Vehicle positioning method, electronic device, vehicle, medium and computer program product

Abstract

The embodiment of the invention provides a vehicle positioning method, an electronic device, a vehicle, a medium and a computer program product, and the method comprises the steps: obtaining a first course angle increment based on the unit cycle driving distance of a left rear wheel, the unit cycle driving distance of a right rear wheel and a rear wheel distance; acquiring a second course angle increment based on the inertial measurement unit; and on the basis of the first course angle increment, the second course angle increment and the vehicle pose at the previous moment, the target vehicle pose at the moment is predicted, and the time difference between the previous moment and the moment is a unit period. Namely, the first course angle increment of the vehicle is calculated by using fewer parameters, and the real-time pose of the vehicle is calculated through the first course angle increment and the second course angle increment, so that the vehicle is positioned, the calculation complexity is reduced, and the vehicle positioning accuracy is improved.

Description

Technical Field

[0001] The present application belongs to the field of automobile technology, and specifically relates to a vehicle positioning method, an electronic device, a vehicle, a medium, and a computer program product. Background Art

[0002] In the automotive industry, with the acceleration of urbanization and the increase in the number of vehicles, the problem of parking difficulties has become increasingly prominent. Automatic parking technology, with its lightness and efficiency, has gradually become an important means to solve the parking problem.

[0003] In the existing technology, the vehicle's heading angle increment is calculated by querying a proportional relationship table between the steering wheel angle and the front wheel angle, and combining parameters such as the vehicle's driving distance, vehicle wheelbase, rear track, and turning radius to update the vehicle's position, thereby achieving automatic parking operations.

[0004] However, using the existing technology, the calculation of the heading angle increment requires a large number of calibration parameters, resulting in high computational complexity. Summary of the Invention

[0005] Embodiments of the present application provide a vehicle positioning method, an electronic device, a vehicle, a medium, and a computer program product to solve the problem in the prior art that the calculation of the heading angle increment requires a large number of calibration parameters, resulting in high computational complexity.

[0006] A first aspect of an embodiment of the present application provides a vehicle positioning method, comprising:

[0007] Obtaining a first heading angle increment based on the unit cycle travel distance of the left rear wheel, the unit cycle travel distance of the right rear wheel, and the rear wheelbase;

[0008] Acquire a second heading angle increment based on the inertial measurement unit;

[0009] The target vehicle posture at the current moment is predicted based on the first heading angle increment, the second heading angle increment, and the vehicle posture at the previous moment, wherein the time difference between the previous moment and the current moment is one unit period.

[0010] Optionally, before obtaining the first heading angle increment based on the unit period travel distance of the left rear wheel, the unit period travel distance of the right rear wheel, and the rear wheelbase, the method further includes:

[0011] Obtaining a unit cycle travel distance of the left rear wheel based on a unit pulse coefficient of the left rear wheel and a unit cycle wheel speed pulse increment of the left rear wheel;

[0012] The unit cycle travel distance of the right rear wheel is acquired based on the unit pulse coefficient of the right rear wheel and the unit cycle wheel speed pulse increment of the right rear wheel.

[0013] Optionally, acquiring a second heading angle increment based on an inertial measurement unit (IMU) includes:

[0014] estimating a zero drift value of the vehicle's Z-axis angular velocity based on the inertial measurement unit;

[0015] Dynamically update the zero drift value of the Z-axis angular velocity according to preset rules;

[0016] The second heading angle increment is obtained by integrating the median of the Z-axis angular velocity.

[0017] Optionally, predicting the target vehicle posture at the current moment based on the first heading angle increment, the second heading angle increment, and the vehicle posture at the previous moment includes:

[0018] Predicting an initial vehicle posture at this moment based on the first heading angle increment and the vehicle posture at a previous moment;

[0019] Obtaining a posture correction value based on the second heading angle increment and the initial vehicle posture at the current moment;

[0020] The target vehicle posture at the moment is obtained based on the initial vehicle posture at the moment and the posture correction amount.

[0021] Optionally, obtaining a posture correction value based on the second heading angle increment and the initial vehicle posture at the current moment includes:

[0022] Obtaining a Kalman gain based on the initial vehicle pose at the current moment, the observation matrix, and the measurement noise;

[0023] Obtaining a third heading angle increment at this moment based on the initial vehicle posture at this moment, the observation matrix, and the heading angle of the vehicle at a previous moment;

[0024] Obtaining an observation residual based on the second heading angle increment and the third heading angle increment;

[0025] The posture correction value is obtained based on the Kalman gain and the observation residual.

[0026] Optionally, acquiring an observation residual based on the second heading angle increment and the third heading angle increment includes:

[0027] Based on erro=Δθ2(k)-h(k), the observation residual is obtained;

[0028] Wherein, erro represents the observation residual, Δθ2(k) represents the second heading angle increment at this moment, and h(k) represents the third heading angle increment at this moment.

[0029] Optionally, obtaining the pose correction value based on the Kalman gain and the observation residual includes:

[0030] Based on Δ=K*erro, the posture correction amount is obtained;

[0031] Wherein, Δ represents the posture correction amount, K represents the Kalman gain, and erro represents the observation residual.

[0032] Optionally, obtaining the Kalman gain based on the initial vehicle pose, observation matrix, and measurement noise at the current moment includes:

[0033] Based on the initial vehicle posture at this moment, obtaining a state transfer matrix of the initial vehicle posture at this moment;

[0034] Obtaining a first prediction covariance matrix based on the state transfer matrix of the initial vehicle posture at the current moment, the posterior covariance matrix at the previous moment, and the process noise covariance matrix;

[0035] The Kalman gain is obtained based on the first prediction covariance matrix, the observation matrix and the measurement noise.

[0036] Optionally, the acquiring the Kalman gain based on the first prediction covariance matrix, the observation matrix, and the measurement noise includes:

[0037] Based on K=P k *H T *(H*P k *H T ) -1 +R, to obtain the Kalman gain;

[0038] Wherein, K represents the Kalman gain, H represents the measurement matrix, which is [0 0 1], P k represents the first prediction covariance matrix at this moment, and R represents the measurement noise.

[0039] Optionally, obtaining the target vehicle posture at the current moment based on the initial vehicle posture and the posture correction amount includes:

[0040] Based on state(k) ′ =state(k)+Δ, to obtain the target vehicle posture at this moment;

[0041] Wherein, state(k)′ represents the target vehicle posture at this moment, state(k) represents the initial vehicle posture at this moment, and Δ represents the posture correction amount.

[0042] Optionally, also include:

[0043] A posterior covariance matrix is obtained based on the Kalman gain, the first prediction covariance matrix and the observation matrix.

[0044] Optionally, before obtaining the unit cycle travel distance of the left rear wheel based on the unit pulse coefficient of the left rear wheel and the unit cycle wheel speed pulse increment of the left rear wheel, the method further includes:

[0045] obtaining a unit pulse coefficient of the left rear wheel based on the travel distance of the left rear wheel and the pulse change value of the left rear wheel;

[0046] Before obtaining the unit cycle travel distance of the right rear wheel based on the unit pulse coefficient of the right rear wheel and the unit cycle wheel speed pulse increment of the right rear wheel, the method further includes:

[0047] A unit pulse coefficient of the right rear wheel is obtained based on the travel distance of the right rear wheel and the pulse change value of the right rear wheel.

