WO2023071442A1 - 一种数据处理方法和装置 - Google Patents

一种数据处理方法和装置 Download PDF

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Publication number
WO2023071442A1
WO2023071442A1 PCT/CN2022/113271 CN2022113271W WO2023071442A1 WO 2023071442 A1 WO2023071442 A1 WO 2023071442A1 CN 2022113271 W CN2022113271 W CN 2022113271W WO 2023071442 A1 WO2023071442 A1 WO 2023071442A1
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Prior art keywords
vehicle
moment
data
pose information
information
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PCT/CN2022/113271
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English (en)
French (fr)
Inventor
王磊杰
刘镇波
温丰
张洪波
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Huawei Technologies Co Ltd
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Huawei Technologies Co Ltd
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Priority to EP22885342.0A priority Critical patent/EP4414749A4/en
Publication of WO2023071442A1 publication Critical patent/WO2023071442A1/zh
Priority to US18/647,621 priority patent/US20240294179A1/en
Anticipated expiration legal-status Critical
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/04—Monitoring the functioning of the control system
    • B60W50/045—Monitoring control system parameters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/46—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being of a radio-wave signal type
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C22/00—Measuring distance traversed on the ground by vehicles, persons, animals or other moving solid bodies, e.g. using odometers, using pedometers
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/02—Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
    • B60W50/0225—Failure correction strategy
    • 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
    • G01C21/1652—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 with ranging devices, e.g. LIDAR or RADAR
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • G01S13/60—Velocity or trajectory determination systems; Sense-of-movement determination systems wherein the transmitter and receiver are mounted on the moving object, e.g. for determining ground speed, drift angle, ground track
    • G—PHYSICS
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    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
    • G01S13/867—Combination of radar systems with cameras
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/02—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems using reflection of acoustic waves
    • G01S15/50—Systems of measurement, based on relative movement of the target
    • G01S15/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • G01S15/60—Velocity or trajectory determination systems; Sense-of-movement determination systems wherein the transmitter and receiver are mounted on the moving object, e.g. for determining ground speed, drift angle, ground track
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/86—Combinations of sonar systems with lidar systems; Combinations of sonar systems with systems not using wave reflection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88—Sonar systems specially adapted for specific applications
    • G01S15/93—Sonar systems specially adapted for specific applications for anti-collision purposes
    • G01S15/931—Sonar systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/50—Systems of measurement based on relative movement of target
    • G01S17/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
    • G01S13/862—Combination of radar systems with sonar systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
    • G01S13/865—Combination of radar systems with lidar systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/87—Combinations of radar systems, e.g. primary radar and secondary radar
    • G01S13/874—Combination of several systems for attitude determination
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G01S2013/932—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles using own vehicle data, e.g. ground speed, steering wheel direction

Definitions

  • the present application relates to the field of intelligent driving, and more specifically, to a data processing method and device.
  • the odometer can provide relative positioning information for modules such as regulation and control, perception, fusion, and prediction.
  • modules such as regulation and control, perception, fusion, and prediction.
  • the requirements for odometers in automatic driving are: to work immediately after booting, continuous and smooth, stable and reliable, and to ensure a certain relative positioning accuracy.
  • the requirements for the accuracy and reliability of the odometer are getting higher and higher.
  • Currently commonly used odometers have many limitations, such as the information (eg, angular velocity and acceleration information) output by an inertial measurement unit (IMU) is usually used as a prediction during vehicle driving.
  • IMU inertial measurement unit
  • the accuracy of the odometer will drop seriously or even fail to work normally, which will affect the normal operation of the vehicle and may cause serious safety hazards.
  • the present application provides a data processing method and device, which can help to avoid the decrease in the accuracy of the odometer caused by the failure of a single sensor, thereby helping to improve the stability and reliability of the vehicle during the navigation process.
  • a data processing method is provided, the method is applied to a vehicle, and the vehicle includes one or more sensors, and the method includes: according to the pose information of the vehicle at the first moment and the first model, determining The first pose information at the second moment, the first model is the pose estimation model from the first moment to the second moment; acquire the data collected by the one or more sensors; according to the first pose information And the data, determine the second pose information of the vehicle at the second moment.
  • the first pose information at the current moment can be estimated through the pose information at the previous moment and the first model, and then the estimated first pose information at the current moment and the sensor data are obtained to obtain The second pose information at the current moment.
  • a single sensor for example, IMU
  • the above-mentioned vehicles may include one or more different types of vehicles, and may also include one or more different types of vehicles on land (for example, roads, roads, railways, etc.), water surfaces (for example: waterways, rivers, Oceans, etc.) or means of transport or movable objects that operate or move in space.
  • a vehicle may include a car, a bicycle, a motorcycle, a train, a subway, an airplane, a ship, an aircraft, a robot, an unmanned aerial vehicle, or other types of transportation means or movable objects, which are not limited in this embodiment of the present application.
  • the first model can be stored in the cloud server, and the vehicle can send the pose information at the first moment to the cloud server, and the cloud server can use the pose information at the first moment and the first model to determine the first pose information of the vehicle at the second moment.
  • the cloud server can send the first pose information to the vehicle, so that the vehicle can determine the second pose information of the vehicle at the second moment according to the data collected by the sensor and the first pose information.
  • determining the first pose information of the vehicle at the second moment according to the pose information of the vehicle at the first moment and the first model includes: according to The pose information of the vehicle at the first moment and the first model are used to determine an initial state transition matrix of the vehicle at the first moment; and the first pose information is determined according to the initial state transition matrix.
  • the vehicle may first determine the initial state transition matrix according to the pose information at the first moment and the first model, and then determine the first pose information at the second moment through the initial state transition matrix.
  • a single sensor for example, IMU
  • the first model includes a position estimation model from the first moment to the second moment, a speed estimation model from the first moment to the second moment, an acceleration estimation model from the first moment to the second moment model, the roll angle estimation model from the first moment to the second moment, the pitch angle estimation model from the first moment to the second moment, the heading angle estimation model from the first moment to the second moment, and the estimation model from the first moment to the second moment One or more of the angular velocity estimation models at the second moment.
  • the first model may include one or more of position, velocity, acceleration, roll angle, pitch angle, heading angle, and angular velocity estimation models, through which the pose information of the vehicle at the current moment can be calculated Prediction, so that there is no need to predict the pose information at the current moment through the data output by a single sensor, avoiding the decrease in the accuracy of the odometer due to sensor failure, which helps to improve the stability and reliability of the vehicle during navigation.
  • the first model is determined by one or more of the following formulas:
  • the method further includes: according to the covariance information of the vehicle at the first moment and the first model, determining the first Covariance information: according to the first covariance information and the data, determine the second covariance information of the vehicle at the second moment.
  • the first covariance information at the current moment can be estimated through the covariance information at the previous moment and the first model, and then the estimated first covariance information at the current moment and the sensor data are obtained to obtain The second covariance information at the current moment. It is not necessary to use the data output by a single sensor (for example, IMU) to estimate the covariance information at the current moment, avoiding the decrease in the accuracy of the odometer due to sensor failure, thereby helping to improve the stability and reliability of the vehicle during navigation .
  • a single sensor for example, IMU
  • the method before determining the second pose information of the vehicle at the second moment according to the first pose information and the data, the method further includes: obtaining A first calibration result, the first calibration result includes an online calibration result and/or an offline calibration result; wherein, according to the first pose information and the data, determine the second pose information of the vehicle at the second moment, The method includes: performing error compensation on the data according to the first calibration result to obtain error-compensated data; determining the second pose information according to the first pose information and the error-compensated data.
  • error compensation is performed on the sensor through online or offline calibration, which helps to further improve the accuracy of the odometer.
  • the vehicle may perform a cross-check on the online calibration result and the offline calibration result of the same parameter, so as to ensure the accuracy of the calibration parameters.
  • the first calibration result includes one or more of wheel speed scale coefficients, inertial measurement unit (IMU) zero bias, and lever arm parameters.
  • IMU inertial measurement unit
  • the method before performing error compensation on the data according to the first calibration result, the method further includes: verifying the data.
  • the verification manner includes but not limited to rationality verification, cross verification, and the like.
  • determining the second pose information of the vehicle at the second moment according to the first pose information and the data includes: according to the first pose information The pose information and the data are optimally estimated to obtain the second pose information.
  • performing optimal estimation based on the first pose information and the data includes: performing optimal estimation based on Kalman filtering according to the first pose information and the data; or, performing optimal estimation based on the first pose information and the data; A pose information and the data are optimally estimated based on the non-Kalman filter.
  • the optimal estimation based on the Kalman filter can obtain the second pose information of the vehicle at the current moment, which helps to avoid the dependence on a single sensor, and solves the serious decline in the accuracy of the odometer caused by the failure of the IMU. The problem of not working properly, thus improving the stability and reliability of the vehicle.
  • a data processing device comprising: a determining unit, configured to determine first pose information of the vehicle at a second moment according to the pose information of the vehicle at the first moment and the first model,
  • the first model is a pose estimation model from the first moment to the second moment;
  • the acquisition unit is used to acquire the data collected by the one or more sensors;
  • the determination unit is also used to obtain the data according to the first pose information and the data to determine the second pose information of the vehicle at the second moment.
  • the determining unit is specifically configured to: determine the position of the vehicle at the first moment according to the pose information of the vehicle at the first moment and the first model An initial state transition matrix; according to the initial state transition matrix, determine the first pose information.
  • the first model includes a position estimation model from the first moment to the second moment, a speed estimation model from the first moment to the second moment, an acceleration estimation model from the first moment to the second moment model, the roll angle estimation model from the first moment to the second moment, the pitch angle estimation model from the first moment to the second moment, the heading angle estimation model from the first moment to the second moment, and the estimation model from the first moment to the second moment One or more of the angular velocity estimation models at the second moment.
