while the extended Kalman filter can integrate multiple sensor data such as IMU and GPS in real time to make the best estimate of the state, thereby significantly improving positioning accuracy.

[0113] (2) Enhanced dynamic response capability: During the navigation and positioning process, this method predicts the residual of vehicle parameters in real time and corrects the vehicle parameters based on the prediction results. This real-time correction mechanism enables the system to quickly adapt to dynamic changes of the vehicle, such as changes in vehicle speed and steering angle, thereby maintaining high positioning accuracy and dynamic response capability.

[0114] (3) Improve calibration efficiency and accuracy: This method calibrates the vehicle parameters through iterative processing, ensuring the accuracy and reliability of the calibration results. The second iterative processing process is based on the convergence condition and the threshold condition, which can improve the calibration efficiency while ensuring accuracy. In addition, for the distance from the inertial measurement unit to the rear axle center, the first iterative processing process is also designed to obtain a more realistic distance value, further improving the positioning accuracy.

[0115] (4) Strong adaptability: This method does not rely on a specific vehicle model or sensor configuration and has strong versatility and adaptability. Whether it is different models of Ackerman chassis vehicles or sensor systems with different configurations, this method can be applied to achieve high-precision positioning by appropriately adjusting parameters and algorithm processes.

[0116] (5) Improve system stability: By predicting and correcting vehicle parameter residuals in real time, this method can effectively reduce positioning fluctuations caused by parameter errors and improve system stability and reliability. This is especially important for application scenarios that require long-term, high-precision positioning, such as autonomous driving and logistics transportation.

[0117] In summary, the embodiments of the present application have shown significant beneficial effects in improving positioning accuracy, enhancing dynamic response capabilities, improving calibration efficiency and accuracy, enhancing adaptability and improving system stability.

[0118] Based on the same inventive concept, an Ackerman chassis carrier positioning device corresponding to the Ackerman chassis carrier positioning method in the first embodiment is also provided in the embodiments of the present application. Since the principle of solving the problem by the device in the embodiments of the present application is similar to the above-mentioned Ackerman chassis carrier positioning method, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0119] like Fig.10 As shown, Fig.10 1 is a schematic diagram of the structure of a positioning device 1000 for an Ackerman chassis vehicle provided in an embodiment of the present application. The positioning device 1000 for an Ackerman chassis vehicle includes:

[0120] A determination module 1001 is used to determine an Ackerman model of a target vehicle;

[0121] A construction module 1002 is used to construct an association relationship between the Ackerman model and the extended Kalman filter;

[0122] A calibration module 1003, configured to calibrate vehicle parameters of the target vehicle based on the association relationship;

[0123] The correction module 1004 is used to predict the vehicle parameter residuals of the target vehicle in real time during the navigation and positioning process, and to correct the vehicle parameters based on the prediction results.

[0124] In a possible implementation manner, the construction module 1002 constructs the association relationship between the Ackerman model and the extended Kalman filter, including:

[0125] Calculate the steering angle at the center of the Ackerman steering axis;

[0126] Calculate the distance from the sensitive center of the inertial measurement unit to the center of the rear axle;

[0127] Based on the steering angle and the distance, a correlation relationship between the Ackerman model and the extended Kalman filter is determined.

[0128] In one possible implementation, the construction module 1002 calculates the steering angle of the center of the Ackerman steering axis, including:

[0129] Determining the wheelbase, rear axle track, rear axle left wheel speed, and rear axle right wheel speed of the target vehicle;

[0130] The steering angle of the Ackerman steering axis center is calculated according to the wheelbase, the rear axle track, the rear axle left wheel speed and the rear axle right wheel speed.

[0131] In one possible implementation, the construction module 1002 calculates the distance from the sensitive center of the inertial measurement unit to the center of the rear axle, including:

[0132] Determine a longitudinal distance component, a lateral distance component, a longitudinal residual, and a lateral residual from an inertial measurement unit of the target vehicle to a rear axle center;

[0133] Based on the longitudinal distance component, the lateral distance component, the longitudinal residual and the lateral residual, a distance from a sensitive center of an inertial measurement unit to a rear axle center is calculated.

[0134] In a possible implementation manner, the construction module 1002 further includes:

[0135] When the iteration condition is met, a first iteration is performed on the distance from the sensitive center of the inertial measurement unit to the center of the rear axle to obtain a true value of the distance from the sensitive center of the inertial measurement unit to the center of the rear axle, wherein the iteration condition is that the wheel angle deviation of the target vehicle is greater than or equal to a preset value.

