WO2022179602A1 - 导航信息的处理方法、装置、电子设备及存储介质 - Google Patents

导航信息的处理方法、装置、电子设备及存储介质 Download PDF

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Publication number
WO2022179602A1
WO2022179602A1 PCT/CN2022/077896 CN2022077896W WO2022179602A1 WO 2022179602 A1 WO2022179602 A1 WO 2022179602A1 CN 2022077896 W CN2022077896 W CN 2022077896W WO 2022179602 A1 WO2022179602 A1 WO 2022179602A1
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Prior art keywords
navigation information
information
target
acceleration
navigation
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PCT/CN2022/077896
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English (en)
French (fr)
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王杰德
司徒春辉
韩雷晋
庞靖
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广州导远电子科技有限公司
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Priority to EP22758966.0A priority Critical patent/EP4300042A1/en
Publication of WO2022179602A1 publication Critical patent/WO2022179602A1/zh
Priority to US18/454,966 priority patent/US20230392938A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/02—Details
    • H04L12/12—Arrangements for remote connection or disconnection of substations or of equipment thereof
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/183—Compensation of inertial measurements, e.g. for temperature effects
    • G01C21/188—Compensation of inertial measurements, e.g. for temperature effects for accumulated errors, e.g. by coupling inertial systems with absolute positioning systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/277—Analysis of motion involving stochastic approaches, e.g. using Kalman filters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
    • H04L12/40—Bus networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
    • H04L12/40—Bus networks
    • H04L2012/40208—Bus networks characterized by the use of a particular bus standard
    • H04L2012/40215—Controller Area Network CAN
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
    • H04L12/40—Bus networks
    • H04L2012/40267—Bus for use in transportation systems
    • H04L2012/40273—Bus for use in transportation systems the transportation system being a vehicle

Definitions

  • the present application relates to the field of computer technology, and in particular, to a method, device, electronic device, and storage medium for processing navigation information.
  • Intelligent driving is an important starting point for the combination of industrial revolution and informatization. It will be able to change the flow of people, resource elements and products, and subversively change human life. Intelligent driving refers to the technology that machines help people to drive and completely replace human driving under special circumstances.
  • the purpose of the embodiments of the present application is to provide a method, device, electronic device and storage medium for processing navigation information, which are aimed at solving the problems of high navigation cost and unstable navigation effect in current vehicle navigation.
  • an embodiment of the present application provides a method for processing navigation information, including:
  • the strapdown solution is performed on the motion state information to obtain the navigation information
  • the navigation information and the vehicle information are fused to obtain the target navigation information, and the input vector of the Kalman filter is a two-dimensional vector;
  • the car is navigated.
  • the inertial device by using the inertial device to obtain the motion state information of the car, and obtaining the vehicle information of the car according to the controller area network, as input data, it is not necessary to rely on the environment to collect data, thereby solving the problem of navigation effects caused by insufficient data acquisition.
  • Unstable problem use motion state information to obtain navigation information to achieve positioning without relying on environmental information; use Kalman filter to fuse navigation information and vehicle information, so as to use vehicle information to correct navigation information and improve the accuracy of navigation information, Improve the positioning accuracy, and use a two-dimensional vector as the input vector of the Kalman filter, the filtering method is simple, the consumption of hardware resources is small, and the navigation cost is reduced.
  • the motion state information of the car is obtained based on the inertial device, including:
  • the position coordinates, three-dimensional motion speed and attitude quaternion of the car are calculated;
  • the state vector of the car is constructed, and the state vector is used as the motion state information.
  • the position coordinates, three-dimensional motion speed and attitude quaternion of the car are obtained based on the calculation of the inertial device, and the motion state information is obtained based on this, so that the data collection does not need to depend on the environment and the difficulty of data collection is reduced.
  • the vehicle information of the car is obtained based on the controller area network, including:
  • the target corner angle and the target moving speed are used as vehicle information.
  • the angle residual value and the speed residual value are calculated to identify whether the target angle of rotation and the target movement speed are suddenly too large or too small, so as to ensure that the target angle of rotation and the target movement speed are available data , so as to ensure the accuracy of the calculation results of the navigation information.
  • the target corner angle and target moving speed of the car in the target time period including:
  • the corner angle is taken as the target corner angle
  • the moving speed is taken as the target moving speed
  • data alignment is performed on the corner angle, moving speed, and motion state information by acquiring time to ensure that the above data are collected at the same time, reduce data deviation, and improve data accuracy.
  • the motion state information includes position coordinates, three-dimensional motion speed and attitude quaternion, and the strapdown solution is performed on the motion state information to obtain navigation information, including:
  • the derivation of the position coordinates, the three-dimensional motion velocity and the attitude quaternion is performed to obtain the first derivation result, wherein the derivation result of the three-dimensional motion velocity is the three-dimensional motion acceleration;
  • a fourth-order approximation operation is performed on the second derivation result to obtain navigation information.
  • the deviation correction is performed on the three-dimensional motion acceleration in the first derivation result to obtain a second derivation result, including:
  • the preset calculation formula is: :
  • f xyz is the acceleration correction result
  • the navigation coordinate system is the three-dimensional motion acceleration
  • 2w ie ⁇ ven is the Coriolis acceleration caused by the motion of the car and the earth's rotation
  • wen ⁇ ven is the centripetal acceleration on the ground caused by the motion of the car
  • g is the gravitational acceleration.
  • the vehicle information includes the corner angle and the moving speed
  • the navigation information and the vehicle information are fused by using a preset Kalman filter to obtain the target navigation information, including:
  • the rotation angle and the moving speed are composed of a two-dimensional observation vector, and the angle residual value and the speed residual value are composed of the observation noise;
  • the navigation information is modified to obtain the target navigation information.
  • the Kalman gain value is calculated by the observation noise composed of the residual value, and the two-dimensional observation vector composed of the corner angle and the moving speed is used to correct the navigation information, so as to realize the correction of the navigation information by the vehicle information, Eliminate noise effects and improve data accuracy.
  • an embodiment of the present application provides a device for processing navigation information, including:
  • the acquisition module is used to acquire the motion state information of the car based on the inertial device, and obtain the vehicle information of the car based on the controller area network;
  • the solution module is used to perform strapdown solution to the motion state information to obtain the navigation information
  • the fusion module is used to fuse the navigation information and the vehicle information by using the preset Kalman filter to obtain the target navigation information, and the input vector of the Kalman filter is a two-dimensional vector;
  • the navigation module is used to navigate the car based on the target navigation information.
  • an embodiment of the present application provides an electronic device, including a memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to make the electronic device execute the navigation information processing method of the first aspect.
  • an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, implements the navigation information processing method according to the first aspect.
  • FIG. 1 is a schematic flowchart of a method for processing navigation information provided by an embodiment of the present application
  • FIG. 2 is a schematic structural diagram of an apparatus for processing navigation information provided by an embodiment of the present application
  • FIG. 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.
  • the present application provides a method for processing navigation information.