  • the first model is determined by one or more of the following formulas:
  • the determining unit is further configured to determine the vehicle's first covariance information; according to the first covariance information and the data, determine the second covariance information of the vehicle at the second moment.
  • the acquiring unit is further configured to determine the second position of the vehicle at the second moment in the determining unit according to the first pose information and the data Before obtaining the attitude information, the first calibration result is obtained, and the first calibration result includes the online calibration result and/or the offline calibration result; the determination unit is specifically used for: performing error compensation on the data according to the first calibration result to obtain The second pose information is determined according to the first pose information and the error-compensated data.
  • the first calibration result includes one or more of wheel speed scale coefficients, inertial measurement unit (IMU) zero bias, and lever arm parameters.
  • IMU inertial measurement unit
  • the device further includes: a checking unit, configured to perform error compensation on the data before the determining unit performs error compensation on the data according to the first calibration result check.
  • the determining unit is specifically configured to: perform optimal estimation based on Kalman filtering according to the first pose information and the data, to obtain the second pose information .
  • a data processing device comprising: a memory for storing computer instructions; a processor for executing the computer instructions stored in the memory, so that the device executes the method in the first aspect above .
  • a vehicle in a fourth aspect, includes the device described in any one of the second aspect or the third aspect above.
  • a computer program product comprising: computer program code, when the computer program code is run on a computer, the computer is made to execute the method in the above first aspect.
  • a computer-readable medium stores program codes, and when the computer program codes are run on a computer, the computer is made to execute the method in the above-mentioned first aspect.
  • an embodiment of the present application provides a chip system, the chip system includes a processor, configured to call a computer program or a computer instruction stored in a memory, so that the processor executes the method described in any one of the above aspects.
  • the processor is coupled to the memory through an interface.
  • the system on a chip further includes a memory, where computer programs or computer instructions are stored in the memory.
  • Fig. 1 is a schematic block diagram of a vehicle provided by an embodiment of the present application.
  • FIG. 2 is a schematic diagram of an application scenario provided by an embodiment of the present application.
  • Fig. 3 is a system application block diagram provided by the embodiment of the present application.
  • FIG. 4 is a schematic diagram of a process of processing data from various sensors in an embodiment of the present application.
  • Fig. 5 is a schematic diagram of the data processing process of the odometer fusion module in the embodiment of the present application.
  • Fig. 6 is a schematic flowchart of a data processing method provided by an embodiment of the present application.
  • Fig. 7 is a schematic diagram of a driving trajectory obtained through testing in the manner provided by the embodiment of the present application.
  • Fig. 8 is another schematic flowchart of the data processing method provided by the embodiment of the present application.
  • Fig. 9 is a schematic block diagram of a data processing device provided by an embodiment of the present application.
  • Fig. 10 is another schematic block diagram of a data processing device provided by an embodiment of the present application.
  • Fig. 1 is a schematic block diagram of a vehicle 100 provided by an embodiment of the present application.
  • Vehicle 100 may include perception system 120 and computing platform 150 .
  • the perception system 120 may include several kinds of sensors that sense information about the environment around the vehicle 100 .
  • the perception system 120 may include a positioning system (the positioning system may be a global positioning system (global positioning system, GPS) system, or the Beidou system or other positioning systems), IMU), laser radar, millimeter wave radar, ultrasonic radar, Camera device, wheel speed sensor (WSS).
  • the visual odometry (VO) can use the image data output by the camera device to estimate the pose of the vehicle
  • the LiDAR odometry (LO) can use the point cloud data output by the LiDAR to estimate the position of the vehicle. pose is estimated.
  • the perception system 120 may also include sensors of the interior systems of the monitored vehicle 100 (eg, interior air quality monitors, fuel gauges, oil temperature gauges, etc.). Sensor data from one or more of these sensors can be used to detect objects and their corresponding properties (position, shape, orientation, velocity, etc.). Such detection and identification is a critical function for safe operation of the vehicle 100 .
  • sensors of the interior systems of the monitored vehicle 100 eg, interior air quality monitors, fuel gauges, oil temperature gauges, etc.
  • Sensor data from one or more of these sensors can be used to detect objects and their corresponding properties (position, shape, orientation, velocity, etc.). Such detection and identification is a critical function for safe operation of the vehicle 100 .
  • Computing platform 150 may include at least one processor 151 that may execute instructions 153 stored in a non-transitory computer-readable medium such as memory 152 .
  • computing platform 150 may also be a plurality of computing devices that control individual components or subsystems of vehicle 100 in a distributed manner.
  • the processor 151 may be any conventional processor, such as a central processing unit (central processing unit, CPU).
  • the processor 151 may also include, for example, an image processor (graphic process unit, GPU), a field programmable gate array (field programmable gate array, FPGA), a system on chip (system on chip, SOC), an ASIC ( application specific integrated circuit, ASIC) or their combination.
  • memory 152 may also store data such as road maps, route information, the vehicle's position, direction, speed, and other such vehicle data, among other information. Such information may be used by vehicle 100 and computing platform 150 during operation of vehicle 100 in autonomous, semi-autonomous, and/or manual modes.
  • vehicle 100 may include one or more different types of vehicles, and may also include one or more different types of vehicles on land (for example, roads, roads, railways, etc.), water surfaces (for example: waterways, Rivers, oceans, etc.) or means of transport or movable objects that operate or move in space.
  • a vehicle may include a car, a bicycle, a motorcycle, a train, a subway, an airplane, a ship, an aircraft, a robot, an unmanned aerial vehicle, or other types of transportation means or movable objects, which are not limited in this embodiment of the present application.
  • the requirements for the accuracy and reliability of the odometer are getting higher and higher.
  • the currently commonly used odometer has many limitations, for example, the information output by the IMU (for example, angular velocity and acceleration information) is usually used as the predicted value during the driving process of the vehicle.
  • the accuracy of the odometer will drop seriously or even fail to work normally, which will affect the normal operation of the vehicle and may cause serious safety hazards.
  • the embodiments of the present application provide a data processing method and device, which can help to avoid the decrease in the accuracy of the odometer caused by the failure of a single sensor, thereby helping to improve the stability and reliability of the vehicle during the navigation process.
  • the vehicle can optimally estimate the pose of the vehicle by using the motion state at the previous moment as the predicted value and the information output by multiple sensors as the observed value, and obtain the pose information of the vehicle at the current moment.
  • the vehicle can obtain the motion information or positioning information of the vehicle based on the pose information at the current moment.
  • Predicted value the current motion state predicted based on the motion state at the previous moment.
  • the predicted value and observed value can be used for optimal estimation, so as to obtain a more accurate motion state at the current moment.
  • FIG. 2 shows a schematic diagram of an application scenario provided by an embodiment of the present application.
  • the vehicle 100 and the cloud service system 200 may be included.
  • the vehicle 100 and the cloud service system 200 may communicate through a network.
  • the vehicle 100 can combine the motion state of the last moment (for example, the position, speed, acceleration, heading angle, roll angle, etc. of the vehicle at the last moment) and the information output by one or more sensors at the current moment (for example, the camera).
  • the image data collected at the current moment and the point cloud data collected by the laser radar at the current moment are sent to the cloud server 200, and the cloud server 200 can use the motion state at the current moment predicted according to the motion state at the previous moment as the predicted value.
  • the predicted value can be used to indicate the first pose information and the first covariance information of the vehicle at the current moment; the cloud server 200 can use the information output by one or more sensors as the observed value, and the observed value can be used to indicate The sensors perceive and measure the surrounding environment of the vehicle at the current moment.
  • the vehicle can perform optimal estimation according to the above predicted value and observed value, and obtain the second pose information and the second covariance information of the vehicle 100 at the current moment.
  • the cloud server 200 can send the second pose information and the second covariance information at the current moment to the vehicle 100, so that the vehicle 100 can obtain the motion information of the vehicle 100 according to the second pose information and the second covariance information at the current moment or location information.
  • the vehicle 100 may send the pose information of the last moment to the cloud server, and the cloud server determines the first pose information of the vehicle 100 at the current moment according to the pose information of the last moment and the first model.
  • the cloud server 200 can send the first pose information to the vehicle 100, so that the vehicle 100 can determine the second pose information of the vehicle 100 at the current moment according to the information output by one or more sensors and the first pose information.
  • FIG. 3 shows a system application block diagram provided by the embodiment of the present application.
  • the input of the odometer fusion module includes but is not limited to information such as IMU, WSS, VO, and LO.
  • the vehicle's current pose and covariance information can be output.
  • the information output by the odometer fusion module can provide relative vehicle motion information for laser positioning and visual positioning, and on the other hand, it can also provide continuous and smooth relative positioning information for perception fusion, regulation control, prediction and other modules.
  • FIG. 4 shows a process of processing data from various sensors in the embodiment of the present application.
  • the data output by the IMU and the WSS can be error compensated to obtain the error compensated IMU data and the error compensated WSS data respectively.
  • the error-compensated IMU data and the error-compensated WSS data can be used as the input of the odometry fusion module; the sum data output by the camera device and the laser radar can be preprocessed through the pose estimation of the visual odometry and the laser odometry respectively.
  • the preprocessed information can be used as the input of the odometry fusion module.
  • the input of the odometer fusion module may be one or more of the above information.
  • the odometer fusion module can output the current pose and covariance information according to the input information.
  • Fig. 5 shows a schematic diagram of the data processing process of the odometer fusion module in the embodiment of the present application.
  • the IMU data after error compensation, the WSS data after error compensation, and the preprocessed LO and VO data can be used as the input of Kalman filter.
  • the IMU data includes the angular velocity and acceleration information of the vehicle
  • the WSS data includes the vehicle speed information
  • R represents the rotation matrix
  • t represents the translation vector.