[0048] A second aspect of an embodiment of the present application provides a vehicle positioning device, the device comprising:

[0049] a calculation module, configured to obtain a first heading angle increment based on a unit cycle travel distance of the left rear wheel, a unit cycle travel distance of the right rear wheel, and a rear wheelbase;

[0050] A measurement module, configured to obtain a second heading angle increment based on an inertial measurement unit;

[0051] A prediction module is used to predict the target vehicle posture at a current moment based on the first heading angle increment, the second heading angle increment, and the vehicle posture at a previous moment, wherein the time difference between the previous moment and the current moment is one unit period.

[0052] Optionally, the calculation module is also used to obtain the unit cycle driving distance of the left rear wheel based on the unit pulse coefficient of the left rear wheel and the unit cycle wheel speed pulse increment of the left rear wheel; and to obtain the unit cycle driving distance of the right rear wheel based on the unit pulse coefficient of the right rear wheel and the unit cycle wheel speed pulse increment of the right rear wheel.

[0053] Optionally, the measurement module is specifically used to estimate the zero drift value of the vehicle's Z-axis angular velocity based on the inertial measurement unit; dynamically update the zero drift value of the Z-axis angular velocity according to preset rules; and obtain the second heading angle increment by integrating the median of the Z-axis angular velocity.

[0054] Optionally, the prediction module is specifically used to predict the initial vehicle posture at the current moment based on the first heading angle increment and the vehicle posture at the previous moment; obtain the posture correction amount based on the second heading angle increment and the initial vehicle posture at the current moment; and obtain the target vehicle posture at the current moment based on the initial vehicle posture and the posture correction amount.

[0055] Optionally, the prediction module is specifically used to obtain the Kalman gain based on the initial vehicle posture, observation matrix and measurement noise at the current moment; obtain the third heading angle increment at the current moment based on the initial vehicle posture, observation matrix and the heading angle of the vehicle at the previous moment; obtain the observation residual based on the second heading angle increment and the third heading angle increment; and obtain the posture correction amount based on the Kalman gain and the observation residual.

[0056] Optionally, the prediction module is specifically used to obtain the observation residual based on erro=Δθ2(k)-h(k); wherein erro represents the observation residual, Δθ2(k) represents the second heading angle increment at this moment, and h(k) represents the third heading angle increment at this moment.

[0057] Optionally, the prediction module is specifically used to obtain the posture correction based on Δ=K*erro; wherein Δ represents the posture correction, K represents the Kalman gain, and erro represents the observation residual.

[0058] Optionally, the prediction module is specifically used to obtain the state transfer matrix of the initial vehicle posture at this moment based on the initial vehicle posture at this moment; obtain the first prediction covariance matrix based on the state transfer matrix of the initial vehicle posture at this moment, the posterior covariance matrix at the previous moment and the process noise covariance matrix; obtain the Kalman gain based on the first prediction covariance matrix, the observation matrix and the measurement noise.

[0059] Optionally, the prediction module is specifically configured to: k *H T *(H*P k *H T ) -1 +R, to obtain the Kalman gain; wherein K represents the Kalman gain, H represents the observation matrix, which is [0 0 1], P k represents the first prediction covariance matrix at this moment, and R represents the measurement noise.

[0060] Optionally, the prediction module is specifically used to obtain the target vehicle posture at the current moment based on state(k)′=state(k)+Δ; wherein state(k)′ represents the target vehicle posture at the current moment, state(k) represents the initial vehicle posture at the current moment, and Δ represents the posture correction amount.

[0061] Optionally, the prediction module is further used to obtain a posterior covariance matrix based on the Kalman gain, the first prediction covariance matrix and the observation matrix.

[0062] Optionally, the calculation module is also used to obtain the unit pulse coefficient of the left rear wheel based on the driving distance of the left rear wheel and the pulse change value of the left rear wheel; and to obtain the unit pulse coefficient of the right rear wheel based on the driving distance of the right rear wheel and the pulse change value of the right rear wheel.

[0063] A third aspect of an embodiment of the present application provides an electronic device, comprising: a processor, the processor being used to connect to a memory, the memory storing programs or instructions that can be run on the processor, and the programs or instructions, when executed by the processor, implementing the steps of the vehicle positioning method described in the first aspect above.

[0064] A fourth aspect of an embodiment of the present application provides a vehicle, comprising the electronic device as described in the third aspect above.

[0065] A fifth aspect of an embodiment of the present application provides a computer-readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the vehicle positioning method described in the first aspect are implemented.

[0066] A sixth aspect of an embodiment of the present application provides a computer program product, which, when executed by a processor of a cloud server, implements the steps of the vehicle positioning method described in the first aspect above.

[0067] The vehicle positioning method, electronic device, vehicle, medium, and computer program product provided in the embodiments of the present application obtain a first heading angle increment based on the unit cycle travel distance of the left rear wheel, the unit cycle travel distance of the right rear wheel, and the rear wheelbase; obtain a second heading angle increment based on an inertial measurement unit; and predict the target vehicle posture at the current moment based on the first heading angle increment, the second heading angle increment, and the vehicle posture at the previous moment, wherein the time difference between the previous moment and the current moment is one unit cycle. In other words, fewer parameters are used to calculate the first heading angle increment of the vehicle, and the real-time posture of the vehicle is inferred from the first heading angle increment and the second heading angle increment to achieve vehicle positioning, thereby reducing the complexity of the calculation and improving the accuracy of vehicle positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A schematic diagram of a vehicle positioning method according to an embodiment of the present invention;

[0069] Figure 2 A schematic diagram of a scenario for calculating a heading angle increment based on a rear wheel differential model provided in an embodiment of the present application;

[0070] Figure 3 A flow chart of another vehicle positioning method provided in an embodiment of the present application;

[0071] Figure 4 A flow chart of another vehicle positioning method provided in an embodiment of the present application;

[0072] Figure 5 A flow chart of another vehicle positioning method provided in an embodiment of the present application;

[0073] Figure 6 A flow chart of another vehicle positioning method provided in an embodiment of the present application;

[0074] Figure 7 A flow chart of another vehicle positioning method provided in an embodiment of the present application;

[0075] Figure 8 A flow chart of another vehicle positioning method provided in an embodiment of the present application;

[0076] Figure 9 A flow chart of another vehicle positioning method provided in an embodiment of the present application;

[0077] Figure 10 A schematic structural diagram of a vehicle positioning device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0078] The following will be combined with the accompanying drawings in the embodiments of this application to clearly describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0079] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same type, and do not limit the number of objects, for example, the first object can be one or more. In addition, "or" in this application represents at least one of the connected objects. For example, "A or B" covers three options, namely, Option 1: including A but not including B; Option 2: including B but not including A; Option 3: including both A and B. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0080] The term "indication" in this application can be either a direct indication (or explicit indication) or an indirect indication (or implicit indication). A direct indication can be understood as the sender explicitly informing the receiver of specific information, the operation to be performed, or the requested result, etc. in the instruction sent; an indirect indication can be understood as the receiver determining the corresponding information based on the instruction sent by the sender, or making a judgment and determining the operation to be performed or the requested result, etc. based on the judgment result.