  • an inertial device to obtain the motion state information of the car, and obtaining the vehicle information of the car according to the controller area network, as input data
  • use motion state information to obtain navigation information to achieve positioning without relying on environmental information
  • use Kalman filter to fuse navigation information and vehicle information, so as to use
  • the vehicle information corrects the navigation information, improves the accuracy of the navigation information, improves the positioning accuracy, and uses a two-dimensional vector as the input vector of the Kalman filter.
  • the filtering method is simple, consumes less hardware resources, and reduces the cost of navigation.
  • FIG. 1 shows an implementation flowchart of a method for processing navigation information provided by an embodiment of the present application.
  • the following methods for processing navigation information in the embodiments of the present application can be applied to electronic devices, including but not limited to in-vehicle computers, smart phones, tablet computers, desktop computers, supercomputers, personal computers that are communicatively connected to the vehicle CAN bus Computer equipment such as digital assistants, physical servers, and cloud servers.
  • the navigation information processing method according to the embodiment of the present application includes steps S101 to S104, which are described in detail as follows:
  • Step S101 acquiring motion state information of the automobile based on the inertial device, and acquiring vehicle information of the automobile based on the controller area network.
  • the inertial device includes, but is not limited to, a three-dimensional acceleration sensor, a three-dimensional angular rate sensor, a gyroscope, etc., which can be installed on the vehicle body.
  • Controller Area Network is a standard bus of automotive computer control system and embedded industrial control area network, which contains vehicle CAN information such as vehicle speed, wheel speed and angle of rotation.
  • vehicle CAN information such as vehicle speed, wheel speed and angle of rotation.
  • the motion state information is the current motion situation of the car, which includes but is not limited to the speed, acceleration, and attitude angle of the car, and the vehicle information is the vehicle CAN information.
  • the inertial device by using the inertial device to obtain the motion state information of the car and the vehicle information of the car according to the controller area network as the input data, it is not necessary to rely on the environment to collect data, thereby solving the problem that the navigation effect is unstable due to insufficient data acquisition. question.
  • obtaining the motion state information of the car based on the inertial device includes: obtaining the three-axis acceleration and the three-axis angular rate of the car based on the inertial device; according to the three-axis acceleration and the three-axis angular rate, calculating the position coordinates, Three-dimensional motion speed and attitude quaternion; according to the position coordinates, three-dimensional motion speed and attitude quaternion, the state vector of the car is constructed, and the state vector is used as the motion state information.
  • the three-axis acceleration of the car can be obtained based on the three-dimensional acceleration sensor, and the three-axis angular rate of the car can be obtained based on the three-dimensional angular rate sensor.
  • the origin point of the car as the coordinate origin
  • the three-axis acceleration and the three-axis angular rate calculate the current position coordinates of the car (p x , p y , p z ), and the three-dimensional motion speed (v x , v y , v z )
  • the attitude quaternion (q 0 , q 1 , q 2 , q 3 ) composed of the attitude angle
  • the state vector X n ⁇ p x p y p z v x v y v z q 0 q 1 q 2 q 3 ⁇ .
  • the position coordinates, three-dimensional motion speed and attitude quaternion of the car are obtained based on the calculation of the inertial device, and motion state information is obtained based on this, so that the data collection does not need to depend on the environment, and the difficulty of data collection is reduced.
  • obtaining vehicle information of the car based on the controller area network includes: obtaining, based on the controller area network, the target corner angle and target moving speed of the car within the target time period; calculating the angular residual value of the target corner angle and the target The speed residual value of the moving speed; if the angle residual value is less than the first preset threshold and the speed residual value is less than the second preset threshold, the target corner angle and the target moving speed are used as vehicle information.
  • the difference between the moving speeds of adjacent targets within a period of time is obtained to obtain the difference V d , and the number m of V d whose V d is greater than the preset value is determined.
  • d is summed to obtain V sum , and the velocity residual value is calculated based on the following formula
  • the speed residual value is smaller than the first preset threshold, it indicates that the target moving speed within the time period is available data. It can be understood that the angle residual value can be obtained by calculating with reference to the operation mode of the velocity residual value, and details are not described herein again.
  • based on the controller area network obtain the target turning angle and target moving speed of the car in the target time period, including: obtaining the car's turning angle and the target time period based on the controller area network Moving speed; determine the first difference between the moment of acquiring the corner angle and the moment of acquiring the motion state information, and the second difference between the moment of acquiring the moving speed and the moment of acquiring the motion state information; if the first difference If the difference from the second value is smaller than the preset difference, the corner angle is taken as the target corner angle, and the moving speed is taken as the target moving speed.
  • the acquisition time of the corner angle, the moving speed and the motion state information is recorded, wherein the acquisition time of the motion state information may specifically be the acquisition time of the triaxial acceleration and the triaxial angular rate.
  • Data alignment is performed on the corner angle, moving speed and motion state information by acquiring time.
  • the first difference is less than the preset difference, it means that the corner angle and the motion state information are obtained within the same time period, and when the second difference is less than
  • the difference is preset, it means that the moving speed and motion state information are obtained within the same time period, and when the first difference and the second difference are both smaller than the preset difference, it means that the above three are obtained within the same time period.
  • Step S102 performing strapdown calculation on the motion state information to obtain navigation information.
  • the strapdown solution is a strapdown inertial navigation solution, and the strapdown represents a solid connection with the carrier (car).
  • the motion state information includes position coordinates, three-dimensional motion speed and attitude quaternion.
  • the strapdown solution process can be used to derive the motion state information to obtain navigation information, so as to realize positioning without relying on environmental information.
  • performing a strapdown solution on the motion state information to obtain the navigation information includes: derivation of the position coordinates, the three-dimensional motion speed and the attitude quaternion, to obtain a first derivation result, wherein the three-dimensional motion speed is The derivation result is the three-dimensional motion acceleration; the deviation correction is performed on the three-dimensional motion acceleration in the first derivation result to obtain the second derivation result; the fourth-order approximation operation is performed on the second derivation result to obtain the navigation information.
  • the motion state information X n ⁇ p x p y p z v x v y v z q 0 q 1 q 2 q 3 ⁇
  • the derivation of the position coordinates (p x , p y , p z ) The result is the velocity (u x , u y , u z )
  • the derivation result of the three-dimensional motion velocity (v x , v y , v z ) is the three-dimensional motion acceleration (a x , a y , a z )
  • the derivation result of (q 0 , q 1 , q 2 , q 3 ) is the attitude angle w dot
  • a fourth-order approximation operation is performed on the second derivative result, specifically:
  • X n+4 is the navigation information, which includes the car position, motion speed and attitude quaternion.
  • performing deviation correction on the three-dimensional motion acceleration in the first derivation result to obtain a second derivation result including: using a preset calculation formula to perform a deviation correction on the three-dimensional motion acceleration, obtaining an acceleration correction result, and converting the position coordinates
  • the derivation result, the derivation result of the attitude quaternion and the acceleration correction result constitute the second derivation result.