  • the data processing process shown in FIG. 5 is described by using a Kalman filtering (Kalman filtering, KF) framework as an example, and this embodiment of the present application is not limited thereto.
  • the data processing process can also adopt a non-Kalman filtering framework.
  • the odometer fusion module can use the motion state of the vehicle at the last moment (for example, the pose and covariance information at the last moment) as the predicted value, and the sensor data after error compensation or preprocessing as the observed value, and perform the final calculation based on the Kalman filter. Optimal estimation, and output the pose and covariance information of the current moment.
  • FIG. 6 shows a schematic flowchart of a data processing method 600 provided by an embodiment of the present application. As shown in FIG. 6, the method 600 includes:
  • the data processing module acquires data collected by one or more sensors.
  • the data processing module may be located in the vehicle or in the cloud server, which is not limited in this embodiment of the present application.
  • the one or more sensors may be one or more of IMU, WSS, camera or laser radar.
  • the IMU data may include information such as angular velocity and acceleration
  • the WSS data may include information such as wheel speeds and steering wheel angles of the four wheels of the vehicle.
  • the data processing module can create 3 or more independent threads, including IMU threads, WSS threads and timer threads.
  • IMU thread IMU data reception, verification and other operations are performed; in the WSS thread, WSS data reception and verification are performed; in the timer thread, the system state equation is constructed, and the prediction is updated based on the vehicle's motion state at the previous moment.
  • the data processing module verifies the data of one or more sensors.
  • the way of verification includes but not limited to direct rationality verification, cross verification and so on.
  • the process of direct plausibility checking includes: the data processing module judges whether the angular velocity in the IMU data is greater than or equal to the angular velocity threshold. If the angular velocity in the IMU data is less than the angular velocity threshold, the calibration is normal; otherwise, the calibration fails.
  • the data processing module judges whether the acceleration in the IMU data is greater than or equal to the acceleration threshold. If the acceleration in the IMU data is less than the acceleration threshold, the calibration is normal; otherwise, the calibration fails.
  • the data processing module judges whether the wheel speeds of the four wheels in the WSS data are greater than or equal to a speed threshold. If the wheel speeds of the four wheels are all less than the speed threshold, the verification is normal; if the wheel speed of at least one of the four wheels is greater than or equal to the speed threshold, the verification fails.
  • the data processing module may perform direct plausibility checks based on one or more of angular velocity information, acceleration information, or wheel speeds of the four wheels.
  • the cross-validation method is as follows:
  • the data processing module can calculate the yaw rate w odom according to the following formula (1) and the wheel speed data:
  • V rr is the speed of the right rear wheel of the vehicle
  • V rl is the speed of the left rear wheel of the vehicle
  • r is the distance between the rear wheels of the vehicle
  • fabs represents the absolute value
  • the verification is normal; otherwise, it is considered that the vehicle is slipping, and the verification fails.
  • the data processing module acquires an online or offline calibration result.
  • the data processing module acquires the online calibration result of the sensor by means of online real-time estimation, or the data processing module acquires the offline calibration result of the sensor by reading the offline calibration parameters.
  • the calibration results include, but are not limited to, the wheel speed scale coefficient, the IMU gyro and the zero bias of the added meter, the lever arm parameters between the sensors, and the like.
  • the offline calibration results may include wheel speed scale coefficients and lever arm parameters, etc., which may be obtained by reading files or loading parameters.
  • the online calibration results include the IMU gyro and the zero bias of the added meter, the wheel speed scale coefficient, etc.
  • the main method is to expand the parameters to be estimated such as the IMU gyro and the added meter zero bias, the wheel speed scale coefficient, etc.
  • the Kalman filter implements online estimation.
  • the data processing module can acquire the online calibration result and the offline calibration result at the same time, so as to perform cross-check on the online calibration result and the offline calibration result. For example, after the data processing module obtains the online calibration result and the offline calibration result of the same parameter, it can judge the difference between the two. If the difference is smaller than the second preset difference, the calibration result is considered normal; otherwise, the calibration result is considered abnormal.
  • the data processing module performs error compensation on the sensor data that has passed the verification according to the online calibration result or the offline calibration result.
  • the compensated data can be used as the input of the odometer fusion module (or Kalman filter), or the compensated data can be used as the observation value in the odometer fusion module.
  • the error compensation items of the IMU data include the zero bias of the gyroscope and the added meter.
  • the gyro and the zero bias of the added meter are subtracted from the IMU data, and the compensated data is used as the input of the Kalman filter.
  • the WSS data includes four wheel speeds, steering wheel angle and other information.
  • the error compensation of the WSS data mainly uses the rear wheel speed data and the average value of the rear wheel speed. After deducting the influence of the scale coefficient, the compensated data is used as the Kalman input to the filter.
  • the odometer fusion module establishes a vehicle-based kinematics model.
  • the kinematic model of the vehicle established by the odometer fusion module can be as the following formulas (2)-(8):
  • ⁇ k is the roll angle of the vehicle at time k
  • ⁇ k-1 is the roll angle of the vehicle at k-1 time
  • ⁇ k-1 is the pitch angle of the vehicle at k-1 time
  • ⁇ k is the pitch angle of the vehicle at time k.
  • the odometer fusion module can obtain the initial state transition matrix F according to one or more of the above formulas (2)-(8).
  • the odometer fusion module predicts the motion state at the previous moment to obtain the motion state at the current moment.
  • the motion state at the last moment may be the pose and covariance information output by the Kalman filter at the last moment.
  • the odometer fusion module can predict the pose information at the current moment according to the following formula (9):
  • X(k) is the pose information of the vehicle at time k predicted by the odometer fusion module
  • F[k-1, X(k-1)] is the state transition matrix at time k-1
  • W(k-1 ) is the system noise of the vehicle at time k-1.
  • the odometer fusion module can predict the covariance matrix information at the current moment according to the following formula (10):
  • k-1) is the covariance matrix information of the vehicle at time k predicted by the odometer fusion module.
  • the initial state transition matrix F perform first-order linearization to obtain the state transition matrix ⁇ .
  • k-1) is the covariance matrix information of the vehicle at time k-1
  • Q is the mean square error of the system noise.
  • the odometer fusion module uses the error-compensated data in S604 as the observation value of the Kalman filter.
  • the odometer fusion module can be used as the observation equation according to the following formula (11):
  • Z(k) is the observed value
  • H[k, X(k)] is the observation matrix at time k
  • V(k) is the observation noise at time k.
  • the odometer fusion module performs optimal estimation based on the Kalman filter based on the motion state at the current moment predicted in S606 and the observed value in S607, so as to obtain the pose and covariance information of the vehicle at the current moment.
  • the calculation method of the Kalman filter gain can be as formula (12):
  • K(k) P(k
  • K(k) is the Kalman filter gain
  • R(k) is the mean square error of the observation noise.
  • the odometry fusion module can obtain updated pose information and covariance information according to the following formulas (13) and (14).
  • X(k) is the pose information output by the Kalman filter
  • k-1) is the one-step predicted value of the state variable.
  • P(k) is the covariance matrix information output by the Kalman filter
  • I is the identity matrix
  • a test is also carried out according to the data processing method provided above, and the IMU data collected by the vehicle, the wheel speed data and the integrated navigation data are used in the test.
  • the real-time kinematic (RTK) is a fixed solution state, and the data obtained by the post-processing of the integrated navigation data is used as the ground truth.
  • the driving trajectory is shown in Figure 7.
  • the solid line represents the real data
  • the dotted line represents the output result of the odometer
  • the dotted line in the dotted line box is the output result of the odometer when the IMU fails.
  • FIG. 8 shows a schematic flowchart of a data processing method 800 provided by an embodiment of the present application. The method is applied to a vehicle that includes one or more sensors. As shown in Figure 8, the method 800 includes:
  • the vehicle determines the first pose information of the vehicle at the second moment according to the pose information of the vehicle at the first moment and the first model, and the first model is the position from the first moment to the second moment. pose estimation model.
  • the first model includes a position estimation model from the first moment to the second moment, a velocity estimation model from the first moment to the second moment, an acceleration estimation model from the first moment to the second moment, and an estimation model from the first moment to the second moment.
  • the roll angle estimation model from the first moment to the second moment, the pitch angle estimation model from the first moment to the second moment, the heading angle estimation model from the first moment to the second moment, and the estimation model of the heading angle from the first moment to the second moment One or more of the angular velocity estimation models.
  • the position estimation model from the first moment to the second moment may be as shown in the above formula (2).
  • the speed estimation model from the first moment to the second moment may be as shown in the above formula (3).
  • the acceleration estimation model from the first moment to the second moment may be as shown in the above formula (4).
  • the roll angle estimation model from the first moment to the second moment may be as shown in the above formula (5).
  • the pitch angle estimation model from the first moment to the second moment may be as shown in the above formula (6).
  • the heading angle estimation model from the first moment to the second moment may be as shown in the above formula (7).
  • the angular velocity estimation model from the first moment to the second moment may be as shown in the above formula (8).
  • the vehicle determines the first pose information of the vehicle at the second moment according to the pose information of the vehicle at the first moment and the first model, including: the vehicle determines the first pose information of the vehicle at the first moment according to Determine the initial state transition matrix of the vehicle at the first moment based on the attitude information and the first model; determine the first attitude information of the vehicle according to the initial state transition matrix.
  • the vehicle determining the first pose information according to the initial state transition matrix includes: the vehicle determining the first pose information according to the initial state transition matrix and system noise at the first moment.
  • the first pose information may be determined according to the above formula (9).
  • the method further includes: determining, by the vehicle, first covariance information of the vehicle at the second moment according to the covariance information of the vehicle at the first moment and the first model; The covariance information and the data determine the second covariance information of the vehicle at the second moment.