[0081] In the prior art, the front wheel angle is obtained by querying the proportional relationship table between the steering wheel angle and the front wheel angle, the front wheel angle is multiplied by the steering direction to obtain the output angle, and then the rear axle center turning radius is calculated by the output angle and the vehicle wheelbase, and then the turning radius of the four wheels of the vehicle is calculated according to the rear wheelbase and the wheelbase, and then the heading angle increment of each wheel is obtained based on the turning path increment and turning radius of each wheel, and finally the heading angle increment of the four wheels is averaged to obtain the heading angle increment of the vehicle, and the posture at the next moment is updated based on the heading angle increment, the rear axle center turning radius and the posture of the vehicle at the current moment to realize automatic parking of the vehicle. However, the above method has too many calibration parameter requirements and too many intermediate variable conversions, resulting in high computational complexity. The present application provides a vehicle positioning method based on Kalman filter fusion, which calculates the first heading angle increment by calibrating the unit pulse coefficient of the wheel and predicts the vehicle posture, thereby reducing the dependence on calibration parameters and thus reducing computational complexity; and then through the inertial measurement unit (Inertial The second heading angle increment is measured by the Intrusion Measurement Unit (IMU) and the vehicle posture is calibrated, thereby improving the accuracy of vehicle positioning.

[0082] The embodiment of the present application corrects the real-time posture of the vehicle by fusing the two vehicle heading angle increments obtained by the two methods using the Kalman filter algorithm. For the convenience of description, in the following embodiments of the present application, the vehicle heading angle increment obtained by calibrating the parameters of the wheel speed odometer and calculating is described as the first heading angle increment; the vehicle heading angle increment obtained by measuring the gyroscope of the IMU is described as the second heading angle increment; the vehicle heading angle increment obtained by predicting the observation equation of the vehicle heading angle increment in the Kalman filter algorithm is described as the third heading angle increment; the vehicle posture at this moment obtained based on the first heading angle increment and the vehicle posture at the previous moment is described as the initial vehicle posture; the vehicle posture obtained based on the initial vehicle posture and the posture correction amount is described as the target vehicle posture; the predicted covariance matrix obtained based on the initial vehicle posture at this moment is described as the first predicted covariance matrix; the predicted covariance matrix at the next moment updated after obtaining the target vehicle posture at this moment is described as the second predicted covariance matrix.

[0083] Among them, Kalman filtering is a filtering algorithm based on state estimation. The main idea is to model the dynamic characteristics and measurement noise of the system, combine the prior information of the prediction model and real-time observation data, and infer the true state value in a noisy environment. Its core lies in two key steps: prediction and update. It can be applied to autonomous driving of vehicles, combined with sensor data, to update the precise positioning of the vehicle in real time.

[0084] A wheel speed odometer is a sensor system used to measure vehicle distance and speed. It calculates parameters by monitoring wheel rotation. Specifically, a wheel speed sensor installed on each wheel of the vehicle can detect wheel rotation speed by generating a wheel pulse signal. There are two main types of wheel speed sensors:

[0085] Magnetoelectric wheel speed sensors: Based on the principle of electromagnetic induction, they consist of a magnetic induction sensor head and a ring gear. The sensor head includes a permanent magnet, a pole shaft, and an induction coil. As the wheel rotates, the teeth and gaps in the ring gear rapidly pass through the sensor's magnetic field, changing the magnetic resistance of the magnetic circuit. This induces a change in electric potential in the coil, generating electric potential pulses of a certain amplitude and frequency. The frequency of these pulses, or the number of pulses generated per second, reflects the wheel's rotational speed.

[0086] Hall-effect wheel speed sensors: Based on the Hall effect principle, they consist of a sensor head and a ring gear. The sensor head includes a permanent magnet, a Hall element, and electronic circuitry. As the gears rotate, they alternating magnetic resistance, causing changes in magnetic induction intensity, which in turn generates Hall potential pulses. These pulses are converted by the electronic circuitry into a standard pulse voltage whose frequency reflects the wheel's rotational speed.

[0087] An IMU is a device that measures the three-axis attitude angle and acceleration of an object. It usually includes but is not limited to the following sensors:

[0088] Accelerometer: Used to measure linear acceleration, that is, acceleration along three orthogonal axes (X-axis, Y-axis and Z-axis), and can identify the acceleration or deceleration state of the vehicle.

[0089] Gyroscope: Used to measure angular velocity, that is, the rotation rate around three orthogonal axes (Yaw, Pitch, Roll), to determine the direction and rotation state of the vehicle.

[0090] Magnetometer: Used to assist in calculating heading angle, that is, measuring the direction or angle of orientation relative to the Earth's magnetic field, helping to provide more accurate vehicle direction data.

[0091] The following describes the technical solution of the mobile target detection method of the present application using several specific embodiments as examples:

[0092] Figure 1 A flow chart of a vehicle positioning method provided in an embodiment of the present application is shown as follows: Figure 1 As shown, the method of the embodiment of the present application is as follows:

[0093] S11: Acquire a first heading angle increment based on the unit period travel distance of the left rear wheel, the unit period travel distance of the right rear wheel, and the rear wheelbase.

[0094] Among them, the heading angle increment refers to the change in the vehicle's heading angle within a unit period, and is an indispensable parameter for calculating the vehicle's posture; the acquisition of the vehicle's first heading angle increment is based on the rear wheel differential model, which focuses on describing how the vehicle's rear wheels distribute power to the left and right wheels through the differential. Since the outer wheels travel a greater distance when the vehicle turns, the differential allows the left and right wheels to rotate at different speeds, and the vehicle's heading angle change can be inferred by monitoring the speed difference between the left and right rear wheels; the rear wheelbase refers to the distance between the center planes of the two rear wheels on the same axle of the vehicle, that is, the distance from the center point of the left rear wheel to the center point of the right rear wheel of the vehicle. Figure 2 A schematic diagram of a scenario for calculating a heading angle increment based on a rear wheel differential model provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the vehicle turns right and the unit cycle distance of the left rear wheel is ΔL RL , the unit cycle distance of the right rear wheel of the vehicle is ΔL RR , the rear wheel span of the vehicle is RearWheelSpan.

[0095] It is understandable that when a vehicle turns, the outer wheel travels a longer distance than the inner wheel, and the quotient of the difference in travel distance between the two wheels and the rear wheelbase of the vehicle can reflect the change in the vehicle's heading angle. Figure 2 As shown, the right rear wheel is the inner wheel and the left rear wheel is the outer wheel. The unit cycle distance of the left rear wheel is subtracted from the unit cycle distance of the right rear wheel to obtain a difference, and then the difference is divided by the rear wheelbase to obtain the first heading angle increment of the vehicle. The formula can be expressed as:

[0096] Δθ1=(ΔL RL -ΔL RR ) / RearWheelSpan

[0097] Wherein, Δθ1 represents the first heading angle increment, ΔL RL represents the unit cycle distance of the left rear wheel, ΔL RR Represents the unit cycle distance of the right rear wheel of the vehicle, and RearWheelSpan represents the rear wheelbase.

[0098] S12: Acquire a second heading angle increment based on the inertial measurement unit.