  • the preset calculation formula is:
  • f xyz is the acceleration correction result
  • the navigation coordinate system is the three-dimensional motion acceleration
  • 2w ie ⁇ ven is the Coriolis acceleration caused by the motion of the car and the earth's rotation
  • wen ⁇ ven is the centripetal acceleration on the ground caused by the motion of the car
  • g is the gravitational acceleration.
  • the gravitational acceleration and the Coriolis acceleration are removed to eliminate the adverse effect of the harmful acceleration on the three-dimensional motion acceleration, thereby improving the accuracy of the calculation result.
  • Step S103 using a preset Kalman filter to fuse navigation information and vehicle information to obtain target navigation information, and the input vector of the Kalman filter is a two-dimensional vector.
  • the Kalman filter is an algorithm for optimally estimating the system state by using the linear system state equation and inputting and outputting observation data of the system. Since the observed data includes the effects of noise and interference in the system, the optimal estimation can also be viewed as a filtering process. Since the navigation information is biased to a certain extent, the navigation information is corrected. Specifically, in this embodiment, the vehicle information is used to filter the navigation information, so that the vehicle information can be corrected to the navigation information, the accuracy of the navigation information is improved, and the positioning accuracy is improved, and a two-dimensional vector is used as the input vector of the Kalman filter. , the filtering method is simple to take, consumes less hardware resources, and reduces the cost of navigation.
  • the vehicle information includes a corner angle and a moving speed
  • a preset Kalman filter is used to fuse the navigation information and the vehicle information to obtain the target navigation information, including: obtaining the angular residual value of the corner angle and the movement speed.
  • the speed residual value of the speed; the rotation angle and the moving speed are composed of a two-dimensional observation vector, and the angle residual value and the speed residual value are composed of the observation noise; based on the observation noise, the Kalman gain value of the Kalman filter is calculated; based on The two-dimensional observation vector and the Kalman gain value are used to correct the navigation information to obtain the target navigation information.
  • observation data are the angle of rotation and the moving speed
  • the observation noise is the angle residual value of the corner angle and the velocity residual value of the moving speed Kalman gain is where H is the transformation matrix.
  • P is the covariance;
  • correction formula for the data correction of the navigation information is: for the latest navigation information. To get the Kalman gain when calculating the next navigation information, update the covariance
  • the Kalman gain value is calculated by the observation noise composed of the residual value, and the two-dimensional observation vector composed of the corner angle and the moving speed is used to correct the navigation information, so as to realize the correction of the navigation information by the vehicle information and eliminate the influence of noise. , to improve data accuracy.
  • Step S104 navigating the car based on the target navigation information.
  • the car can be navigated in various environments.
  • FIG. 2 is a structural block diagram of an apparatus for processing navigation information provided by an embodiment of the present application.
  • each module included in the apparatus is used to execute each step in the embodiment corresponding to FIG. 1 .
  • FIG. 1 and the related description in the embodiment corresponding to FIG. 1 For the convenience of description, only the parts related to this embodiment are shown.
  • the apparatus for processing navigation information provided by the embodiments of this application includes:
  • an acquisition module 201 configured to acquire motion state information of the automobile based on the inertial device, and acquire vehicle information of the automobile based on the controller area network;
  • the solution module 202 is used to perform strapdown solution to the motion state information to obtain navigation information
  • the fusion module 203 is configured to use a preset Kalman filter to fuse navigation information and vehicle information to obtain target navigation information, and the input vector of the Kalman filter is a two-dimensional vector;
  • the navigation module 204 is used for navigating the car based on the target navigation information.
  • the above obtaining module 201 includes:
  • a first acquisition unit used for acquiring the triaxial acceleration and triaxial angular rate of the vehicle based on the inertial device
  • the first calculation unit is used to calculate the position coordinates, the three-dimensional motion speed and the attitude quaternion of the car according to the three-axis acceleration and the three-axis angular rate;
  • the construction unit is used to construct the state vector of the car according to the position coordinates, the three-dimensional motion speed and the attitude quaternion, and use the state vector as the motion state information.
  • the above obtaining module 201 further includes:
  • a second obtaining unit configured to obtain the target turning angle and target moving speed of the vehicle within the target time period based on the controller area network
  • the second calculation unit is used to calculate the angular residual value of the target corner angle and the speed residual value of the target moving speed;
  • the target corner angle and the target moving speed are used as vehicle information.
  • the second obtaining unit includes:
  • the acquisition sub-unit is used to acquire the turning angle and moving speed of the car within the target time period based on the controller area network;
  • a determination subunit for determining the first difference between the acquisition moment of the corner angle and the acquisition moment of the motion state information, and the second difference between the acquisition moment of the moving speed and the acquisition moment of the motion state information;
  • the corner angle is taken as the target corner angle
  • the moving speed is taken as the target moving speed
  • the motion state information includes position coordinates, three-dimensional motion speed and attitude quaternion, and the above-mentioned calculation module 202 includes:
  • the derivation unit is used to derive the position coordinates, the three-dimensional motion velocity and the attitude quaternion, and obtain the first derivation result, wherein the derivation result of the three-dimensional motion velocity is the three-dimensional motion acceleration;
  • a first correction unit configured to perform deviation correction on the three-dimensional motion acceleration in the first derivation result to obtain a second derivation result
  • the operation unit is configured to perform a fourth-order approximation operation on the second derivation result to obtain navigation information.
  • the first correction unit includes:
  • the correction subunit is used to correct the deviation of the three-dimensional motion acceleration by using the preset calculation formula to obtain the acceleration correction result, and form the second derivation result of the derivation result of the position coordinates, the derivation result of the attitude quaternion and the acceleration correction result , the default calculation formula is:
  • f xyz is the acceleration correction result
  • the navigation coordinate system is the three-dimensional motion acceleration
  • 2w ie ⁇ ven is the Coriolis acceleration caused by the motion of the car and the earth's rotation
  • wen ⁇ ven is the centripetal acceleration on the ground caused by the motion of the car
  • g is the gravitational acceleration.
  • the vehicle information includes a turning angle and a moving speed
  • the fusion module 303 includes:
  • the third obtaining unit is used to obtain the angle residual value of the corner angle and the speed residual value of the moving speed;
  • the composition unit is used to form a two-dimensional observation vector with the rotation angle and the moving speed, and form the observation noise with the angle residual value and the speed residual value;
  • the third calculation unit is used to calculate the Kalman gain value of the Kalman filter based on the observation noise
  • the second correction unit is configured to perform data correction on the navigation information based on the two-dimensional observation vector and the Kalman gain value to obtain target navigation information.
  • the above-mentioned apparatus for processing navigation information may implement the method for processing navigation information in the above-mentioned method embodiments.
  • the options in the foregoing method embodiment are also applicable to this embodiment, and are not described in detail here.
  • For the remaining contents of the embodiments of the present application reference may be made to the contents of the foregoing method embodiments, which will not be repeated in this embodiment.
  • FIG. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
  • the electronic device 3 of this embodiment includes: at least one processor 30 (only one is shown in FIG. 3 ), a processor, a memory 31 , and a processor stored in the memory 31 and capable of processing in the at least one A computer program 32 running on the processor 30, the processor 30 implements the steps in any of the above method embodiments when the computer program 32 is executed.