  • the vehicle determines the first covariance information of the vehicle at the second moment according to the covariance information at the first moment and the first model, including: linearizing the initial state transition matrix at the first moment to obtain a state transition matrix; according to the state transition matrix, the covariance information at the first moment and the mean square error of system noise, determine the first covariance information.
  • the first covariance information may be determined according to the above formula (10).
  • the vehicle acquires data collected by the one or more sensors.
  • the one or more sensors may be one or more of IMU, WSS, camera or lidar.
  • the vehicle determines second pose information of the vehicle at the second moment according to the first pose information and the data.
  • the method further includes: the vehicle acquires a first calibration result, and the first calibration
  • the results include online calibration results and/or offline calibration results; wherein, the vehicle determines the second pose information of the vehicle at the second moment according to the first pose information and the data, including: the vehicle according to the first pose information
  • error compensation is performed on the data to obtain error-compensated data; the vehicle determines the second pose information according to the first pose information and the error-compensated data.
  • the first calibration result includes one or more of wheel speed scale coefficients, inertial measurement unit (IMU) bias, and lever arm parameters.
  • IMU inertial measurement unit
  • the method further includes: the vehicle verifies the data, and the verification includes one of rationality verification and cross verification. one or more species.
  • the vehicle determines the second pose information of the vehicle at the second moment according to the first pose information and the data, including: the vehicle performs optimal estimation according to the first pose information and the data , to get the second pose information.
  • the vehicle performs optimal estimation according to the first pose information and the data, including: the vehicle performs optimal estimation based on Kalman filtering according to the first pose information and the data; or, the vehicle performs optimal estimation based on the first pose information and the data;
  • the first pose information and the data are optimally estimated based on non-Kalman filtering.
  • FIG. 9 shows a schematic block diagram of a data processing apparatus 900 provided by an embodiment of the present application.
  • the device 900 includes:
  • the determination unit 910 is configured to determine the first pose information of the vehicle at the second moment according to the pose information of the vehicle at the first moment and the first model, and the first model is from the first moment to the second moment pose estimation model.
  • the obtaining unit 920 is configured to obtain data collected by the one or more sensors.
  • the determining unit 910 is further configured to determine second pose information of the vehicle at the second moment according to the first pose information and the data.
  • the determining unit 910 is specifically configured to: determine the initial state transition matrix of the vehicle at the first moment according to the pose information of the vehicle at the first moment and the first model; according to the initial state transition matrix, Determine the first pose information.
  • the determining unit 910 is further configured to determine the first covariance information of the vehicle at the second moment according to the covariance information of the vehicle at the first moment and the first model; information and the data to determine the second covariance information of the vehicle at the second moment.
  • the acquiring unit 920 is further configured to acquire the first calibration result before the determining unit 910 determines the second pose information of the vehicle at the second moment according to the first pose information and the data, the The first calibration result includes an online calibration result and/or an offline calibration result;
  • the determining unit 910 is specifically configured to: perform error compensation on the data according to the first calibration result to obtain error-compensated data; determine the second pose information according to the first pose information and the error-compensated data .
  • the first calibration result includes one or more of wheel speed scale coefficients, inertial measurement unit (IMU) bias, and lever arm parameters.
  • IMU inertial measurement unit
  • the apparatus 900 further includes: a checking unit, configured to check the data before the determining unit performs error compensation on the data according to the first calibration result.
  • a checking unit configured to check the data before the determining unit performs error compensation on the data according to the first calibration result.
  • the verification includes one or more of rationality verification and cross verification.
  • the determining unit 910 is specifically configured to: perform optimal estimation according to the first pose information and the data, to obtain the second pose information.