[0099] The IMU is a sensor combination capable of measuring the motion state of an object, including the gyroscope. The gyroscope is used to measure the vehicle's angular velocity around a specific axis. By integrating this velocity, the change in the vehicle's angle around that axis can be inferred. Therefore, by measuring the vehicle's angular velocity around the Z axis at two moments using the gyroscope and calculating the median integral, the change in the vehicle's heading angle per unit period, i.e., the second heading angle increment, can be obtained.

[0100] S13: Predicting a target vehicle posture at a current moment based on the first heading angle increment, the second heading angle increment, and the vehicle posture at a previous moment, wherein the time difference between the previous moment and the current moment is one unit period.

[0101] Among them, the Kalman filter algorithm is used, combined with the first heading angle increment, the second heading angle increment and the vehicle posture at the last moment, to update the vehicle's more accurate posture. It can be understood that for the estimation of the vehicle posture state, the posture state vector in the Kalman filter algorithm is composed of three state variables: the vehicle's horizontal coordinate, vertical coordinate and heading angle. A prediction model can be established based on the first heading angle increment and the vehicle posture at the last moment to predict the initial vehicle posture at this moment, and the unit period difference between the last moment and the current moment is 20 milliseconds (ms); the second heading angle increment is used as the observation value of the real-time measurement, and the vehicle's heading angle increment is corrected based on the prediction value and observation value of the prediction model, and then the initial vehicle posture at this moment is corrected, and the updated result of the vehicle posture state that is close to the real state is output, that is, the target vehicle posture at this moment.

[0102] In an embodiment of the present application, a first heading angle increment is obtained based on the unit cycle distance traveled by the left rear wheel, the unit cycle distance traveled by the right rear wheel, and the rear wheelbase; a second heading angle increment is obtained based on an inertial measurement unit; and a target vehicle posture at the current moment is predicted based on the first heading angle increment, the second heading angle increment, and the vehicle posture at the previous moment, where the time difference between the previous moment and the current moment is one unit cycle. In other words, the first heading angle increment of the vehicle is calculated using fewer parameters, and the real-time posture of the vehicle is inferred from the first and second heading angle increments to achieve vehicle positioning, thereby reducing the complexity of the calculation and improving the accuracy of vehicle positioning.

[0103] Figure 3 A flow chart of another vehicle positioning method provided in an embodiment of the present application is provided. Figure 3 is Figure 1 On the basis of, before S11, it also includes S101 and S102, such as Figure 3 As shown, the method of the embodiment of the present application is as follows:

[0104] S101: Obtaining a unit cycle travel distance of the left rear wheel based on a unit pulse coefficient of the left rear wheel and a unit cycle wheel speed pulse increment of the left rear wheel.

[0105] Among them, the unit pulse coefficient refers to the wheel travel distance corresponding to each unit pulse, which is used to convert the pulse signal into the actual travel distance. The pulse signal is obtained by the wheel speed sensor; the unit cycle wheel speed pulse increment refers to the change in the number of pulses generated by the wheel speed sensor within a unit cycle.

[0106] It can be understood that the unit pulse coefficient and the unit cycle wheel speed pulse increment are multiplied to obtain the distance traveled by the wheel in a unit cycle. Therefore, the unit cycle distance traveled by the left rear wheel can be obtained based on the product of the unit pulse coefficient of the left rear wheel and the unit cycle wheel speed pulse increment of the left rear wheel. The formula can be expressed as:

[0107] ΔL RL =Num RL *CounterLength RL

[0108] Where, ΔL RL Indicates the unit cycle distance of the left rear wheel, Num RL Indicates the unit cycle wheel speed pulse increment of the left rear wheel, CounterLength RL Indicates the unit pulse coefficient of the left rear wheel.

[0109] S102: Obtaining the unit cycle travel distance of the right rear wheel based on the unit pulse coefficient of the right rear wheel and the unit cycle wheel speed pulse increment of the right rear wheel.

[0110] The unit cycle travel distance of the right rear wheel can be obtained based on the product of the unit pulse coefficient of the right rear wheel and the unit cycle wheel speed pulse increment of the right rear wheel. The formula can be expressed as follows:

[0111] ΔL RR =Num RR *CounterLength RR

[0112] Where, ΔL RR Indicates the unit cycle distance of the right rear wheel, Num RR Indicates the unit cycle wheel speed pulse increment of the right rear wheel, CounterLength RR Indicates the unit pulse coefficient of the right rear wheel.

[0113] In this embodiment, the unit cycle travel distance of the left rear wheel is obtained based on the unit pulse coefficient of the left rear wheel and the unit cycle wheel speed pulse increment of the left rear wheel; the unit cycle travel distance of the right rear wheel is obtained based on the unit pulse coefficient of the right rear wheel and the unit cycle wheel speed pulse increment of the right rear wheel. Thus, the unit cycle travel distance of the wheel is calculated through the calibration parameters of the vehicle, which helps to calculate the first heading angle increment of the vehicle.

[0114] Figure 4 A flow chart of another vehicle positioning method provided in an embodiment of the present application is shown below. Figure 4 is Figure 1Based on this, a possible implementation of S12 is described, such as Figure 4 As shown, the method of the embodiment of the present application is as follows:

[0115] S121: Estimate a zero drift value of the vehicle's Z-axis angular velocity based on the inertial measurement unit.

[0116] Among them, the IMU's gyroscope may also have non-zero output when in a stationary state, that is, it will produce a zero drift value of the vehicle's Z-axis angular velocity, which will affect its measurement accuracy. Therefore, it is necessary to calculate the zero drift value of the vehicle's Z-axis angular velocity.

[0117] For example, when the vehicle is stationary, the Z-axis angular velocity data points output by the gyroscope are collected over a period of time and averaged to obtain the zero-drift value. Thus, when the vehicle is moving, the zero-drift value can be subtracted from the gyroscope's measured value, making its output more accurate when the vehicle is not stationary, that is, closer to the true value.

[0118] S122: Dynamically update the zero drift value of the Z-axis angular velocity according to a preset rule.

[0119] Among them, because the zero drift value of the gyroscope may change due to external factors such as temperature changes, vibration and other external factors during long-term operation, it is necessary to re-evaluate and adjust the zero drift value of the Z-axis angular velocity of the gyroscope in real time or regularly. By dynamically updating the zero drift value, the error accumulated over a long period of time can be reduced, further improving the measurement accuracy of the gyroscope. The methods for implementing dynamic updates according to preset rules may include but are not limited to the following two:

[0120] Automatic calibration under static conditions: When the vehicle is stationary, the zero drift value is recalculated and updated at preset intervals.

[0121] Machine learning algorithm: Use machine learning models to predict the changing trend of zero drift and make adjustments in advance accordingly.

[0122] S123: Obtain the second heading angle increment by integrating the median of the Z-axis angular velocity.

[0123] The vehicle heading angle increment measured by the gyroscope can be obtained by performing median integration on the difference between the vehicle's current Z-axis angular velocity value and the vehicle's previous Z-axis angular velocity value. The second heading angle increment at this moment can be obtained by subtracting the zero drift value at this moment from the vehicle heading angle increment measured by the gyroscope. The formula can be expressed as:

[0124] Δθ2(k)=[Gyro_z(k-1)-Gyro_z(k)]*dt / 2-IMU_Gyro_z_Bias(k)

[0125] Wherein, Δθ2(k) represents the second heading angle increment, Gyro_z(k-1) represents the Z-axis angular velocity value of the vehicle at the previous moment, Gyro_z(k) represents the Z-axis angular velocity value of the vehicle at the current moment, and IMU_Gyro_z_Bias(k) represents the zero drift value at the current moment.