  • the electronic device 3 may be a computing device such as a smart phone, a tablet computer, a desktop computer, a supercomputer, a personal digital assistant, a physical server, and a cloud server.
  • the electronic device may include, but is not limited to, the processor 30 and the memory 31 .
  • FIG. 3 is only an example of the electronic device 3, and does not constitute a limitation to the electronic device 3, and may include more or less components than the one shown, or combine some components, or different components , for example, may also include input and output devices, network access devices, and the like.
  • the so-called processor 30 may be a central processing unit (Central Processing Unit, CPU), and the processor 30 may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuits) , ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • the memory 31 may be an internal storage unit of the electronic device 3 , such as a hard disk or a memory of the electronic device 3 .
  • the memory 31 may also be an external storage device of the electronic device 3 in other embodiments, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, flash memory card (Flash Card), etc.
  • the memory 31 may also include both an internal storage unit of the electronic device 3 and an external storage device.
  • the memory 31 is used to store an operating system, an application program, a boot loader (Boot Loader), data, and other programs, such as program codes of the computer program.
  • the memory 31 can also be used to temporarily store data that has been output or will be output.
  • an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the steps in any of the foregoing method embodiments can be implemented.
  • the embodiments of the present application provide a computer program product, when the computer program product runs on an electronic device, the steps in the foregoing method embodiments can be implemented when the electronic device executes.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more functions for implementing the specified logical function(s) executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures.
  • each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented in dedicated hardware-based systems that perform the specified functions or actions , or can be implemented in a combination of dedicated hardware and computer instructions.
  • each functional module in each embodiment of the present application may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.
  • the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium.
  • the technical solution of the present application can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution.
  • the computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or CD and other media that can store program codes .

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Abstract

一种导航信息的处理方法、装置、电子设备及存储介质，导航信息的处理方法包括：基于惯性器件获取汽车的运动状态信息，基于控制器局域网络获取汽车的车辆信息（S101）；对运动状态信息进行捷联解算，得到导航信息（S102）；利用预设的卡尔曼滤波器，对导航信息与车辆信息进行融合，得到目标导航信息，卡尔曼滤波器的输入向量为二维向量（S103）；基于目标导航信息，对汽车进行导航（S104）。导航信息的处理方法不需要依赖环境采集数据，解决数据获取不足而导致导航效果不稳定的问题，利用卡尔曼滤波器融合导航信息和车辆信息，提高导航信息的准确度，提高定位精度，采用二维向量作为卡尔曼滤波器的输入向量，滤波方式简单，对硬件资源消耗少，降低导航成本。

Description

导航信息的处理方法、装置、电子设备及存储介质 技术领域
本申请涉及计算机技术领域，具体而言，涉及一种导航信息的处理方法、装置、电子设备及存储介质。
背景技术
目前，随着现代汽车工业的快速发展，智能驾驶已成为汽车行业的发展趋势。智能驾驶是工业革命和信息化结合的重要抓手，其将能够改变人、资源要素和产品的流动方式，颠覆性地改变人类生活。智能驾驶是指机器帮助人进行驾驶，以及在特殊情况下完全取代人驾驶的技术。
在相关技术中，将机器视觉、卫星定位和激光雷达定位等技术大量应用于智能驾驶。但是为了提高精度，以上技术所需要提供的数据越来越多，算法单元也越来越庞大，从而对软硬件的要求越来越高，最后直接导致智能驾驶技术难以普及。特别是地下车库的封闭性和光源的不稳定性，许多参与定位的设备无法提高大量数据而导致设备失效。可见，目前的智能驾驶存在导航成本高和导航效果不稳定的问题。