  • the embodiment of the present application also provides a device, the device includes a processing unit and a storage unit, wherein the storage unit is used to store instructions, and the processing unit executes the instructions stored in the storage unit, so that the device executes the data processing method in the above embodiment .
  • Fig. 10 shows a schematic block diagram of a data processing device 1000 provided by an embodiment of the present application.
  • the device 1000 includes a memory 1001 for storing computer instructions; a processor 1002 for executing the computer instructions stored in the memory, so that The device 1000 executes the above data processing method.
  • the embodiment of the present application also provides a vehicle, which may include the above-mentioned device 900 or device 1000 .
  • the embodiment of the present application also provides a computer program product, the computer program product including: computer program code, when the computer program code is run on the computer, the computer is made to execute the above method.
  • the embodiment of the present application also provides a computer-readable medium, the computer-readable medium stores program codes, and when the computer program codes are run on a computer, the computer is made to execute the above method.
  • each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software.
  • the methods disclosed in the embodiments of the present application may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.
  • the software module can be located in a mature storage medium in the field such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, register.
  • the storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps of the above method in combination with its hardware. To avoid repetition, no detailed description is given here.
  • the processor may be a central processing unit (central processing unit, CPU), and the processor may also be other general-purpose processors, digital signal processors (digital signal processor, DSP), dedicated integrated Circuit (application specific integrated circuit, 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 may include a read-only memory and a random access memory, and provide instructions and data to the processor.
  • sequence numbers of the above-mentioned processes do not mean the order of execution, and the execution order of the processes should be determined by their functions and internal logic, and should not be used in the embodiments of the present application.
  • the implementation process constitutes any limitation.
  • the disclosed systems, devices and methods may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of the units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components can be combined or May be integrated into another system, or some features may be ignored, or not implemented.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit.
  • the functions are realized in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.
  • the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) 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 (read-only memory, ROM), random access memory (random access memory, RAM), magnetic disk or optical disc and other media that can store program codes. .

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Abstract

一种数据处理方法和装置，该方法包括：根据车辆（100）在第一时刻的位姿信息和第一模型，确定该车辆（100）在第二时刻的第一位姿信息，该第一模型为从该第一时刻到该第二时刻的位姿估计模型（S801）；获取一个或者多个传感器采集的数据（S802）；根据该第一位姿信息以及该数据，确定该车辆（100）在该第二时刻的第二位姿信息（S803）。实施例可以应用于智能驾驶领域，有助于避免因单一传感器发生故障而造成里程计精度下降，从而有助于提升车辆（100）在导航过程中的稳定性和可靠性。

Description

一种数据处理方法和装置
本申请要求于2021年10月29日提交中国专利局、申请号为202111276896.2、申请名称为“一种数据处理方法和装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及智能驾驶领域，并且更具体地，涉及一种数据处理方法和装置。
背景技术
里程计作为自动驾驶或者辅助驾驶最基础的业务模块，可以为规控、感知、融合、预测等模块提供相对定位信息。不同于全局定位的绝对精度要求，自动驾驶中对里程计的要求是：开机即工作、连续平滑、稳定可靠且能保证一定的相对定位精度。
随着自动驾驶或者辅助驾驶场景复杂度的提升，对里程计的精度和可靠性的要求越来越高。目前常用的里程计存在很多局限性，如在车辆行驶过程中通常以惯性测量单元(inertial measurement unit，IMU)输出的信息(例如，角速度和加速度信息)作为预测。当IMU出现发生阻塞或者故障时，里程计精度下降严重甚至无法正常工作，进而影响车辆的正常运行，可能带来严重的安全隐患。
发明内容
本申请提供一种数据处理方法和装置，有助于避免因单一传感器发生故障而造成里程计精度下降，从而有助于提升车辆在导航过程中的稳定性和可靠性。
第一方面，提供了一种数据处理方法，该方法应用于车辆，该车辆包括一个或者多个传感器，该方法包括：根据该车辆在第一时刻的位姿信息和第一模型，确定该车辆在第二时刻的第一位姿信息，该第一模型为从该第一时刻到该第二时刻的位姿估计模型；获取该一个或者多个传感器采集的数据；根据该第一位姿信息以及该数据，确定该车辆在该第二时刻的第二位姿信息。
本申请实施例中，通过上一时刻的位姿信息以及第一模型，可以估计出当前时刻的第一位姿信息，进而通过估计得到的当前时刻的第一位姿信息以及传感器的数据，得到当前时刻的第二位姿信息。无需使用单一传感器(例如，IMU)输出的数据对当前时刻的位姿信息进行估计，避免因为传感器发生故障而造成里程计精度下降，从而有助于提升车辆在导航过程中的稳定性和可靠性。
应理解，上述车辆可以包括一种或多种不同类型的交通工具，也可以包括一种或多种不同类型的在陆地(例如，公路，道路，铁路等)，水面(例如：水路，江河，海洋等)或者空间上操作或移动的运输工具或者可移动物体。例如，车辆可以包括汽车，自行车，摩托车，火车，地铁，飞机，船，飞行器，机器人，无人机或其它类型的运输工具或可移 动物体等，本申请实施例对此不作限定。
在一些可能的实现方式中，该第一模型可以保存在云端服务器中，车辆可以将第一时刻的位姿信息发送给云端服务器，由云端服务器根据第一时刻的位姿信息以及该第一模型来确定车辆在第二时刻的第一位姿信息。云端服务器可以将第一位姿信息发送给车辆，从而车辆可以根据传感器采集的数据以及该第一位姿信息，确定该车辆在该第二时刻的第二位姿信息。
结合第一方面，在第一方面的某些实现方式中，该根据该车辆在第一时刻的位姿信息和第一模型，确定该车辆在第二时刻的第一位姿信息，包括：根据该车辆在该第一时刻的位姿信息和该第一模型，确定该车辆在该第一时刻的初始状态转移矩阵；根据该初始状态转移矩阵，确定该第一位姿信息。
本申请实施例中，车辆可以根据第一时刻的位姿信息以及第一模型，先确定初始状态转移矩阵，进而通过初始状态转移矩阵来确定第二时刻的第一位姿信息。无需使用单一传感器(例如，IMU)输出的数据对当前时刻的位姿信息进行估计，避免因为传感器发生故障而造成里程计精度下降，从而有助于提升车辆在导航过程中的稳定性和可靠性。
在一些可能的实现方式中，该第一模型包括从第一时刻到第二时刻的位置估计模型，从第一时刻到第二时刻的速度估计模型、从第一时刻到第二时刻的加速度估计模型、从第一时刻到第二时刻的横滚角估计模型、从第一时刻到第二时刻的俯仰角估计模型、从第一时刻到第二时刻的航向角估计模型和从第一时刻到第二时刻的角速度估计模型中的一个或者多个。