[0126] In this embodiment, when the vehicle is stationary, the zero-drift value of the vehicle's Z-axis angular velocity is estimated based on the IMU; the zero-drift value of the Z-axis angular velocity is dynamically updated according to a strategy; and the second heading angle increment is obtained by integrating the median of the Z-axis angular velocity. Thus, the second heading angle increment of the vehicle is obtained through gyroscope measurement, which helps to fuse and optimize the second heading angle increment with the first heading angle increment of the vehicle to obtain a more accurate heading angle increment.

[0127] Figure 5 A flow chart of another vehicle positioning method provided in an embodiment of the present application is provided. Figure 5 is Figure 1 Based on this, a possible implementation of S13 is described, such as Figure 5 As shown, the method of the embodiment of the present application is as follows:

[0128] S131: Predicting an initial vehicle posture at this moment based on the first heading angle increment and the vehicle posture at the previous moment.

[0129] Among them, the first heading angle increment of the vehicle and the unit cycle distance traveled by the vehicle are used as the control input of the Kalman filter algorithm. Combined with the horizontal coordinate, vertical coordinate and heading angle of the vehicle at the previous moment, the vehicle posture state prediction equation at this moment can be established; based on the vehicle posture state prediction equation at this moment, the vehicle posture at this moment can be predicted, that is, the initial vehicle posture is obtained. The acquisition of the unit cycle distance traveled by the vehicle is based on the vehicle kinematic model. The vehicle kinematic model means that it is assumed that the vehicle is a rigid body and all wheels travel in their direction without slipping. The rear wheels are more stable than the front wheels and are closer to straight-line travel. The overall displacement of the vehicle can be approximately represented by the average displacement of the rear wheels. Therefore, taking the average of the unit cycle distance traveled by the left rear wheel and the unit cycle distance traveled by the right rear wheel can avoid the interference caused by the front wheel steering, and the unit cycle distance traveled by the vehicle can be obtained. The formula can be expressed as:

[0130] ΔS=(ΔL RR +ΔL RL ) / 2

[0131] Wherein, ΔS represents the unit cycle distance of the vehicle, ΔL RR represents the unit cycle distance of the right rear wheel, Δl RL Indicates the unit cycle travel distance of the left rear wheel.

[0132] Furthermore, the vehicle posture state prediction equation at this moment includes the vehicle horizontal coordinate state prediction equation, the vehicle vertical coordinate state prediction equation and the vehicle heading angle state prediction equation at this moment, which can be expressed as:

[0133] θ(k)=θ(k-1)+Δθ1(k)

[0134] x(k)=x(k-1)+ΔS(k)cos[θ(k-1)+Δθ1(k) / 2]

[0135] y(k)=y(k-1)+ΔS(k)sin(θ(k-1)+Δθ1(k) / 2)

[0136] state(k)=(x(k),y(k),θ(k)) T

[0137] Among them, θ(k) represents the vehicle heading angle at the current moment, x(k) represents the vehicle horizontal coordinate at the current moment, y(k) represents the vehicle vertical coordinate at the current moment, state(k) represents the initial vehicle posture at the current moment, θ(k-1) represents the vehicle heading angle at the previous moment, x(k-1) represents the vehicle horizontal coordinate at the previous moment, y(k-1) represents the vehicle vertical coordinate at the previous moment, Δθ1(k) represents the first heading angle increment at the current moment, and ΔS(k) represents the unit cycle driving distance at the current moment.

[0138] S132: Obtaining a posture correction value based on the second heading angle increment and the initial vehicle posture at the current moment.

[0139] Among them, since the second heading angle increment is an observed value and the first heading angle increment in the initial vehicle posture at this moment is a predicted value, the weights of the observed value and the predicted value can be weighed and expressed as a Kalman gain; based on the initial vehicle posture at this moment, the observed value of the heading angle increment can be predicted, and then based on the obtained predicted observation value and observation value, its deviation can be obtained, expressed as an observation residual; based on the Kalman gain and observation residual, the posture correction amount can be obtained, that is, the degree of correction of the observed value to the predicted value can be reflected, and then the initial vehicle posture at this moment can be corrected.

[0140] S133: Based on the initial vehicle posture at the current moment and the posture correction amount, obtain the target vehicle posture at the current moment.

[0141] Among them, by adding the initial vehicle posture at this moment and the posture correction amount, the vehicle posture state update equation at this moment can be established; based on the vehicle posture state update equation at this moment, the initial vehicle posture at this moment can be updated, that is, the target vehicle posture at this moment is obtained, and the vehicle posture state update equation at this moment can be expressed as:

[0142] state(k) ′ =state(k)+Δ

[0143] Wherein, state(k)′ represents the target vehicle posture at this moment, state(k) represents the initial vehicle posture at this moment, and Δ represents the posture correction amount.

[0144] In this embodiment, the initial vehicle posture at the current moment is predicted based on the first heading angle increment and the vehicle posture at the previous moment; a posture correction value is obtained based on the second heading angle increment and the initial vehicle posture at the current moment; and the target vehicle posture at the current moment is obtained based on the initial vehicle posture at the current moment and the posture correction value, thereby realizing the Kalman filter algorithm's prediction and update process for the vehicle posture, and improving the accuracy of vehicle positioning.

[0145] Figure 6 A flow chart of another vehicle positioning method provided in an embodiment of the present application is provided. Figure 6 is Figure 5 Based on this, a possible implementation of S132 is described, such as Figure 6 As shown, the method of the embodiment of the present application is as follows:

[0146] S1321: Obtaining a Kalman gain based on the initial vehicle pose, observation matrix, and measurement noise at this moment.

[0147] Among them, the Kalman gain is used to represent the weight between the predicted value and the observed value of the vehicle heading angle increment in the process of correcting the vehicle posture, wherein the predicted value is the first heading angle increment and the observed value is the second heading angle increment; the observation matrix refers to a linear change matrix that maps the vehicle posture state to the observation space, and describes how to predict the observed value of the vehicle posture through the initial vehicle posture at the current moment. If only the vehicle's heading angle is observed, the observation matrix can be expressed as H=[0 0 1], and the observed value of the heading angle increment can be predicted through the initial vehicle posture at the current moment; based on the vehicle posture state prediction equation at the current moment, the uncertainty of the initial vehicle posture at the current moment can be estimated and represented by a prediction covariance matrix; and in the process of observing the second heading angle increment of the vehicle through the gyroscope, measurement noise will be generated; then based on the prediction covariance matrix at the current moment, the observation matrix and the measurement noise, the Kalman gain can be obtained.

[0148] S1322: Based on the initial vehicle posture at this moment, the observation matrix and the heading angle of the vehicle at the previous moment, obtain a third heading angle increment at this moment.