[0136] In a possible implementation manner, the construction module 1002 determines the association relationship between the Ackerman model and the extended Kalman filter based on the steering angle and the distance, including:

[0137] Determining an Ackerman model-derived speed based on the steering angle and the distance, wherein the Ackerman model-derived speed represents a speed at an inertial measurement unit calculated by an Ackerman model;

[0138] The velocity observation of the extended Kalman filter is determined based on the difference between the forward velocity component of the vehicle calculated by the inertial measurement unit and the velocity derived from the Ackerman model.

[0139] In a possible implementation manner, the vehicle parameters include a longitudinal distance component and a lateral distance component from the inertial measurement unit to the center of the rear axle, and the calibration module 1003 calibrates the vehicle parameters of the target vehicle, including:

[0140] Performing a second iterative process on the vehicle parameter to obtain a first predicted value of the vehicle parameter;

[0141] If the first prediction value satisfies the verification condition, the vehicle parameters of the target vehicle are calibrated based on the first prediction value, and the calibration is ended, wherein the verification condition is determined based on at least one of a convergence condition and a threshold condition.

[0142] In a possible implementation manner, the vehicle parameter residual includes a longitudinal residual and a lateral residual from the inertial measurement unit to the rear axle center. The correction module 1004 predicts the vehicle parameter residual of the target vehicle in real time and corrects the vehicle parameter based on the prediction result, including:

[0143] Performing a third iterative process on the vehicle parameter residual to obtain a second predicted value of the vehicle parameter residual;

[0144] The vehicle parameter is corrected based on the second prediction value.

[0145] Those skilled in the art should understand that Fig.10 The functions of the various units in the positioning device 1000 for the Ackerman chassis carrier shown can be understood by referring to the related description of the positioning method for the Ackerman chassis carrier mentioned above. Fig.10 The functions of the various units in the positioning device 1000 for the Ackerman chassis carrier shown can be implemented by a program running on a processor, or can be implemented by a specific logic circuit.

[0146] The positioning device of the Ackerman chassis carrier has the following beneficial effects:

[0147] (1) Improve positioning accuracy: By building a correlation between the Ackerman model and the extended Kalman filter, this method can make full use of the geometric characteristics of the Ackerman model and the powerful data fusion capability of the extended Kalman filter to effectively reduce positioning errors. The Ackerman model provides a theoretical speed prediction based on the vehicle steering angle, while the extended Kalman filter can integrate multiple sensor data such as IMU and GPS in real time to make the best estimate of the state, thereby significantly improving positioning accuracy.

[0148] (2) Enhanced dynamic response capability: During the navigation and positioning process, this method predicts the residual of vehicle parameters in real time and corrects the vehicle parameters based on the prediction results. This real-time correction mechanism enables the system to quickly adapt to dynamic changes of the vehicle, such as changes in vehicle speed and steering angle, thereby maintaining high positioning accuracy and dynamic response capability.

[0149] (3) Improve calibration efficiency and accuracy: This method calibrates the vehicle parameters through iterative processing, ensuring the accuracy and reliability of the calibration results. The second iterative processing process is based on the convergence condition and the threshold condition, which can improve the calibration efficiency while ensuring accuracy. In addition, for the distance from the inertial measurement unit to the rear axle center, the first iterative processing process is also designed to obtain a more realistic distance value, further improving the positioning accuracy.

[0150] (4) Strong adaptability: This method does not rely on a specific vehicle model or sensor configuration and has strong versatility and adaptability. Whether it is different models of Ackerman chassis vehicles or sensor systems with different configurations, this method can be applied to achieve high-precision positioning by appropriately adjusting parameters and algorithm processes.

[0151] (5) Improve system stability: By predicting and correcting vehicle parameter residuals in real time, this method can effectively reduce positioning fluctuations caused by parameter errors and improve system stability and reliability. This is especially important for application scenarios that require long-term, high-precision positioning, such as autonomous driving and logistics transportation.

[0152] In summary, the embodiments of the present application have shown significant beneficial effects in improving positioning accuracy, enhancing dynamic response capabilities, improving calibration efficiency and accuracy, enhancing adaptability and improving system stability.

[0153] like Fig.11 As shown, Fig.11 The present invention provides a schematic diagram of the structure of an electronic device 1100, wherein the electronic device 1100 includes:

[0154] A processor 1101, a storage medium 1102 and a bus 1103, wherein the storage medium 1102 stores machine-readable instructions executable by the processor 1101. When the electronic device 1100 is running, the processor 1101 communicates with the storage medium 1102 via the bus 1103, and the processor 1101 executes the machine-readable instructions to perform the steps of the positioning method of the Ackerman chassis carrier described in the embodiment of the present application.

[0155] In actual application, the components in the electronic device 1100 are coupled together via the bus 1103. It is understood that the bus 1103 is used to realize the connection and communication between these components. In addition to the data bus, the bus 1103 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Fig.11 Various buses are labeled as bus 1103.