发明内容
本申请实施例的目的在于提供一种导航信息的处理方法、装置、电子设备及存储介质，旨在解决目前车辆导航存在导航成本高和导航效果不稳定的问题。
第一方面，本申请实施例提供了一种导航信息的处理方法，包括：
基于惯性器件获取汽车的运动状态信息，以及基于控制器局域网络获取汽车的车辆信息；
对运动状态信息进行捷联解算，得到导航信息；
利用预设的卡尔曼滤波器，对导航信息与车辆信息进行融合，得到目标导航信息，卡尔曼滤波器的输入向量为二维向量；
基于目标导航信息，对汽车进行导航。
在本实施例中，通过将惯性器件获取汽车的运动状态信息，以及根据控制器局域网络获取汽车的车辆信息，作为输入数据，从而不需要依赖环境采集数据，进而解决数据获取不足而导致导航效果不稳定的问题；利用运动状态信息得到导航信息，实现不依赖环境信息而定位；利用卡尔曼滤波器融合导航信息和车辆信息，从而采用车辆信息对导航信息进行修正，提高导航信息的准确度，提高定位精度，以及采用二维向量作为卡尔曼滤波器的输入向量，滤波方式简单拿，对硬件资源消耗少，降低导航成本。
进一步地，基于惯性器件获取汽车的运动状态信息，包括：
基于惯性器件获取汽车的三轴加速度和三轴角速率；
根据三轴加速度和三轴角速率，计算出汽车的位置坐标、三维运动速度和姿态四元数；
根据位置坐标、三维运动速度和姿态四元数，构建汽车的状态向量，并将状态向量作为运动状态信息。
在本实施例中，基于惯性器件运算得到汽车的位置坐标、三维运动速度和姿态四元数，并基于此得到运动状态信息，从而不需要依赖环境采集数据，降低数据采集难度。
进一步地，基于控制器局域网络获取汽车的车辆信息，包括：
基于控制器局域网络，获取汽车在目标时间段内的目标转角角度和目标移动速度；
计算目标转角角度的角度残差值和目标移动速度的速度残差值；
若角度残差值小于第一预设阈值，以及速度残差值小于第二预设阈值，则将目标转角角度和目标移动速度作为车辆信息。
在本实施例中，通过计算角度残差值和速度残差值，以识别目标转角角度和目标移动速度是否存在突然过大或国小的数据，从而保证目标转角角度和目标移动速度为可用数据，进而保证导航信息计算结果的准确性。
进一步地，基于控制器局域网络，获取汽车在目标时间段内的目标转角角度和目标移动速度，包括：
基于控制器局域网络，获取汽车在目标时间段内的转角角度和移动速度；
确定转角角度的获取时刻与运动状态信息的获取时刻之间的第一差值，以及移动速度的获取时刻与运动状态信息的获取时刻之间的第二差值；
若第一差值与第二差值均小于预设差值，则将转角角度作为目标转角角度，将移动速度作为目标移动速度。
在本实施例中，通过获取时间对转角角度、移动速度与运动状态信息进行数据对齐，保证上述数据是在相同时刻采集到的，减少数据偏差，以提高数据准确度。
进一步地，运动状态信息包括位置坐标、三维运动速度和姿态四元数，对运动状态信息进行捷联解算，得到导航信息，包括：
对位置坐标、三维运动速度和姿态四元数进行求导，得到第一求导结果，其中三维运动速度的求导结果为三维运动加速度；
对第一求导结果中的三维运动加速度进行偏差修正，得到第二求导结果；
对第二求导结果进行四阶近似运算，得到导航信息。
在本实施例中，通过对三维运动加速度进行修正，以降低重力加速度的有害影响，以及对第二求导结果四阶近似运算，提高导航信息的准确度。
进一步地，对第一求导结果中的三维运动加速度进行偏差修正，得到第二求导结果，包括：
利用预设计算公式对三维运动加速度进行偏差修正，得到加速度修正 结果，将位置坐标的求导结果、姿态四元数的求导结果和加速度修正结果组成第二求导结果，预设计算公式为：
Figure PCTCN2022077896-appb-000001
其中f xyz为加速度修正结果，
Figure PCTCN2022077896-appb-000002
为导航坐标系，
Figure PCTCN2022077896-appb-000003
为三维运动加速度，2w ie×v en为汽车运动与地球自转引起的哥氏加速度，w en×v en为汽车运动引起的对地向心加速度，g为重力加速度。
在本实施例中，通过去除重力加速度、哥氏加速度，以消除有害加速度对三维运动加速度的不利影响，提高运算结果的准确度。
进一步地，车辆信息包括转角角度和移动速度，利用预设的卡尔曼滤波器，对导航信息与车辆信息进行融合，得到目标导航信息，包括：
获取转角角度的角度残差值和移动速度的速度残差值；
将转角角度与移动速度组成二维观测向量，以及将角度残差值和速度残差值组成观测噪声；
基于观测噪声，计算卡尔曼滤波器的卡尔曼增益值；
基于二维观测向量和卡尔曼增益值，对导航信息进行数据修正，得到目标导航信息。
在本实施例中，通过残差值组成的观测噪声计算卡尔曼增益值，并利用转角角度和移动速度组成的二维观测向量，对导航信息进行修正，以实现车辆信息对导航信息的修正，消除噪声影响，提高数据准确度。
第二方面，本申请实施例提供了一种导航信息的处理装置，包括：
获取模块，用于基于惯性器件获取汽车的运动状态信息，以及基于控制器局域网络获取汽车的车辆信息；
解算模块，用于对运动状态信息进行捷联解算，得到导航信息；
融合模块，用于利用预设的卡尔曼滤波器，对导航信息与车辆信息进行融合，得到目标导航信息，卡尔曼滤波器的输入向量为二维向量；
导航模块，用于基于目标导航信息，对汽车进行导航。
第三方面，本申请实施例提供了一种电子设备，包括存储器及处理器，存储器用于存储计算机程序，处理器运行计算机程序以使电子设备执行如第一方面的导航信息的处理方法。
第四方面，本申请实施例提供了一种计算机可读存储介质，其存储有计算机程序，计算机程序被处理器执行时实现如第一方面的导航信息的处理方法。
可以理解，第二方面至第四方面的有益效果可以参见上述第一方面的相关描述，在此不再赘述。
附图说明
为了更清楚地说明本申请实施例的技术方案，下面将对本申请实施例中所需要使用的附图作简单地介绍，应当理解，以下附图仅示出了本申请的某些实施例，因此不应被看作是对范围的限定，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他相关的附图。
图1为本申请实施例提供的导航信息的处理方法的流程示意图；
图2为本申请实施例提供的导航信息的处理装置的结构示意图；
图3为本申请实施例提供的电子设备的结构示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行描述。
应注意到：相似的标号和字母在下面的附图中表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行进一步定义和解释。同时，在本申请的描述中，术语“第一”、“第二”等仅用于区 分描述，而不能理解为指示或暗示相对重要性。
如背景技术相关记载，当前技术在地下车库的封闭性和光源的不稳定性的情况，许多参与定位的设备无法提高大量数据而导致设备失效，存在导航成本高和导航效果不稳定的问题。
针对上述现有技术中的问题，本申请提供了一种导航信息的处理方法，通过将惯性器件获取汽车的运动状态信息，以及根据控制器局域网络获取汽车的车辆信息，作为输入数据，从而不需要依赖环境采集数据，进而解决数据获取不足而导致导航效果不稳定的问题；利用运动状态信息得到导航信息，实现不依赖环境信息而定位；利用卡尔曼滤波器融合导航信息和车辆信息，从而采用车辆信息对导航信息进行修正，提高导航信息的准确度，提高定位精度，以及采用二维向量作为卡尔曼滤波器的输入向量，滤波方式简单拿，对硬件资源消耗少，降低导航成本。
参见图1，图1示出了本申请实施例提供的一种导航信息的处理方法的实现流程图。本申请实施例中下述的导航信息的处理方法可应用于电子设备，电子设备包括但不限于与车辆CAN总线通信连接的车载电脑、智能手机、平板电脑、桌上型计算机、超级计算机、个人数字助理、物理服务器和云服务器等计算机设备。本申请实施例的导航信息的处理方法，包括步骤S101至S104，详述如下：
步骤S101，基于惯性器件获取汽车的运动状态信息，以及基于控制器局域网络获取汽车的车辆信息。
在本实施例中，惯性器件包括但不限于三维加速度传感器、三维角速率传感器、陀螺仪等，其可以安装于汽车车身上。控制器局域网络(Controller Area Network，CAN)是汽车计算机控制系统和嵌入式工业控制局域网的标准总线，其包含有车速、轮速和转角角度等车辆CAN信息。运动状态信息为汽车当前的运动情况，其包括但不限于汽车速度、加速度和姿态角等，车辆信息为车辆CAN信息。