[0149] Based on the initial vehicle posture at this moment, the observation matrix, and the vehicle's heading angle at the previous moment, an observation equation for the vehicle heading angle increment can be established to predict the observed value of the heading angle increment based on the initial vehicle posture at this moment, i.e., the third heading angle increment. Based on the product of the observation matrix and the initial vehicle posture at this moment, the predicted observed value of the vehicle heading angle at this moment can be obtained; based on the difference between the predicted observed value of the vehicle heading angle at this moment and the heading angle of the vehicle at the previous moment, the third heading angle increment at this moment can be obtained; the observation equation for the vehicle heading angle increment can be expressed as:

[0150] h[state(k)]=H*state(k)-θ(k-1)

[0151] Wherein, h[state(k)] represents the third heading angle increment at the current moment, H represents the observation matrix, which is [0 0 1], θ(k-1) represents the vehicle heading angle at the previous moment, and state(k) represents the initial vehicle posture at the current moment.

[0152] S1323: Obtain an observation residual based on the second heading angle increment and the third heading angle increment.

[0153] The observation residual is the difference between the observed value and the predicted observed value of the vehicle heading angle increment, where the observed value is the second heading angle increment and the predicted observed value is the third heading angle increment. Based on the difference between the second heading angle increment at this moment and the third heading angle increment at this moment, the observation residual can be obtained, which is expressed as:

[0154] erro=Δθ2(k)-g(k)

[0155] Wherein, erro represents the observation residual, Δθ2(k) represents the second heading angle increment at the current moment, and h(k) represents the third heading angle increment at the current moment.

[0156] S1324: Obtain the posture correction value based on the Kalman gain and the observation residual.

[0157] Among them, based on the product of the Kalman gain and the observation residual, the correction amount of the vehicle posture state can be obtained, which is expressed as:

[0158] Δ=K*erro

[0159] Wherein, Δ represents the posture correction amount, K represents the Kalman gain, and erro represents the observation residual.

[0160] Furthermore, the vehicle posture state update equation at this moment can be expressed as:

[0161] state(k)′=state(k)+K*erro

[0162] In this embodiment, a Kalman gain is obtained based on the initial vehicle posture at the current moment, the observation matrix, and the measurement noise; a third heading angle increment at the current moment is obtained based on the initial vehicle posture at the current moment, the observation matrix, and the heading angle of the vehicle at the previous moment; an observation residual is obtained based on the second heading angle increment and the third heading angle increment; and the posture correction amount is obtained based on the Kalman gain and the observation residual. Thus, a correction amount for the predicted value of the vehicle posture at the current moment is obtained, which helps to obtain a more accurate vehicle posture at the current moment.

[0163] Figure 7 A flow chart of another vehicle positioning method provided in an embodiment of the present application is provided. Figure 7 is Figure 6 Based on this, a possible implementation of S1321 is described, such as Figure 7 As shown, the method of the embodiment of the present application is as follows:

[0164] S13211: Based on the initial vehicle posture at this moment, obtain a state transfer matrix of the initial vehicle posture at this moment.

[0165] Among them, the state transfer matrix of the initial vehicle posture at this moment is the Jacobian matrix of the vehicle posture state prediction equation at this moment with respect to the vehicle posture state vector at this moment, that is, it is used to describe the partial derivatives of the vehicle posture state prediction equation at this moment with respect to the vehicle posture state vector at this moment, and is used to linearize the nonlinear vehicle posture state prediction equation at this moment. Therefore, the partial derivatives of the vehicle horizontal coordinate state prediction equation, the vehicle vertical coordinate state prediction equation, and the vehicle heading angle increment state prediction equation at this moment are respectively taken with respect to x(k), y(k), and θ(k), to obtain the state transfer matrix of the initial vehicle posture at this moment, which is expressed as:

[0166]

[0167] S13212: Obtain a first prediction covariance matrix based on the state transfer matrix, the posterior covariance matrix, and the process noise covariance matrix of the initial vehicle posture at the current moment.

[0168] Among them, the first prediction covariance matrix is used to represent the uncertainty in the vehicle posture state prediction process at the current moment, and its initial value is a unit matrix; after updating the initial vehicle posture at the current moment to obtain the target vehicle posture at the current moment, the first prediction covariance matrix will also be updated, and the updated value at the previous moment can be used as the posterior covariance matrix for calculating the first prediction covariance matrix at the current moment; the process noise covariance matrix represents the influence of noise in the prediction process on the state prediction, which is the product of a constant coefficient and a unit matrix, and the constant coefficient is a calibration value that is continuously corrected and adjusted according to the process noise during the prediction process; based on the state transfer matrix of the initial vehicle posture at the current moment, the posterior covariance matrix at the previous moment, and the process noise covariance matrix, the first prediction covariance matrix at the current moment can be obtained, which is expressed as:

[0169] P k =F*P k-1 *F T +Q

[0170] Among them, P k represents the first prediction covariance matrix at this moment, P k-1 represents the posterior covariance matrix of the previous moment, F represents the state transfer matrix of the initial vehicle posture at the current moment, and F T represents the transposed matrix of F, and Q represents the process noise covariance matrix.

[0171] S13213: Obtain the Kalman gain based on the prediction covariance matrix, the observation matrix and the measurement noise.

[0172] The measurement noise refers to the noise generated during the gyroscope's measurement and calculation of the second heading angle increment, representing the uncertainty of the measurement device during the observation process, and is a one-dimensional scalar. Based on the first prediction covariance matrix at this moment, the observation matrix, and the measurement noise, the Kalman gain can be obtained, which is expressed as:

[0173] K=P k *H T *(H*P k *H T ) -1 +R

[0174] Wherein, K represents the Kalman gain, H represents the measurement matrix, which is [0 0 1], and H T represents the transposed matrix of H, P k represents the first prediction covariance matrix at the current moment, and R represents the measurement noise.

[0175] In this embodiment, the state transfer matrix of the initial vehicle posture at this moment is obtained based on the initial vehicle posture at this moment; the first prediction covariance matrix is obtained based on the state transfer matrix of the initial vehicle posture at this moment, the posterior covariance matrix at the previous moment, and the process noise covariance matrix; the Kalman gain is obtained based on the first prediction covariance matrix, the observation matrix, and the measurement noise, thereby obtaining the Kalman gain, which helps to correct the predicted value of the vehicle posture.

[0176] Figure 8 A flow chart of another vehicle positioning method provided in an embodiment of the present application is provided. Figure 8 is Figure 7 On the basis of S13214, Figure 8 As shown, the method of the embodiment of the present application is as follows:

[0177] S13214: Obtain a posterior covariance matrix based on the Kalman gain, the first prediction covariance matrix and the observation matrix.

[0178] Among them, the posterior covariance matrix is used to represent the uncertainty in the process of updating the vehicle posture state at the current moment. It can be the prediction covariance matrix at the current moment updated based on the first prediction covariance matrix at the current moment after updating the posture state of the vehicle at the current moment. At this time, the first prediction covariance matrix at the current moment is used as the prior covariance matrix for calculating the update value; therefore, based on the Kalman gain, the first prediction covariance matrix at the current moment and the observation matrix, the posterior covariance matrix at the current moment can be obtained, which is expressed as:

[0179] P ′ k =(IK*H)*P k

[0180] Among them, P′ k represents the posterior covariance matrix at this moment, I represents the identity matrix, K represents the Kalman gain, H represents the observation matrix, which is [0 0 1], P k Represents the first prediction covariance matrix at the current moment.