[0156] The above electronic device has the following beneficial effects:

[0157] (1) Improve positioning accuracy: By building a correlation between the Ackerman model and the extended Kalman filter, this method can make full use of the geometric characteristics of the Ackerman model and the powerful data fusion capability of the extended Kalman filter to effectively reduce positioning errors. The Ackerman model provides a theoretical speed prediction based on the vehicle steering angle, while the extended Kalman filter can integrate multiple sensor data such as IMU and GPS in real time to make the best estimate of the state, thereby significantly improving positioning accuracy.

[0158] (2) Enhanced dynamic response capability: During the navigation and positioning process, this method predicts the residual of vehicle parameters in real time and corrects the vehicle parameters based on the prediction results. This real-time correction mechanism enables the system to quickly adapt to dynamic changes of the vehicle, such as changes in vehicle speed and steering angle, thereby maintaining high positioning accuracy and dynamic response capability.

[0159] (3) Improve calibration efficiency and accuracy: This method calibrates the vehicle parameters through iterative processing, ensuring the accuracy and reliability of the calibration results. The second iterative processing process is based on the convergence condition and the threshold condition, which can improve the calibration efficiency while ensuring accuracy. In addition, for the distance from the inertial measurement unit to the rear axle center, the first iterative processing process is also designed to obtain a more realistic distance value, further improving the positioning accuracy.

[0160] (4) Strong adaptability: This method does not rely on a specific vehicle model or sensor configuration and has strong versatility and adaptability. Whether it is different models of Ackerman chassis vehicles or sensor systems with different configurations, this method can be applied to achieve high-precision positioning by appropriately adjusting parameters and algorithm processes.

[0161] (5) Improve system stability: By predicting and correcting vehicle parameter residuals in real time, this method can effectively reduce positioning fluctuations caused by parameter errors and improve system stability and reliability. This is especially important for application scenarios that require long-term, high-precision positioning, such as autonomous driving and logistics transportation.

[0162] In summary, the embodiments of the present application have shown significant beneficial effects in improving positioning accuracy, enhancing dynamic response capabilities, improving calibration efficiency and accuracy, enhancing adaptability and improving system stability.

[0163] The embodiment of the present application further provides a computer-readable storage medium, which stores executable instructions. When the executable instructions are executed by at least one processor 1101, the positioning method of the Ackerman chassis carrier described in the embodiment of the present application is implemented.

[0164] In some embodiments, the storage medium can be a magnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface storage, an optical disk, or a compact disc read-only memory (CDROM); it can also be various devices including one or any combination of the above memories.

[0165] In some embodiments, executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0166] As an example, executable instructions may, but do not necessarily, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, for example, in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (for example, files storing one or more modules, subroutines, or code portions).

[0167] By way of example, executable instructions may be deployed to be executed on one computing device, or on multiple computing devices located at one site, or on multiple computing devices distributed across multiple sites and interconnected by a communication network.

[0168] The computer-readable storage medium has the following advantages:

[0169] (1) Improve positioning accuracy: By building a correlation between the Ackerman model and the extended Kalman filter, this method can make full use of the geometric characteristics of the Ackerman model and the powerful data fusion capability of the extended Kalman filter to effectively reduce positioning errors. The Ackerman model provides a theoretical speed prediction based on the vehicle steering angle, while the extended Kalman filter can integrate multiple sensor data such as IMU and GPS in real time to make the best estimate of the state, thereby significantly improving positioning accuracy.

[0170] (2) Enhanced dynamic response capability: During the navigation and positioning process, this method predicts the residual of vehicle parameters in real time and corrects the vehicle parameters based on the prediction results. This real-time correction mechanism enables the system to quickly adapt to dynamic changes of the vehicle, such as changes in vehicle speed and steering angle, thereby maintaining high positioning accuracy and dynamic response capability.

[0171] (3) Improve calibration efficiency and accuracy: This method calibrates the vehicle parameters through iterative processing, ensuring the accuracy and reliability of the calibration results. The second iterative processing process is based on the convergence condition and the threshold condition, which can improve the calibration efficiency while ensuring accuracy. In addition, for the distance from the inertial measurement unit to the rear axle center, the first iterative processing process is also designed to obtain a more realistic distance value, further improving the positioning accuracy.

[0172] (4) Strong adaptability: This method does not rely on a specific vehicle model or sensor configuration and has strong versatility and adaptability. Whether it is different models of Ackerman chassis vehicles or sensor systems with different configurations, this method can be applied to achieve high-precision positioning by appropriately adjusting parameters and algorithm processes.