本实施例通过将惯性器件获取汽车的运动状态信息，以及根据控制器局域网络获取汽车的车辆信息，作为输入数据，从而不需要依赖环境采集数据，进而解决数据获取不足而导致导航效果不稳定的问题。
在一实施例中，基于惯性器件获取汽车的运动状态信息，包括：基于惯性器件获取汽车的三轴加速度和三轴角速率；根据三轴加速度和三轴角速率，计算出汽车的位置坐标、三维运动速度和姿态四元数；根据位置坐标、三维运动速度和姿态四元数，构建汽车的状态向量，并将状态向量作为运动状态信息。
在本实施例中，可以基于三维加速度传感器获取汽车的三轴加速度，可以基于三维角速率传感器获取汽车的三轴角速率。以汽车始发点作为坐标原点，基于三轴加速度和三轴角速率，计算出汽车的当前位置坐标(p x,p y,p z)，三维运动速度(v x,v y,v z)和姿态角组成的姿态四元数(q 0,q 1,q 2,q 3)，最后构建状态向量X n＝{p x p y p z v x v y v z q 0q 1q 2q 3}。
本实施例基于惯性器件运算得到汽车的位置坐标、三维运动速度和姿态四元数，并基于此得到运动状态信息，从而不需要依赖环境采集数据，降低数据采集难度。
可选地，基于控制器局域网络获取汽车的车辆信息，包括：基于控制器局域网络，获取汽车在目标时间段内的目标转角角度和目标移动速度；计算目标转角角度的角度残差值和目标移动速度的速度残差值；若角度残差值小于第一预设阈值，以及速度残差值小于第二预设阈值，则将目标转角角度和目标移动速度作为车辆信息。
在本实施例中，针对目标移动速度，对一段时间内相邻的目标移动速度作差，得到差值V d，确定V d大于预设值的V d数量m，对大于预设值的V d求和得到V sum，基于下列公式计算速度残差值
Figure PCTCN2022077896-appb-000004
当速度残差值小于第一预设阈值时，表示该时间段内的目标移动速度为可用数据。 可以理解的是，角度残差值可参照速度残差值的运算方式进行计算得到，在此不再赘述。
本实施例通过计算角度残差值和速度残差值，以识别目标转角角度和目标移动速度是否存在突然过大或国小的数据，从而保证目标转角角度和目标移动速度为可用数据，进而保证导航信息计算结果的准确性。
在一种可能实现的方式中，基于控制器局域网络，获取汽车在目标时间段内的目标转角角度和目标移动速度，包括：基于控制器局域网络，获取汽车在目标时间段内的转角角度和移动速度；确定转角角度的获取时刻与运动状态信息的获取时刻之间的第一差值，以及移动速度的获取时刻与运动状态信息的获取时刻之间的第二差值；若第一差值与第二差值均小于预设差值，则将转角角度作为目标转角角度，将移动速度作为目标移动速度。
在本实现方式中，记录转角角度、移动速度和运动状态信息的获取时刻，其中运动状态信息的获取时刻可具体为三轴加速度和三轴角速率的获取时刻。通过获取时间对转角角度、移动速度与运动状态信息进行数据对齐，当第一差值小于预设差值时，说明转角角度与运动状态信息在相同时间段内获取得到，当第二差值小于预设差值时，说明移动速度与运动状态信息在相同时间段内获取得到，当第一差值与第二差值均小于预设差值，说明上述三者在相同时间段内获取得到。本实施例保证上述数据是在相同时刻采集到的，减少数据偏差，以提高数据准确度。
步骤S102，对运动状态信息进行捷联解算，得到导航信息。
在本实施例中，捷联解算为捷联式惯性导航解算，捷联表示与载体(汽车)固联。本实施例运动状态信息包括位置坐标、三维运动速度和姿态四元数，捷联解算过程可以为对运动状态信息进行求导，得到导航信息，实现不依赖环境信息而定位。
在一实施例中，对运动状态信息进行捷联解算，得到导航信息，包括： 对位置坐标、三维运动速度和姿态四元数进行求导，得到第一求导结果，其中三维运动速度的求导结果为三维运动加速度；对第一求导结果中的三维运动加速度进行偏差修正，得到第二求导结果；对第二求导结果进行四阶近似运算，得到导航信息。
在本实施例中，运动状态信息X n＝{p x p y p z v x v y v z q 0q 1q 2q 3}，位置坐标(p x,p y,p z)的求导结果为速度(u x,u y,u z)，三维运动速度(v x,v y,v z)的求导结果为三维运动加速度(a x,a y,a z)，姿态四元数(q 0,q 1,q 2,q 3)的求导结果为姿态角w dot，则第一求导结果为X ndot＝{u x u y u z a x a y a z w dot}；对三维运动加速度(a x,a y,a z)修正后得到加速度(f x,f y,f z)，则第二求导结果为X ndot＝{u x u y u z f x f y f z w dot}。
示例性地，通过计算斜率K，对第二求导结果进行四阶近似运算，具体为：
K 1＝X ndot
Figure PCTCN2022077896-appb-000005
K 2＝X n+1dot
Figure PCTCN2022077896-appb-000006
K 3＝X n+2dot
X n+3＝X n+2+d t×K 3
K 4＝X n+3dot
Figure PCTCN2022077896-appb-000007
则X n+4为导航信息，其包含汽车位置、运动速度和姿态四元数。
可选地，对第一求导结果中的三维运动加速度进行偏差修正，得到第二求导结果，包括：利用预设计算公式对三维运动加速度进行偏差修正，得到加速度修正结果，将位置坐标的求导结果、姿态四元数的求导结果和 加速度修正结果组成第二求导结果，预设计算公式为：
Figure PCTCN2022077896-appb-000008
其中f xyz为加速度修正结果，
Figure PCTCN2022077896-appb-000009
为导航坐标系，
Figure PCTCN2022077896-appb-000010
为三维运动加速度，2w ie×v en为汽车运动与地球自转引起的哥氏加速度，w en×v en为汽车运动引起的对地向心加速度，g为重力加速度。
本实施例通过去除重力加速度、哥氏加速度，以消除有害加速度对三维运动加速度的不利影响，提高运算结果的准确度。
步骤S103，利用预设的卡尔曼滤波器，对导航信息与车辆信息进行融合，得到目标导航信息，卡尔曼滤波器的输入向量为二维向量。
在本实施例中，卡尔曼滤波器为利用线性系统状态方程，通过系统输入输出观测数据，对系统状态进行最优估算的算法。由于观测数据中包括系统中的噪声和干扰的影响，所以最优估算也可以看作是滤波过程。由于导航信息在一定程度上存在偏差，所以对导航信息进行修正。具体而言，本实施例采用车辆信息对导航信息进行滤波，从而实现车辆信息对导航信息进行修正，提高导航信息的准确度，提高定位精度，以及采用二维向量作为卡尔曼滤波器的输入向量，滤波方式简单拿，对硬件资源消耗少，降低导航成本。
在一实施例中，车辆信息包括转角角度和移动速度，利用预设的卡尔曼滤波器，对导航信息与车辆信息进行融合，得到目标导航信息，包括：获取转角角度的角度残差值和移动速度的速度残差值；将转角角度与移动速度组成二维观测向量，以及将角度残差值和速度残差值组成观测噪声；基于观测噪声，计算卡尔曼滤波器的卡尔曼增益值；基于二维观测向量和卡尔曼增益值，对导航信息进行数据修正，得到目标导航信息。
在本实施例中，观测数据为转角角度和移动速度
Figure PCTCN2022077896-appb-000011
观测噪 声为转角角度的角度残差值和移动速度的速度残差值
Figure PCTCN2022077896-appb-000012
卡尔曼增益为
Figure PCTCN2022077896-appb-000013
其中H为转换矩阵。P为协方差；对导航信息进行数据修正的修正公式为
Figure PCTCN2022077896-appb-000014
为最新的导航信息。为计算下一次导航信息时得到卡尔曼增益，所以对协方差进行更新
Figure PCTCN2022077896-appb-000015
本实施例通过残差值组成的观测噪声计算卡尔曼增益值，并利用转角角度和移动速度组成的二维观测向量，对导航信息进行修正，以实现车辆信息对导航信息的修正，消除噪声影响，提高数据准确度。
步骤S104，基于目标导航信息，对汽车进行导航。
在本实施例中，根据不依赖环境运算得到的目标导航信息，实现在多种环境下对汽车进行导航。