本申请实施例中，第一模型中可以包括位置、速度、加速度、横滚角、俯仰角、航向角以及角速度估计模型中的一个或者多个，通过这些模型可以对当前时刻车辆的位姿信息进行预测，从而无需通过单一传感器输出的数据对当前时刻的位姿信息进行预测，避免因为传感器发生故障而造成里程计精度下降，从而有助于提升车辆在导航过程中的稳定性和可靠性。
在一些可能的实现方式中，该第一模型由如下公式中的一个或者多个确定：
Figure PCTCN2022113271-appb-000001
Figure PCTCN2022113271-appb-000002
Figure PCTCN2022113271-appb-000003
Figure PCTCN2022113271-appb-000004
Figure PCTCN2022113271-appb-000005
Figure PCTCN2022113271-appb-000006
Figure PCTCN2022113271-appb-000007
其中，
Figure PCTCN2022113271-appb-000008
为里程计坐标系下该车辆在该第二时刻的位置，
Figure PCTCN2022113271-appb-000009
为该里程计坐标系下该车辆在该第一时刻的位置，
Figure PCTCN2022113271-appb-000010
为从车辆坐标系到该里程计坐标系的旋转矩阵，
Figure PCTCN2022113271-appb-000011
为该车辆坐标系下该车辆在该第一时刻的速度，T为该第一时刻和该第二时刻之间的时间差，
Figure PCTCN2022113271-appb-000012
为该车辆坐标系下该车辆在该第一时刻的加速度，
Figure PCTCN2022113271-appb-000013
为该车辆坐标系下该车辆在该第二时刻的加速度，
Figure PCTCN2022113271-appb-000014
为该车辆坐标系下该车辆在该第二时刻的速度，φ k为该车辆在该第二时 刻的横滚角，φ k-1为该车辆在该第一时刻的横滚角，θ k-1为该车辆在k-1时刻的俯仰角，
Figure PCTCN2022113271-appb-000015
为该车辆在该第一时刻的横滚角速率，
Figure PCTCN2022113271-appb-000016
为该车辆在该第一时刻的俯仰角速率，
Figure PCTCN2022113271-appb-000017
为该车辆在该第一时刻的航向角速率，θ k为该车辆在该第二时刻的俯仰角，θ k-1为该车辆在该第一时刻的俯仰角，
Figure PCTCN2022113271-appb-000018
为该车辆在该第二时刻的航向角，
Figure PCTCN2022113271-appb-000019
为该车辆在该第一时刻的航向角，
Figure PCTCN2022113271-appb-000020
为该车辆坐标系下车辆在该第二时刻的角速度，
Figure PCTCN2022113271-appb-000021
为该车辆坐标系下该车辆在该第一时刻的角速度。
结合第一方面，在第一方面的某些实现方式中，该方法还包括：根据该车辆在该第一时刻的协方差信息和该第一模型，确定该车辆在该第二时刻的第一协方差信息；根据该第一协方差信息以及该数据，确定该车辆在该第二时刻的第二协方差信息。
本申请实施例中，通过上一时刻的协方差信息以及第一模型，可以估计出当前时刻的第一协方差信息，进而通过估计得到的当前时刻的第一协方差信息以及传感器的数据，得到当前时刻的第二协方差信息。无需使用单一传感器(例如，IMU)输出的数据对当前时刻的协方差信息进行估计，避免因为传感器发生故障而造成里程计精度下降，从而有助于提升车辆在导航过程中的稳定性和可靠性。
结合第一方面，在第一方面的某些实现方式中，该根据该第一位姿信息以及该数据，确定该车辆在该第二时刻的第二位姿信息之前，该方法还包括：获取第一标定结果，该第一标定结果包括在线标定结果和/或离线标定结果；其中，该根据该第一位姿信息以及该数据，确定该车辆在该第二时刻的第二位姿信息，包括：根据该第一标定结果，对该数据进行误差补偿，得到误差补偿后的数据；根据该第一位姿信息以及该误差补偿后的数据，确定该第二位姿信息。
本申请实施例中，通过在线或者离线标定的方式对传感器进行误差补偿，有助于进一步提升里程计的精度。
在一些可能的实现方式中，车辆可以对同一参数的在线标定结果和离线标定结果进行交叉校验，从而保证标定参数的准确性。
结合第一方面，在第一方面的某些实现方式中，该第一标定结果包括轮速刻度系数、惯性测量单元IMU零偏、杆臂参数中的一个或者多个。
结合第一方面，在第一方面的某些实现方式中，该根据该第一标定结果，对该数据进行误差补偿之前，该方法还包括：对该数据进行校验。
在一些可能的实现方式中，校验方式包括但不限于合理性校验、交叉校验等。
结合第一方面，在第一方面的某些实现方式中，该根据该第一位姿信息以及该数据，确定该车辆在该第二时刻的第二位姿信息，包括：根据该第一位姿信息以及该数据进行最优估计，得到该第二位姿信息。
在一些可能的实现方式中，根据该第一位姿信息以及该数据进行最优估计，包括：根据该第一位姿信息以及该数据，基于卡尔曼滤波进行最优估计；或者，根据该第一位姿信息以及该数据，基于非卡尔曼滤波进行最优估计。
本申请实施例中，基于车辆上一时刻的位姿信息和第一模型确定的当前时刻的第一位姿信息作为预测值，传感器输出的数据(或者，对传感器输出的数据进行处理后的数据)作为观测值，基于卡尔曼滤波进行最优估计可以得到车辆在当前时刻的第二位姿信息，有 助于避免对单一传感器的依赖，解决了因为IMU发生故障而造成里程计精度下降严重甚至无法正常工作的问题，从而提高了车辆的稳定性和可靠性。
第二方面，提供了一种数据处理装置，该装置包括：确定单元，用于根据车辆在第一时刻的位姿信息和第一模型，确定该车辆在第二时刻的第一位姿信息，该第一模型为从该第一时刻到该第二时刻的位姿估计模型；获取单元，用于获取该一个或者多个传感器采集的数据；该确定单元，还用于根据该第一位姿信息以及该数据，确定该车辆在该第二时刻的第二位姿信息。
结合第二方面，在第二方面的某些实现方式中，该确定单元具体用于：根据该车辆在该第一时刻的位姿信息和该第一模型，确定该车辆在该第一时刻的初始状态转移矩阵；根据该初始状态转移矩阵，确定该第一位姿信息。
在一些可能的实现方式中，该第一模型包括从第一时刻到第二时刻的位置估计模型，从第一时刻到第二时刻的速度估计模型、从第一时刻到第二时刻的加速度估计模型、从第一时刻到第二时刻的横滚角估计模型、从第一时刻到第二时刻的俯仰角估计模型、从第一时刻到第二时刻的航向角估计模型和从第一时刻到第二时刻的角速度估计模型中的一个或者多个。
在一些可能的实现方式中，该第一模型由如下公式中的一个或者多个确定：
Figure PCTCN2022113271-appb-000022
Figure PCTCN2022113271-appb-000023
Figure PCTCN2022113271-appb-000024
Figure PCTCN2022113271-appb-000025
Figure PCTCN2022113271-appb-000026
Figure PCTCN2022113271-appb-000027
Figure PCTCN2022113271-appb-000028
其中，
Figure PCTCN2022113271-appb-000029
为里程计坐标系下该车辆在该第二时刻的位置，
Figure PCTCN2022113271-appb-000030
为该里程计坐标系下该车辆在该第一时刻的位置，
Figure PCTCN2022113271-appb-000031
为从车辆坐标系到该里程计坐标系的旋转矩阵，
Figure PCTCN2022113271-appb-000032
为该车辆坐标系下该车辆在该第一时刻的速度，T为该第一时刻和该第二时刻之间的时间差，
Figure PCTCN2022113271-appb-000033
为该车辆坐标系下该车辆在该第一时刻的加速度，
Figure PCTCN2022113271-appb-000034
为该车辆坐标系下该车辆在该第二时刻的加速度，
Figure PCTCN2022113271-appb-000035
为该车辆坐标系下该车辆在该第二时刻的速度，φ k为该车辆在该第二时刻的横滚角，φ k-1为该车辆在该第一时刻的横滚角，θ k-1为该车辆在k-1时刻的俯仰角，
Figure PCTCN2022113271-appb-000036
为该车辆在该第一时刻的横滚角速率，
Figure PCTCN2022113271-appb-000037
为该车辆在该第一时刻的俯仰角速率，
Figure PCTCN2022113271-appb-000038
为该车辆在该第一时刻的航向角速率，θ k为该车辆在该第二时刻的俯仰角，θ k-1为该车辆在该第一时刻的俯仰角，
Figure PCTCN2022113271-appb-000039
为该车辆在该第二时刻的航向角，
Figure PCTCN2022113271-appb-000040
为该车辆在该第一时刻的航向角，
Figure PCTCN2022113271-appb-000041
为该车辆坐标系下车辆在该第二时刻的角速度，
Figure PCTCN2022113271-appb-000042
为该车辆坐标系下该车辆在该第一时刻的角速度。
结合第二方面，在第二方面的某些实现方式中，该确定单元，还用于根据该车辆在该第一时刻的协方差信息和该第一模型，确定该车辆在该第二时刻的第一协方差信息；根据 该第一协方差信息以及该数据，确定该车辆在该第二时刻的第二协方差信息。
结合第二方面，在第二方面的某些实现方式中，该获取单元，还用于在该确定单元根据该第一位姿信息以及该数据，确定该车辆在该第二时刻的第二位姿信息之前，获取第一标定结果，该第一标定结果包括在线标定结果和/或离线标定结果；该确定单元具体用于：根据该第一标定结果，对该数据进行误差补偿，得到误差补偿后的数据；根据该第一位姿信息以及该误差补偿后的数据，确定该第二位姿信息。
结合第二方面，在第二方面的某些实现方式中，该第一标定结果包括轮速刻度系数、惯性测量单元IMU零偏、杆臂参数中的一个或者多个。
结合第二方面，在第二方面的某些实现方式中，该装置还包括：校验单元，用于在该确定单元根据该第一标定结果，对该数据进行误差补偿之前，对该数据进行校验。
结合第二方面，在第二方面的某些实现方式中，该确定单元具体用于：根据该第一位姿信息以及该数据，基于卡尔曼滤波进行最优估计，得到该第二位姿信息。
第三方面，提供了一种数据处理装置，该装置包括：存储器，用于存储计算机指令；处理器，用于执行该存储器中存储的计算机指令，以使得该装置执行上述第一方面中的方法。
第四方面，提供了一种车辆，该车辆包括上述第二方面或者第三方面中任一项所述的装置。
第五方面，提供了一种计算机程序产品，所述计算机程序产品包括：计算机程序代码，当所述计算机程序代码在计算机上运行时，使得计算机执行上述第一方面中的方法。
需要说明的是，上述计算机程序代码可以全部或者部分存储在第一存储介质上，其中第一存储介质可以与处理器封装在一起的，也可以与处理器单独封装，本申请实施例对此不作具体限定。
第六方面，提供了一种计算机可读介质，所述计算机可读介质存储有程序代码，当所述计算机程序代码在计算机上运行时，使得计算机执行上述第一方面中的方法。
第七方面，本申请实施例提供了一种芯片系统，该芯片系统包括处理器，用于调用存储器中存储的计算机程序或计算机指令，以使得该处理器执行上述任一方面所述的方法。
结合第七方面，在一种可能的实现方式中，该处理器通过接口与存储器耦合。
结合第七方面，在一种可能的实现方式中，该芯片系统还包括存储器，该存储器中存储有计算机程序或计算机指令。
附图说明
图1是本申请实施例提供的车辆的示意性框图。
图2是本申请实施例提供的应用场景的示意图。
图3是本申请实施例提供的系统应用框图。
图4是本申请实施例中对各个传感器的数据进行处理的过程示意图。
图5是本申请实施例中里程计融合模块的数据处理过程的示意图。
图6是本申请实施例提供的数据处理方法的示意性流程图。
图7是通过本申请实施例提供的方式测试得到行驶轨迹的示意图。
图8是本申请实施例提供的数据处理方法的另一示意性流程图。
图9是本申请实施例提供的数据处理装置的示意性框图。
图10是本申请实施例提供的数据处理装置的另一示意性框图。