[0173] (5) Improve system stability: By predicting and correcting vehicle parameter residuals in real time, this method can effectively reduce positioning fluctuations caused by parameter errors and improve system stability and reliability. This is especially important for application scenarios that require long-term, high-precision positioning, such as autonomous driving and logistics transportation.

[0174] In summary, the embodiments of the present application have shown significant beneficial effects in improving positioning accuracy, enhancing dynamic response capabilities, improving calibration efficiency and accuracy, enhancing adaptability and improving system stability.

[0175] In the several embodiments provided in the present application, it should be understood that the disclosed methods and electronic devices can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0176] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0177] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0178] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a platform server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0179] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A positioning method for an Ackerman chassis vehicle, characterized in that: The method comprises: Determine the Ackerman model of the target vehicle; Constructing a correlation relationship between the Ackerman model and the extended Kalman filter; Based on the association relationship, calibrate the vehicle parameters of the target vehicle; During the navigation and positioning process, the vehicle parameter residuals of the target vehicle are predicted in real time, and the vehicle parameters are corrected based on the prediction results.

2. The method according to claim 1, characterized in that The constructing of the association relationship between the Ackerman model and the extended Kalman filter includes: Calculate the steering angle at the center of the Ackerman steering axis; Calculate the distance from the sensitive center of the inertial measurement unit to the center of the rear axle; Based on the steering angle and the distance, a correlation relationship between the Ackerman model and the extended Kalman filter is determined.

3. The method according to claim 2, characterized in that The calculating the steering angle of the center of the Ackerman steering axis comprises: Determining the wheelbase, rear axle track, rear axle left wheel speed, and rear axle right wheel speed of the target vehicle; The steering angle of the Ackerman steering axis center is calculated according to the wheelbase, the rear axle track, the rear axle left wheel speed and the rear axle right wheel speed.

4. The method according to claim 2, characterized in that: The calculating the distance from the sensitive center of the inertial measurement unit to the center of the rear axle includes: Determine a longitudinal distance component, a lateral distance component, a longitudinal residual, and a lateral residual from an inertial measurement unit of the target vehicle to a rear axle center; Based on the longitudinal distance component, the lateral distance component, the longitudinal residual and the lateral residual, a distance from a sensitive center of an inertial measurement unit to a rear axle center is calculated.

5. The method according to claim 4, characterized in that The method further comprises: When the iteration condition is met, a first iteration is performed on the distance from the sensitive center of the inertial measurement unit to the center of the rear axle to obtain a true value of the distance from the sensitive center of the inertial measurement unit to the center of the rear axle, wherein the iteration condition is that the wheel angle deviation of the target vehicle is greater than or equal to a preset value.

6. The method according to claim 2, characterized in that The determining the association relationship between the Ackerman model and the extended Kalman filter based on the steering angle and the distance includes: Determining an Ackerman model-derived speed based on the steering angle and the distance, wherein the Ackerman model-derived speed represents a speed at an inertial measurement unit calculated by an Ackerman model; The velocity observation of the extended Kalman filter is determined based on the difference between the forward velocity component of the vehicle calculated by the inertial measurement unit and the velocity derived from the Ackerman model.

7. The method according to claim 1, characterized in that The vehicle parameters include a longitudinal distance component and a lateral distance component from an inertial measurement unit to a rear axle center. The calibrating the vehicle parameters of the target vehicle includes: Performing a second iterative process on the vehicle parameter to obtain a first predicted value of the vehicle parameter; If the first prediction value satisfies the verification condition, the vehicle parameters of the target vehicle are calibrated based on the first prediction value, and the calibration is ended, wherein the verification condition is determined based on at least one of a convergence condition and a threshold condition.

8. The method according to claim 1, characterized in that The vehicle parameter residual includes a longitudinal residual and a lateral residual from an inertial measurement unit to a rear axle center, and the vehicle parameter residual of the target vehicle is predicted in real time, and the vehicle parameter is corrected based on the prediction result, including: Performing a third iterative process on the vehicle parameter residual to obtain a second predicted value of the vehicle parameter residual; The vehicle parameter is corrected based on the second prediction value.

9. A positioning device for an Ackerman chassis vehicle, characterized in that: The device comprises: A determination module, used for determining an Ackerman model of a target vehicle; A construction module, used to construct an association relationship between the Ackerman model and the extended Kalman filter; A calibration module, used for calibrating vehicle parameters of the target vehicle based on the association relationship; The correction module is used to predict the vehicle parameter residuals of the target vehicle in real time during the navigation and positioning process, and to correct the vehicle parameters based on the prediction results.

10. An electronic device, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the storage medium communicate via the bus, and the processor executes the machine-readable instructions to perform the positioning method of the Ackerman chassis carrier as described in any one of claims 1 to 8.

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