为了执行上述方法实施例对应的方法，以实现相应的功能和技术效果，下面提供一种导航信息的处理装置。参见图2，图2是本申请实施例提供的一种导航信息的处理装置的结构框图。本实施例中该装置包括的各模块用于执行图1对应的实施例中的各步骤，具体参见图1以及图1所对应的实施例中的相关描述。为了便于说明，仅示出了与本实施例相关的部分，本申请实施例提供的导航信息的处理装置，包括：
获取模块201，用于基于惯性器件获取汽车的运动状态信息，以及基于控制器局域网络获取汽车的车辆信息；
解算模块202，用于对运动状态信息进行捷联解算，得到导航信息；
融合模块203，用于利用预设的卡尔曼滤波器，对导航信息与车辆信息进行融合，得到目标导航信息，卡尔曼滤波器的输入向量为二维向量；
导航模块204，用于基于目标导航信息，对汽车进行导航。
在一实施例中，上述获取模块201，包括：
第一获取单元，用于基于惯性器件获取汽车的三轴加速度和三轴角速 率；
第一计算单元，用于根据三轴加速度和三轴角速率，计算出汽车的位置坐标、三维运动速度和姿态四元数；
构建单元，用于根据位置坐标、三维运动速度和姿态四元数，构建汽车的状态向量，并将状态向量作为运动状态信息。
在一实施例中，上述获取模块201，还包括：
第二获取单元，用于基于控制器局域网络，获取汽车在目标时间段内的目标转角角度和目标移动速度；
第二计算单元，用于计算目标转角角度的角度残差值和目标移动速度的速度残差值；
作为单元，用于若角度残差值小于第一预设阈值，以及速度残差值小于第二预设阈值，则将目标转角角度和目标移动速度作为车辆信息。
在一实施例中，第二获取单元，包括：
获取子单元，用于基于控制器局域网络，获取汽车在目标时间段内的转角角度和移动速度；
确定子单元，用于确定转角角度的获取时刻与运动状态信息的获取时刻之间的第一差值，以及移动速度的获取时刻与运动状态信息的获取时刻之间的第二差值；
作为子单元，用于若第一差值与第二差值均小于预设差值，则将转角角度作为目标转角角度，将移动速度作为目标移动速度。
在一实施例中，运动状态信息包括位置坐标、三维运动速度和姿态四元数，上述解算模块202，包括：
求导单元，用于对位置坐标、三维运动速度和姿态四元数进行求导，得到第一求导结果，其中三维运动速度的求导结果为三维运动加速度；
第一修正单元，用于对第一求导结果中的三维运动加速度进行偏差修正，得到第二求导结果；
运算单元，用于对第二求导结果进行四阶近似运算，得到导航信息。
在一实施例中，第一修正单元，包括：
修正子单元，用于利用预设计算公式对三维运动加速度进行偏差修正，得到加速度修正结果，将位置坐标的求导结果、姿态四元数的求导结果和加速度修正结果组成第二求导结果，预设计算公式为：
Figure PCTCN2022077896-appb-000016
其中f xyz为加速度修正结果，
Figure PCTCN2022077896-appb-000017
为导航坐标系，
Figure PCTCN2022077896-appb-000018
为三维运动加速度，2w ie×v en为汽车运动与地球自转引起的哥氏加速度，w en×v en为汽车运动引起的对地向心加速度，g为重力加速度。
在一实施例中，车辆信息包括转角角度和移动速度，融合模块303，包括：
第三获取单元，用于获取转角角度的角度残差值和移动速度的速度残差值；
组成单元，用于将转角角度与移动速度组成二维观测向量，以及将角度残差值和速度残差值组成观测噪声；
第三计算单元，用于基于观测噪声，计算卡尔曼滤波器的卡尔曼增益值；
第二修正单元，用于基于二维观测向量和卡尔曼增益值，对导航信息进行数据修正，得到目标导航信息。
上述的导航信息的处理装置可实施上述方法实施例的导航信息的处理方法。上述方法实施例中的可选项也适用于本实施例，这里不再详述。本申请实施例的其余内容可参照上述方法实施例的内容，在本实施例中，不再进行赘述。
图3为本申请一实施例提供的电子设备的结构示意图。如图3所示，该实施例的电子设备3包括：至少一个处理器30(图3中仅示出一个)处 理器、存储器31以及存储在所述存储器31中并可在所述至少一个处理器30上运行的计算机程序32，所述处理器30执行所述计算机程序32时实现上述任意方法实施例中的步骤。
所述电子设备3可以是智能手机、平板电脑、桌上型计算机、超级计算机、个人数字助理、物理服务器和云服务器等计算设备。该电子设备可包括但不仅限于处理器30、存储器31。本领域技术人员可以理解，图3仅仅是电子设备3的举例，并不构成对电子设备3的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如还可以包括输入输出设备、网络接入设备等。
所称处理器30可以是中央处理单元(Central Processing Unit，CPU)，该处理器30还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
所述存储器31在一些实施例中可以是所述电子设备3的内部存储单元，例如电子设备3的硬盘或内存。所述存储器31在另一些实施例中也可以是所述电子设备3的外部存储设备，例如所述电子设备3上配备的插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)等。进一步地，所述存储器31还可以既包括所述电子设备3的内部存储单元也包括外部存储设备。所述存储器31用于存储操作系统、应用程序、引导装载程序(BootLoader)、数据以及其他程序等，例如所述计算机程序的程序代码等。所述存储器31还可以用于暂时地存储已经输出或者将要输出的数据。
另外，本申请实施例还提供一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现可实 现上述任意方法实施例中的步骤。
本申请实施例提供了一种计算机程序产品，当计算机程序产品在电子设备上运行时，使得电子设备执行时实现可实现上述各个方法实施例中的步骤。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置和方法，也可以通过其它的方式实现。以上所描述的装置实施例仅仅是示意性的，例如，附图中的流程图和框图显示了根据本申请的多个实施例的装置、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或代码的一部分，所述模块、程序段或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现方式中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
另外，在本申请各个实施例中的各功能模块可以集成在一起形成一个独立的部分，也可以是各个模块单独存在，也可以两个或两个以上模块集成形成一个独立的部分。
所述功能如果以软件功能模块的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only  Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述仅为本申请的实施例而已，并不用于限制本申请的保护范围，对于本领域的技术人员来说，本申请可以有各种更改和变化。凡在本申请的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本申请的保护范围之内。应注意到：相似的标号和字母在下面的附图中表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行进一步定义和解释。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应所述以权利要求的保护范围为准。
需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。

Claims (10)

  1. 一种导航信息的处理方法，其特征在于，包括：
    基于惯性器件获取汽车的运动状态信息，以及基于控制器局域网络获取所述汽车的车辆信息；
    对所述运动状态信息进行捷联解算，得到导航信息；
    利用预设的卡尔曼滤波器，对所述导航信息与所述车辆信息进行融合，得到目标导航信息，所述卡尔曼滤波器的输入向量为二维向量；
    基于所述目标导航信息，对所述汽车进行导航。
  2. 根据权利要求1所述的导航信息的处理方法，其特征在于，所述基于惯性器件获取汽车的运动状态信息，包括：
    基于所述惯性器件获取所述汽车的三轴加速度和三轴角速率；