具体实施方式
下面将结合附图，对本申请中的技术方案进行描述。
图1是本申请实施例提供的车辆100的示意性框图。车辆100可以包括感知系统120和计算平台150。
感知系统120可包括感测关于车辆100周边的环境的信息的若干种传感器。例如，感知系统120可包括定位系统(定位系统可以是全球定位系统(global positioning system，GPS)系统，也可以是北斗系统或者其他定位系统)、IMU)、激光雷达、毫米波雷达、超声雷达、摄像装置、轮速传感器(wheel speed sensor，WSS)。其中，视觉里程计(visual odometry，VO)可以采用摄像装置输出的图像数据对车辆的位姿进行估计，激光雷达里程计(LiDAR odometry，LO)可以采用激光雷达输出的点云数据对车辆的位姿进行估计。感知系统120还可包括被监视车辆100的内部系统的传感器(例如，车内空气质量监测器、燃油量表、机油温度表等)。来自这些传感器中的一个或多个的传感器数据可用于检测对象及其相应特性(位置、形状、方向、速度等)。这种检测和识别是车辆100的安全操作的关键功能。
车辆100的部分或所有功能受计算平台150控制。计算平台150可包括至少一个处理器151，处理器151可以执行存储在例如存储器152这样的非暂态计算机可读介质中的指令153。
在一些实施例中，计算平台150还可以是采用分布式方式控制车辆100的个体组件或子系统的多个计算设备。处理器151可以是任何常规的处理器，诸如中央处理单元(central processing unit，CPU)。替选地，处理器151还可以包括诸如图像处理器(graphic process unit，GPU)、现场可编程门阵列(field programmable gate array，FPGA)、片上系统(system on chip，SOC)、专用集成芯片(application specific integrated circuit，ASIC)或它们的组合。
除了指令153以外，存储器152还可存储数据，例如道路地图、路线信息，车辆的位置、方向、速度以及其它这样的车辆数据，以及其他信息。这种信息可在车辆100在自主、半自主和/或手动模式中操作期间被车辆100和计算平台150使用。
应理解，图1中车辆的结构不应理解为对本申请实施例的限制。
还应理解，上述车辆100可以包括一种或多种不同类型的交通工具，也可以包括一种或多种不同类型的在陆地(例如，公路，道路，铁路等)，水面(例如：水路，江河，海洋等)或者空间上操作或移动的运输工具或者可移动物体。例如，车辆可以包括汽车，自行车，摩托车，火车，地铁，飞机，船，飞行器，机器人，无人机或其它类型的运输工具或可移动物体等，本申请实施例对此不作限定。
如前所述，随着自动驾驶或者辅助驾驶场景复杂度的提升，对里程计的精度和可靠性的要求越来越高。目前常用的里程计存在很多局限性，如在车辆行驶过程中通常以IMU输出的信息(例如，角速度和加速度信息)作为预测值。当IMU出现发生阻塞或者故障时，里程计精度下降严重甚至无法正常工作，进而影响车辆的正常运行，可能带来严重的 安全隐患。
本申请实施例提供了一种数据处理方法和装置，有助于避免因单一传感器发生故障而造成里程计精度下降，从而有助于提升车辆在导航过程中的稳定性和可靠性。具体地，车辆可以以上一时刻的运动状态作为预测值，以多个传感器输出的信息作为观测值，从而对车辆的位姿进行最优估计，得到车辆当前时刻的位姿信息。车辆可以根据当前时刻的位姿信息，得到车辆的运动信息或者定位信息。
在介绍本申请实施例之前，首先对本申请实施例中的术语进行解释。
预测值：根据上一时刻的运动状态预测得到的当前时刻的运动状态。
观测值：当前时刻传感器测量得到的数据。
其中，预测值和观测值可以用于最优估计，从而得到当前时刻更准确的一个运动状态。
图2示出了本申请实施例提供的应用场景的示意图。在该应用场景中，可以包括车辆100和云端服务系统200。其中，车辆100和云端服务系统200可以通过网络通信。车辆100可以将上一时刻的运动状态(例如，车辆在上一时刻的位置、速度、加速度、航向角、横滚角等)以及当前时刻的一个或者多个传感器输出的信息(例如，摄像头在当前时刻采集的图像数据以及激光雷达在当前时刻采集的点云数据)发送给云端服务器200，该云端服务器200可以以根据上一时刻的运动状态预测得到的当前时刻的运动状态作为预测值，该预测值可以用于指示车辆在当前时刻的第一位姿信息和第一协方差信息；该云端服务器200可以以一个或者多个传感器输出的信息作为观测值，该观测值可以用于指示车辆中的传感器对当前时刻车辆周边环境的感知结果和测量结果。车辆可以根据上述预测值和观测值进行最优估计，得到车辆100当前时刻的第二位姿信息和第二协方差信息。云端服务器200可以将当前时刻的第二位姿信息和第二协方差信息发送给车辆100，从而车辆100可以根据当前时刻的第二位姿信息和第二协方差信息，得到车辆100的运动信息或者定位信息。
或者，车辆100可以将上一时刻的位姿信息发送给云端服务器，由云端服务器根据该上一时刻的位姿信息以及该第一模型来确定车辆100在当前时刻的第一位姿信息。云端服务器200可以将第一位姿信息发送给车辆100，从而车辆100可以根据一个或者多个传感器输出的信息以及该第一位姿信息，确定该车辆100在当前时刻的第二位姿信息。
图3示出了本申请实施例提供的系统应用框图。以智能驾驶场景为例，里程计融合模块的输入包括但不限于IMU、WSS、VO以及LO等信息。上述信息经过里程计融合模块处理后可以输出车辆当前时刻的位姿以及协方差信息。里程计融合模块输出的信息一方面可以为激光定位、视觉定位提供车辆相对运动信息，另一方面也可以为感知融合、规控、预测等模块提供连续平滑的相对定位信息。
图4示出了本申请实施例中对各个传感器的数据进行处理的过程。如图4所示，IMU和WSS输出的数据经过误差补偿可以分别得到误差补偿后的IMU的数据和误差补偿后的WSS数据。该误差补偿后的IMU数据和误差补偿后的WSS数据可以作为里程计融合模块的输入；摄像装置和激光雷达输出的和数据分别经过视觉里程计和激光里程计的位姿估计，可以得到预处理后的视觉里程计信息VO和激光里程计信息LO，经过预处理的信息可以作为里程计融合模块的输入。应理解，里程计融合模块的输入可以是上述信息中的一种或者多种。里程计融合模块可以根据输入的信息，输出当前时刻的位姿以及协方差信息。
图5示出了本申请实施例中里程计融合模块的数据处理过程的示意图。经过误差补偿后的IMU数据、误差补偿后的WSS数据以及经过预处理后的LO和VO的数据可以作为卡尔曼滤波的输入。其中IMU数据包括车辆的角速度和加速度信息，WSS数据包括车辆速度信息，R代表旋转矩阵，t代表平移向量。
应理解，图5所示的数据处理过程是以采用卡尔曼滤波(Kalman filtering，KF)框架为例进行说明的，本申请实施例并不限于此。例如，该数据处理过程也可以采用非卡尔曼滤波框架。
里程计融合模块可以利用车辆上一时刻运动状态(例如，上一时刻的位姿以及协方差信息)作为预测值，经过误差补偿或者预处理后的传感器数据作为观测值，基于卡尔曼滤波进行最优估计，并输出当前时刻的位姿以及协方差信息。
图6示出了本申请实施例提供的数据处理方法600的示意性流程图。如图6所示，该方法600包括：
S601，数据处理模块获取一个或者多个传感器采集的数据。
一个实施例中，该数据处理模块可以是位于车辆中的，也可以是位于云端服务器中的，本申请实施例对此不作限定。
示例性的，该一个或者多个传感器可以是IMU、WSS、摄像装置或者激光雷达中的一种或者多种。
应理解，下文是以获取IMU和WSS的数据为例进行说明的，IMU数据可以包括角速度和加速度等信息，WSS数据可以包括车辆四个车轮的轮速、方向盘转角等信息。
一个实施例中，数据处理模块可以创建3个或者更多独立线程，包括IMU线程、WSS线程和定时器线程。IMU线程中进行IMU数据接收、校验等操作；WSS线程中进行WSS数据接收、校验等操作；定时器线程中构建系统状态方程，并基于上一时刻车辆的运动状态进行预测更新。
S602，数据处理模块对一个或者多个传感器的数据进行校验。
一个实施例中，校验的方式包括但不限于直接合理性校验、交叉校验等。
示例性的，直接合理性校验的过程包括：数据处理模块判断IMU数据中的角速度是否大于或者等于角速度阈值。如果IMU数据中的角速度小于该角速度阈值，则校验正常；否则校验失败。
示例性的，数据处理模块判断IMU数据中的加速度是否大于或者等于加速度阈值。如果IMU数据中的加速度小于该加速度阈值，则校验正常；否则校验失败。
示例性的，数据处理模块判断WSS数据中的四个车轮的轮速是否大于或者等于速度阈值。如果四个车轮的轮速都小于该速度阈值，则校验正常；如果四个车轮中的至少一个车轮的轮速大于或者等于该速度阈值，则校验失败。
应理解，数据处理模块可以根据角速度信息、加速度信息或者四个车轮的轮速中的一种或者多种进行直接合理性校验。
示例性的，交叉校验方法如下：
(a)数据处理模块可以根据如下公式(1)以及轮速数据计算横摆角速度w odom：
Figure PCTCN2022113271-appb-000043
其中，V rr为车辆右后轮速度，V rl为车辆左后轮速度，r为车辆后轮之间的距离，fabs表示取绝对值。
(b)获取IMU输出的垂直角速度w imu。
(c)判断w odom和w imu的差值是否大于或者等于预设差值。
示例性的，如果w odom和w imu的差值小于第一预设差值，则校验正常；否则认为车辆发生打滑，校验失败。
S603，数据处理模块获取在线或者离线标定结果。
一个实施例中，数据处理模块通过在线实时估计方式获取传感器在线标定结果，或者，数据处理模块通过读取离线标定参数的方式获取传感器离线标定结果。
一个实施例中，标定结果包括但不限于轮速刻度系数、IMU陀螺和加表零偏、各传感器之间的杆臂参数等。
示例性的，离线标定结果可以包括轮速刻度系数和杆臂参数等，可以通过读取文件或者加载参数方法获取。
示例性的，在线标定结果包括IMU陀螺和加表零偏、轮速刻度系数等，主要方法是将待估计参数如IMU陀螺和加表零偏、轮速刻度系数等扩充到状态变量中，通过卡尔曼滤波器实现在线估计。
一个实施例中，数据处理模块可以同时获取在线标定结果和离线标定结果，从而对在线标定结果和离线标定结果进行交叉校验。例如，数据处理模块在获取同一参数的在线标定结果和离线标定结果之后，可以判断二者的差值。如果该差值小于第二预设差值，则认为标定结果正常；否则认为标定结果异常。
S604，数据处理模块根据在线标定结果或者离线标定结果，对通过校验的传感器数据进行误差补偿。
一个实施例中，补偿后的数据可以作为里程计融合模块(或者，卡尔曼滤波器)的输入，或者，补偿后的数据可以作为里程计融合模块中的观测值。
示例性的，IMU数据的误差补偿项包括陀螺和加表零偏，在IMU数据基础上，扣除标定得到的陀螺和加表零偏，补偿后的数据作为卡尔曼滤波器的输入。
示例性的，WSS数据包括四轮轮速、方向盘转角等信息，WSS数据的误差补偿主要采用后轮轮速数据、后轮轮速的均值，扣除刻度系数的影响，补偿后的数据作为卡尔曼滤波器的输入。
S605，里程计融合模块建立基于车辆的运动学模型。
一个实施例中，里程计融合模块建立的车辆的运动学模型可以如以下公式(2)-(8)：
Figure PCTCN2022113271-appb-000044
其中，
Figure PCTCN2022113271-appb-000045
为里程计坐标系下车辆在k时刻的位置，
Figure PCTCN2022113271-appb-000046
为里程计坐标系下车辆在k-1时刻的位置，
Figure PCTCN2022113271-appb-000047
为从车辆坐标系到里程计坐标系的旋转矩阵，
Figure PCTCN2022113271-appb-000048
为车辆坐标系下车辆在k-1时刻的速度，T为时间差且与定时器的周期相关，
Figure PCTCN2022113271-appb-000049
为车辆坐标系下车辆在k-1时刻的加速度。
Figure PCTCN2022113271-appb-000050
其中，
Figure PCTCN2022113271-appb-000051
为车辆坐标系下车辆在k时刻的速度。
Figure PCTCN2022113271-appb-000052
其中，
Figure PCTCN2022113271-appb-000053
为车辆坐标系下车辆在k时刻的加速度。