[0181] In this embodiment, a posterior covariance matrix is obtained based on the Kalman gain, the first prediction covariance matrix and the observation matrix, thereby helping to improve the estimation accuracy of the corrected vehicle posture.

[0182] Figure 9 A flow chart of another vehicle positioning method provided in an embodiment of the present application is provided. Figure 9 is Figure 3 On the basis of, before S101, S1011 is also included, before S102, S1021 is also included, such as Figure 9 As shown, the method of the embodiment of the present application is as follows:

[0183] S1011: Based on the travel distance of the left rear wheel and the pulse change value of the left rear wheel, obtain the unit pulse coefficient of the left rear wheel.

[0184] Among them, a wheel speed sensor is installed on each wheel of the vehicle, which allows the vehicle to travel a preset distance in a straight line. The preset distance is not less than 40 meters. The method of measuring the straight-line travel distance of each wheel of the vehicle may include but is not limited to a ruler, a speed sensor and real-time dynamic differential positioning (RTK) technology.

[0185] Furthermore, the number of pulses of the left rear wheel of the vehicle at the origin and at the end point is read respectively to calculate the pulse change value of the left rear wheel. Based on the quotient of the driving distance of the left rear wheel and the pulse change value of the left rear wheel of the vehicle, the unit pulse coefficient of the left rear wheel can be obtained.

[0186] S1021: Based on the travel distance of the right rear wheel and the pulse change value of the right rear wheel, obtain the unit pulse coefficient of the right rear wheel.

[0187] Among them, the number of pulses of the right rear wheel of the vehicle at the origin and at the end point is read respectively to calculate the pulse change value of the right rear wheel. Based on the quotient of the driving distance of the right rear wheel and the pulse change value of the right rear wheel, the unit pulse coefficient of the right rear wheel of the vehicle can be obtained.

[0188] In this embodiment, the unit pulse coefficient of the left rear wheel is obtained based on the travel distance of the left rear wheel and the pulse change value of the left rear wheel; the unit pulse coefficient of the right rear wheel is obtained based on the travel distance of the right rear wheel and the pulse change value of the right rear wheel, thereby calibrating the unit pulse coefficients of the vehicle wheels, which helps to subsequently calculate the unit cycle travel distance of the vehicle and the first heading angle increment.

[0189] In the above embodiment, since the wheel speed sensors installed on each wheel of the vehicle are of the same model and specification, the number of pulses generated by each wheel is the same. When the vehicle travels a certain distance in a straight line, the unit pulse coefficient of the right rear wheel and the unit pulse coefficient of the left rear wheel should theoretically be the same. However, due to the influence of some factors, some differences may occur, so the unit pulse coefficient of each wheel needs to be calculated independently. Among them, these factors may include but are not limited to the following:

[0190] Tire size variations: Inconsistent tire pressure affects the rolling circumference of each wheel, which in turn changes the distance the wheel travels per pulse.

[0191] Tire wear: As tires are used for a longer time, they will gradually wear out, causing their diameter to decrease, thereby changing the wheel travel distance corresponding to a unit pulse.

[0192] Different road conditions: Different road surfaces, such as mud, snow, or sand, produce different friction coefficients on the wheels, which will lead to different rolling resistance of the tires, thereby changing the wheel travel distance corresponding to a unit pulse.

[0193] Figure 10 This is a schematic diagram of the structure of a vehicle positioning device provided in an embodiment of the present application. The device includes a calculation module 1001, a measurement module 1002 and a prediction module 1003, wherein:

[0194] A calculation module 1001 is configured to obtain a first heading angle increment based on a unit cycle travel distance of the left rear wheel, a unit cycle travel distance of the right rear wheel, and a rear wheelbase;

[0195] The measuring module 1002 is configured to obtain a second heading angle increment based on an inertial measurement unit;

[0196] The prediction module 1003 is configured to predict the target vehicle posture at a current moment based on the first heading angle increment, the second heading angle increment, and the vehicle posture at a previous moment, wherein the time difference between the previous moment and the current moment is one unit period.

[0197] Optionally, the calculation module 1001 is also used to obtain the unit cycle driving distance of the left rear wheel based on the unit pulse coefficient of the left rear wheel and the unit cycle wheel speed pulse increment of the left rear wheel; and to obtain the unit cycle driving distance of the right rear wheel based on the unit pulse coefficient of the right rear wheel and the unit cycle wheel speed pulse increment of the right rear wheel.

[0198] Optionally, the measurement module 1002 is specifically used to estimate the zero drift value of the vehicle's Z-axis angular velocity based on the inertial measurement unit; dynamically update the zero drift value of the Z-axis angular velocity according to preset rules; and obtain the second heading angle increment by integrating the median of the Z-axis angular velocity.

[0199] Optionally, the prediction module 1003 is specifically used to predict the initial vehicle posture at the current moment based on the first heading angle increment and the vehicle posture at the previous moment; obtain the posture correction amount based on the second heading angle increment and the initial vehicle posture at the current moment; and obtain the target vehicle posture at the current moment based on the initial vehicle posture at the current moment and the posture correction amount.

[0200] Optionally, the prediction module is specifically used to obtain the Kalman gain based on the initial vehicle posture, observation matrix and measurement noise at the current moment; obtain the third heading angle increment at the current moment based on the initial vehicle posture, observation matrix and the heading angle of the vehicle at the previous moment; obtain the observation residual based on the second heading angle increment and the third heading angle increment; and obtain the posture correction amount based on the Kalman gain and the observation residual.

[0201] Optionally, the prediction module 1003 is specifically used to obtain the observation residual based on erro=Δθ2(k)-h(k); wherein erro represents the observation residual, Δθ2(k) represents the second heading angle increment at this moment, and h(k) represents the third heading angle increment at this moment.

[0202] Optionally, the prediction module 1003 is specifically used to obtain the posture correction based on Δ=K*erro; wherein Δ represents the posture correction, K represents the Kalman gain, and erro represents the observation residual.

[0203] Optionally, the prediction module 1003 is specifically used to obtain the state transfer matrix of the initial vehicle posture at this moment based on the initial vehicle posture at this moment; obtain the first prediction covariance matrix based on the state transfer matrix of the initial vehicle posture at this moment, the posterior covariance matrix at the previous moment and the process noise covariance matrix; obtain the Kalman gain based on the first prediction covariance matrix, the observation matrix and the measurement noise.

[0204] Optionally, the prediction module 1003 is specifically configured to: k *H T *(H*P k *H T ) -1 +R, to obtain the Kalman gain; wherein K represents the Kalman gain, H represents the observation matrix, which is [0 0 1], P k represents the first prediction covariance matrix at this moment, and R represents the measurement noise.

[0205] Optionally, the prediction module 1003 is specifically used to obtain the target vehicle posture at the current moment based on state(k)′=state(k)+Δ; wherein state(k)′ represents the target vehicle posture at the current moment, state(k) represents the initial vehicle posture at the current moment, and Δ represents the posture correction amount.

[0206] Optionally, the prediction module 1003 is further configured to obtain a posterior covariance matrix based on the Kalman gain, the first prediction covariance matrix, and the observation matrix.