    根据所述三轴加速度和所述三轴角速率，计算出所述汽车的位置坐标、三维运动速度和姿态四元数；
    根据所述位置坐标、所述三维运动速度和所述姿态四元数，构建所述汽车的状态向量，并将所述状态向量作为所述运动状态信息。
  3. 根据权利要求1所述的导航信息的处理方法，其特征在于，所述基于控制器局域网络获取所述汽车的车辆信息，包括：
    基于所述控制器局域网络，获取所述汽车在目标时间段内的目标转角角度和目标移动速度；
    计算所述目标转角角度的角度残差值和所述目标移动速度的速度残差值；
    若所述角度残差值小于第一预设阈值，以及所述速度残差值小于第二预设阈值，则将所述目标转角角度和所述目标移动速度作为所述车辆信息。
  4. 根据权利要求3所述的导航信息的处理方法，其特征在于，所述基于所述控制器局域网络，获取所述汽车在目标时间段内的目标转角角度和 目标移动速度，包括：
    基于所述控制器局域网络，获取所述汽车在目标时间段内的转角角度和移动速度；
    确定所述转角角度的获取时刻与所述运动状态信息的获取时刻之间的第一差值，以及所述移动速度的获取时刻与所述运动状态信息的获取时刻之间的第二差值；
    若所述第一差值与所述第二差值均小于预设差值，则将所述转角角度作为所述目标转角角度，将所述移动速度作为所述目标移动速度。
  5. 根据权利要求1所述的导航信息的处理方法，其特征在于，所述运动状态信息包括位置坐标、三维运动速度和姿态四元数，所述对所述运动状态信息进行捷联解算，得到导航信息，包括：
    对所述位置坐标、所述三维运动速度和所述姿态四元数进行求导，得到第一求导结果，其中所述三维运动速度的求导结果为三维运动加速度；
    对所述第一求导结果中的三维运动加速度进行偏差修正，得到第二求导结果；
    对所述第二求导结果进行四阶近似运算，得到所述导航信息。
  6. 根据权利要求5所述的导航信息的处理方法，其特征在于，所述对所述第一求导结果中的三维运动加速度进行偏差修正，得到第二求导结果，包括：
    利用预设计算公式对所述三维运动加速度进行偏差修正，得到加速度修正结果，将所述位置坐标的求导结果、所述姿态四元数的求导结果和所述加速度修正结果组成所述第二求导结果，所述预设计算公式为：
    Figure PCTCN2022077896-appb-100001
    其中f xyz为所述加速度修正结果，
    Figure PCTCN2022077896-appb-100002
    为导航坐标系，
    Figure PCTCN2022077896-appb-100003
    为所述三维运动加速度，2w ie×v en为汽车运动与地球自转引起的哥氏加速度，w en×v en 为所述汽车运动引起的对地向心加速度，g为重力加速度。
  7. 根据权利要求1所述的导航信息的处理方法，其特征在于，所述车辆信息包括转角角度和移动速度，所述利用预设的卡尔曼滤波器，对所述导航信息与所述车辆信息进行融合，得到目标导航信息，包括：
    获取所述转角角度的角度残差值和所述移动速度的速度残差值；
    将所述转角角度与所述移动速度组成二维观测向量，以及将所述角度残差值和所述速度残差值组成观测噪声；
    基于所述观测噪声，计算所述卡尔曼滤波器的卡尔曼增益值；
    基于所述二维观测向量和所述卡尔曼增益值，对所述导航信息进行数据修正，得到目标导航信息。
  8. 一种导航信息的处理装置，其特征在于，包括：
    获取模块，用于基于惯性器件获取汽车的运动状态信息，以及基于控制器局域网络获取所述汽车的车辆信息；
    解算模块，用于对所述运动状态信息进行捷联解算，得到导航信息；
    融合模块，用于利用预设的卡尔曼滤波器，对所述导航信息与所述车辆信息进行融合，得到目标导航信息，所述卡尔曼滤波器的输入向量为二维向量；
    导航模块，用于基于所述目标导航信息，对所述汽车进行导航。
  9. 一种电子设备，其特征在于，包括存储器及处理器，所述存储器用于存储计算机程序，所述处理器运行所述计算机程序以使所述电子设备执行如权利要求1至7中任一项所述的导航信息的处理方法。
  10. 一种计算机可读存储介质，其特征在于，其存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1至7中任一项所述的导航信息的处理方法。
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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104076382A (zh) * 2014-07-22 2014-10-01 中国石油大学(华东) 一种基于多源信息融合的车辆无缝定位方法
CN105823484A (zh) * 2016-05-26 2016-08-03 清华大学 一种集成式惯性导航测量单元及相应测量方法
CN107063246A (zh) * 2017-04-24 2017-08-18 齐鲁工业大学 一种视觉导航/惯性导航的松散组合导航方法
CN108240809A (zh) * 2017-12-08 2018-07-03 佛吉亚好帮手电子科技有限公司 一种基于l3gd20芯片在qnx车机系统中惯导导航的系统及方法
CN108731667A (zh) * 2017-04-14 2018-11-02 百度在线网络技术（北京）有限公司 用于确定无人驾驶车辆的速度和位姿的方法和装置
CN111780756A (zh) * 2020-07-20 2020-10-16 北京百度网讯科技有限公司 车辆航位推算方法、装置、设备以及存储介质
US20200340819A1 (en) * 2018-07-26 2020-10-29 Trimble Inc. Vehicle manual guidance systems with steering wheel angle sensors and road wheel angle sensors
CN113008244A (zh) * 2021-02-25 2021-06-22 广州导远电子科技有限公司 导航信息的处理方法、装置、电子设备及存储介质

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111811521A (zh) * 2020-07-09 2020-10-23 北京百度网讯科技有限公司 定位方法和装置、电子设备、车端设备、自动驾驶汽车

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104076382A (zh) * 2014-07-22 2014-10-01 中国石油大学(华东) 一种基于多源信息融合的车辆无缝定位方法
CN105823484A (zh) * 2016-05-26 2016-08-03 清华大学 一种集成式惯性导航测量单元及相应测量方法
CN108731667A (zh) * 2017-04-14 2018-11-02 百度在线网络技术（北京）有限公司 用于确定无人驾驶车辆的速度和位姿的方法和装置
CN107063246A (zh) * 2017-04-24 2017-08-18 齐鲁工业大学 一种视觉导航/惯性导航的松散组合导航方法
CN108240809A (zh) * 2017-12-08 2018-07-03 佛吉亚好帮手电子科技有限公司 一种基于l3gd20芯片在qnx车机系统中惯导导航的系统及方法
US20200340819A1 (en) * 2018-07-26 2020-10-29 Trimble Inc. Vehicle manual guidance systems with steering wheel angle sensors and road wheel angle sensors
CN111780756A (zh) * 2020-07-20 2020-10-16 北京百度网讯科技有限公司 车辆航位推算方法、装置、设备以及存储介质
CN113008244A (zh) * 2021-02-25 2021-06-22 广州导远电子科技有限公司 导航信息的处理方法、装置、电子设备及存储介质

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