[0207] Optionally, the calculation module 1001 is also used to obtain the unit pulse coefficient of the left rear wheel based on the driving distance of the left rear wheel and the pulse change value of the left rear wheel; and to obtain the unit pulse coefficient of the right rear wheel based on the driving distance of the right rear wheel and the pulse change value of the right rear wheel.

[0208] The device of this embodiment can be used to execute the technical solutions of the above-mentioned method embodiments accordingly. Its implementation principles and technical effects are similar and will not be described in detail here.

[0209] An embodiment of the present application also provides an electronic device, comprising: a processor, the processor being connected to a memory, the memory storing programs or instructions that can be run on the processor, and the programs or instructions, when executed by the processor, implementing the steps of any of the above-mentioned vehicle positioning method embodiments.

[0210] An embodiment of the present application further provides a vehicle, comprising: a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of any of the above-mentioned vehicle positioning method embodiments are implemented.

[0211] An embodiment of the present application further provides a computer-readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of any of the above-mentioned vehicle positioning method embodiments are implemented.

[0212] An embodiment of the present application also provides a computer program product, which, when executed by a processor of a cloud server, implements the steps of any of the above-mentioned vehicle positioning method embodiments.

[0213] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0214] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of a computer software product plus a necessary general-purpose hardware platform, or of course, by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes a number of instructions for enabling a terminal or network-side device to execute the methods described in each embodiment of the present application.

[0215] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms of implementation methods without departing from the purpose of this application and the scope of protection of the claims. These implementation methods are all within the protection of this application.

Claims

1. A vehicle positioning method, characterized in that: include: Obtaining a first heading angle increment based on the unit cycle travel distance of the left rear wheel, the unit cycle travel distance of the right rear wheel, and the rear wheelbase; Acquire a second heading angle increment based on the inertial measurement unit; The target vehicle posture at the current moment is predicted based on the first heading angle increment, the second heading angle increment, and the vehicle posture at the previous moment, wherein the time difference between the previous moment and the current moment is one unit period.

2. The method according to claim 1, characterized in that Before obtaining the first heading angle increment based on the unit cycle travel distance of the left rear wheel, the unit cycle travel distance of the right rear wheel, and the rear wheelbase, the method further includes: Obtaining a unit cycle travel distance of the left rear wheel based on a unit pulse coefficient of the left rear wheel and a unit cycle wheel speed pulse increment of the left rear wheel; The unit cycle travel distance of the right rear wheel is acquired based on the unit pulse coefficient of the right rear wheel and the unit cycle wheel speed pulse increment of the right rear wheel.

3. The method according to claim 1, characterized in that The obtaining of a second heading angle increment based on an inertial measurement unit includes: estimating a zero drift value of the vehicle's Z-axis angular velocity based on the inertial measurement unit; Dynamically update the zero drift value of the Z-axis angular velocity according to preset rules; The second heading angle increment is obtained by integrating the median of the Z-axis angular velocity.

4. The method according to claim 1 or 2, characterized in that The predicting the target vehicle posture at the current moment based on the first heading angle increment, the second heading angle increment, and the vehicle posture at the previous moment includes: Predicting an initial vehicle posture at this moment based on the first heading angle increment and the vehicle posture at a previous moment; Obtaining a posture correction value based on the second heading angle increment and the initial vehicle posture at the current moment; The target vehicle posture at the moment is obtained based on the initial vehicle posture at the moment and the posture correction amount.

5. The method according to claim 4, characterized in that The obtaining of a posture correction value based on the second heading angle increment and the initial vehicle posture at the current moment includes: Obtaining a Kalman gain based on the initial vehicle pose at the current moment, the observation matrix, and the measurement noise; Obtaining a third heading angle increment at this moment based on the initial vehicle posture at this moment, the observation matrix, and the heading angle of the vehicle at a previous moment; Obtaining an observation residual based on the second heading angle increment and the third heading angle increment; The posture correction value is obtained based on the Kalman gain and the observation residual.

6. The method according to claim 5, characterized in that The obtaining of an observation residual based on the second heading angle increment and the third heading angle increment includes: Based on erro=Δθ2(k)-h(k), the observation residual is obtained; Wherein, erro represents the observation residual, Δθ2(k) represents the second heading angle increment at this moment, and h(k) represents the third heading angle increment at this moment.

7. The method according to claim 5, characterized in that The obtaining of the posture correction value based on the Kalman gain and the observation residual includes: Based on Δ=K*erro, the posture correction amount is obtained; Wherein, Δ represents the posture correction amount, K represents the Kalman gain, and erro represents the observation residual.

8. The method according to claim 5, characterized in that The obtaining of the Kalman gain based on the initial vehicle pose, the observation matrix, and the measurement noise at the current moment includes: Based on the initial vehicle posture at this moment, obtaining a state transfer matrix of the initial vehicle posture at this moment; Obtaining a first prediction covariance matrix based on the state transfer matrix of the initial vehicle posture at the current moment, the posterior covariance matrix at the previous moment, and the process noise covariance matrix; The Kalman gain is obtained based on the first prediction covariance matrix, the observation matrix and the measurement noise.

9. The method according to claim 8, characterized in that The obtaining the Kalman gain based on the first prediction covariance matrix, the observation matrix and the measurement noise includes: Based on K=P k *H T *(H*P k *H T ) -1 +R, to obtain the Kalman gain; Wherein, K represents the Kalman gain, H represents the measurement matrix, which is [0 0 1], P k represents the first prediction covariance matrix at this moment, and R represents the measurement noise.

10. The method according to claim 4, characterized in that The step of obtaining the target vehicle posture at the current moment based on the initial vehicle posture and the posture correction amount includes: Based on state(k)′=state(k)+Δ, the target vehicle posture at the current moment is obtained; Wherein, state(k)′ represents the target vehicle posture at this moment, state(k) represents the initial vehicle posture at this moment, and Δ represents the posture correction amount.

11. The method according to claim 8, characterized in that Also includes: A posterior covariance matrix is obtained based on the Kalman gain, the first prediction covariance matrix and the observation matrix.

12. The method according to claim 2, characterized in that Before obtaining the unit cycle travel distance of the left rear wheel based on the unit pulse coefficient of the left rear wheel and the unit cycle wheel speed pulse increment of the left rear wheel, the method further includes: obtaining a unit pulse coefficient of the left rear wheel based on the travel distance of the left rear wheel and the pulse change value of the left rear wheel; Before obtaining the unit cycle travel distance of the right rear wheel based on the unit pulse coefficient of the right rear wheel and the unit cycle wheel speed pulse increment of the right rear wheel, the method further includes: A unit pulse coefficient of the right rear wheel is obtained based on the travel distance of the right rear wheel and the pulse change value of the right rear wheel.

13. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the vehicle positioning method according to any one of claims 1 to 12 are implemented.

14. A vehicle, characterized in that: include: A processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the vehicle positioning method according to any one of claims 1 to 12 are implemented.

15. A computer-readable storage medium, characterized in that The readable storage medium stores a program or instruction, and when the program or instruction is executed by a processor, the steps of the vehicle positioning method according to any one of claims 1 to 12 are implemented.

16. A computer program product, which, when executed by a processor of a vehicle or a cloud server, implements the steps of the vehicle positioning method according to any one of claims 1 to 12.

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