Figure PCTCN2022113271-appb-000054
其中，φ k为车辆在k时刻的横滚角，φ k-1为车辆在k-1时刻的横滚角，θ k-1为车辆在k-1时刻的俯仰角，
Figure PCTCN2022113271-appb-000055
为车辆在k-1时刻的横滚角速率，
Figure PCTCN2022113271-appb-000056
为车辆在k-1时刻的俯仰角速率，
Figure PCTCN2022113271-appb-000057
为车辆在k-1时刻的航向角速率。
Figure PCTCN2022113271-appb-000058
其中，θ k为车辆在k时刻的俯仰角。
Figure PCTCN2022113271-appb-000059
其中，
Figure PCTCN2022113271-appb-000060
为车辆在k时刻的航向角，
Figure PCTCN2022113271-appb-000061
为车辆在k-1时刻的航向角。
Figure PCTCN2022113271-appb-000062
其中，
Figure PCTCN2022113271-appb-000063
为车辆坐标系下车辆在k时刻的角速度，
Figure PCTCN2022113271-appb-000064
为车辆坐标系下车辆在k-1时刻的角速度。
里程计融合模块可以根据上述公式(2)-(8)中的一个或者多个得到初始状态转移矩阵F。
应理解，以上公式(2)-(8)仅仅是示意性的，本申请实施例中还可以通过其他公式、方程或者函数来估计当前时刻的位置、速度、加速度、横滚角、俯仰角、航向角以及角速度等。
S606，里程计融合模块在S605中运动学模型的基础上，基于上一时刻的运动状态进行预测，得到当前时刻的运动状态。
一个实施例中，该上一时刻(例如，k-1时刻)的运动状态可以是上一时刻卡尔曼滤波器输出的位姿以及协方差信息。
示例性的，里程计融合模块可以根据如下公式(9)对当前时刻的位姿信息进行预测：
X(k)＝F[k-1,X(k-1)]+W(k-1)          (9)
其中，X(k)为里程计融合模块预测的车辆在k时刻的位姿信息，F[k-1,X(k-1)]为k-1时刻的状态转移矩阵，W(k-1)为车辆在k-1时刻的系统噪声。
示例性的，里程计融合模块可以根据如下公式(10)对当前时刻的协方差矩阵信息进行预测：
P(k|k-1)＝ψ(k)P(k-1|k-1)ψ T(k)+Q          (10)
其中，P(k|k-1)为里程计融合模块预测的车辆在k时刻的协方差矩阵信息，在初始状态转移矩阵F的基础上，考虑到其非线性特性，对初始状态转移矩阵F进行一阶线性化，得到状态转移矩阵ψ。P(k-1|k-1)为车辆在k-1时刻的协方差矩阵信息，Q为系统噪声的均方差。
S607，里程计融合模块将S604中误差补偿后的数据作为卡尔曼滤波器的观测值。
示例性的，里程计融合模块可以根据如下公式(11)作为观测方程：
Z(k)＝H[k,X(k)]+V(k)          (11)
其中，Z(k)为观测值，H[k,X(k)]为k时刻的观测矩阵，V(k)为k时刻的观测噪声。
S608，里程计融合模块根据S606预测得到的当前时刻的运动状态以及S607中的观测值，基于卡尔曼滤波进行最优估计，从而得到当前时刻车辆的位姿和协方差信息。
示例性的，卡尔曼滤波增益的计算方式可以如公式(12)：
K(k)＝P(k|k-1)H T(k)(H(k)P(k|k-1)H T(k)+R(k)      (12)
其中，K(k)为卡尔曼滤波增益，R(k)为观测噪声的均方差。
示例性的，里程计融合模块可以根据如下公式(13)和(14)得到更新后的位姿信息以及协方差信息。
X(k)＝X(k|k-1)+K(Z(k)-H(k)X(k|k-1))        (13)
其中，X(k)为卡尔曼滤波器输出的位姿信息，X(k|k-1)为状态变量一步预测值。
P(k)＝(I-K(k)H(k)P(k|k-1)(I-K(k)H(k)) T+K(k)R(k)K(k) T    (14)
其中，P(k)为卡尔曼滤波器输出的协方差矩阵信息，I为单位矩阵。
本申请实施例中还根据上述提供的数据处理方法进行了测试，在测试中使用了车辆采集的IMU数据，轮速数据以及组合导航数据。整个车辆行驶过程中，实时动态(real-time kinematic，RTK)为固定解状态，以组合导航数据经过后处理得到的数据作为真实数据(ground truth)，行驶轨迹如图7所示。为了验证本申请实施例所提供的数据处理方法在IMU失效或者故障状态下的性能，模拟3段IMU异常的情况，每段60s。如图7中虚线框所示。其中，实线表示真实数据，点线表示里程计输出结果，虚线框内的点线为IMU失效时里程计输出结果。
根据上述轨迹可以看出，当IMU出现异常或者故障时，里程计工作正常，没有明显误差增大的情况。为了验证里程计的精度，截取上述部分行驶数据，车辆每行驶200m计算一次当前里程计与真实数据的差值，统计结果如下表1所示。
表1里程计误差统计表
行驶距离(m) 误差(m) 误差(％)
200.407 0.61 0.308
200.249 0.74 0.371
200.311 0.59 0.297
200.508 0.86 0.430
图8示出了本申请实施例提供的数据处理方法800的示意性流程图。该方法应用于车辆，该车辆包括一个或者多个传感器。如图8所示，该方法800包括：
S801，车辆根据该车辆在第一时刻的位姿信息和第一模型，确定该车辆在第二时刻的第一位姿信息，该第一模型为从该第一时刻到该第二时刻的位姿估计模型。
可选地，该第一模型包括从第一时刻到第二时刻的位置估计模型，从第一时刻到第二时刻的速度估计模型、从第一时刻到第二时刻的加速度估计模型、从第一时刻到第二时刻的横滚角估计模型、从第一时刻到第二时刻的俯仰角估计模型、从第一时刻到第二时刻的航向角估计模型以及从第一时刻到第二时刻的角速度估计模型中的一个或者多个。
示例性的，从第一时刻到第二时刻的位置估计模型可以如上述公式(2)所示。
示例性的，从第一时刻到第二时刻的速度估计模型可以如上述公式(3)所示。
示例性的，从第一时刻到第二时刻的加速度估计模型可以如上述公式(4)所示。
示例性的，从第一时刻到第二时刻的横滚角估计模型可以如上述公式(5)所示。
示例性的，从第一时刻到第二时刻的俯仰角估计模型可以如上述公式(6)所示。
示例性的，从第一时刻到第二时刻的航向角估计模型可以如上述公式(7)所示。
示例性的，从第一时刻到第二时刻的角速度估计模型可以如上述公式(8)所示。
应理解，以上公式(2)-(8)仅仅是示意性的，对于位置估计模型、速度估计模型、加速度估计模型、横滚角估计模型、俯仰角估计模型、航向角估计模型或者角速度估计模型还可以通过其他公式、方程或者函数进行表示，本申请实施例对此并不作具体限定。
可选地，该车辆根据该车辆在第一时刻的位姿信息和第一模型，确定该车辆在第二时刻的第一位姿信息，包括：该车辆根据该车辆在该第一时刻的位姿信息和该第一模型，确定该车辆在该第一时刻的初始状态转移矩阵；该车辆根据该初始状态转移矩阵，确定该第一位姿信息。
可选地，该车辆根据该初始状态转移矩阵，确定该第一位姿信息，包括：该车辆根据该初始状态转移矩阵以及第一时刻的系统噪声，确定该第一位姿信息。
示例性的，第一位姿信息可以根据上述公式(9)确定。
可选地，该方法还包括：该车辆根据该车辆在该第一时刻的协方差信息和该第一模型，确定该车辆在该第二时刻的第一协方差信息；该车辆根据该第一协方差信息以及该数据，确定该车辆在该第二时刻的第二协方差信息。
可选地，该车辆根据该第一时刻的协方差信息和该第一模型，确定该车辆在该第二时刻的第一协方差信息，包括：对上述第一时刻的初始状态转移矩阵进行线性化，得到状态转移矩阵；根据该状态转移矩阵、该第一时刻的协方差信息以及系统噪声的均方差，确定该第一协方差信息。
示例性的，该第一协方差信息可以根据上述公式(10)确定。
S802，该车辆获取该一个或者多个传感器采集的数据。
可选地，该一个或者多个传感器可以是IMU、WSS、摄像装置或者激光雷达中的一种或者多种。
应理解，上述S801和S802之间并没有实际的先后顺序。
S803，该车辆根据该第一位姿信息以及该数据，确定该车辆在该第二时刻的第二位姿信息。
可选地，该车辆根据该第一位姿信息以及该数据，确定该车辆在该第二时刻的第二位姿信息之前，该方法还包括：该车辆获取第一标定结果，该第一标定结果包括在线标定结果和/或离线标定结果；其中，该车辆根据该第一位姿信息以及该数据，确定该车辆在该第二时刻的第二位姿信息，包括：该车辆根据该第一标定结果，对该数据进行误差补偿，得到误差补偿后的数据；该车辆根据该第一位姿信息以及该误差补偿后的数据，确定该第二位姿信息。
可选地，该第一标定结果包括轮速刻度系数、惯性测量单元IMU零偏、杆臂参数中的一个或者多个。
可选地，该车辆根据该第一标定结果，对该数据进行误差补偿之前，该方法还包括：该车辆对该数据进行校验，该校验包括合理性校验和交叉校验中的一种或者多种。
可选地，该车辆根据该第一位姿信息以及该数据，确定该车辆在该第二时刻的第二位姿信息，包括：该车辆根据该第一位姿信息以及该数据进行最优估计，得到该第二位姿信 息。
可选地，该车辆根据该第一位姿信息以及该数据进行最优估计，包括：该车辆根据该第一位姿信息以及该数据，基于卡尔曼滤波进行最优估计；或者，该车辆根据该第一位姿信息以及该数据，基于非卡尔曼滤波进行最优估计。
图9示出了本申请实施例提供的一种数据处理装置900的示意性框图。如图9所示，该装置900包括：
确定单元910，用于根据车辆在第一时刻的位姿信息和第一模型，确定该车辆在第二时刻的第一位姿信息，该第一模型为从该第一时刻到该第二时刻的位姿估计模型。
获取单元920，用于获取该一个或者多个传感器采集的数据。
确定单元910，还用于根据该第一位姿信息以及该数据，确定该车辆在该第二时刻的第二位姿信息。
可选地，确定单元910具体用于：根据该车辆在该第一时刻的位姿信息和该第一模型，确定该车辆在该第一时刻的初始状态转移矩阵；根据该初始状态转移矩阵，确定该第一位姿信息。
可选地，确定单元910，还用于根据该车辆在该第一时刻的协方差信息和该第一模型，确定该车辆在该第二时刻的第一协方差信息；根据该第一协方差信息以及该数据，确定该车辆在该第二时刻的第二协方差信息。
可选地，该获取单元920，还用于在确定单元910根据该第一位姿信息以及该数据，确定该车辆在该第二时刻的第二位姿信息之前，获取第一标定结果，该第一标定结果包括在线标定结果和/或离线标定结果；
确定单元910具体用于：根据该第一标定结果，对该数据进行误差补偿，得到误差补偿后的数据；根据该第一位姿信息以及该误差补偿后的数据，确定该第二位姿信息。
可选地，该第一标定结果包括轮速刻度系数、惯性测量单元IMU零偏、杆臂参数中的一个或者多个。
可选地，该装置900还包括：校验单元，用于在该确定单元根据该第一标定结果，对该数据进行误差补偿之前，对该数据进行校验。
可选地，该校验包括合理性校验和交叉校验中的一种或者多种。
可选地，该确定单元910具体用于：根据该第一位姿信息以及该数据进行最优估计，得到该第二位姿信息。
本申请实施例还提供了一种装置，该装置包括处理单元和存储单元，其中存储单元用于存储指令，处理单元执行存储单元所存储的指令，以使该装置执行上述实施例中数据处理方法。
图10示出了本申请实施例提供的数据处理装置1000的示意性框图，该装置1000包括存储器1001，用于存储计算机指令；处理器1002，用于执行该存储器中存储的计算机指令，以使得该装置1000执行上述数据处理方法。
本申请实施例还提供了一种车辆，该车辆可以包括上述装置900或者装置1000。
本申请实施例还提供了一种计算机程序产品，所述计算机程序产品包括：计算机程序代码，当所述计算机程序代码在计算机上运行时，使得计算机执行上述方法。
本申请实施例还提供了一种计算机可读介质，所述计算机可读介质存储有程序代码， 当所述计算机程序代码在计算机上运行时，使得计算机执行上述方法。
在实现过程中，上述方法的各步骤可以通过处理器中的硬件的集成逻辑电路或者软件形式的指令完成。结合本申请实施例所公开的方法可以直接体现为硬件处理器执行完成，或者用处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器，处理器读取存储器中的信息，结合其硬件完成上述方法的步骤。为避免重复，这里不再详细描述。
应理解，本申请实施例中，该处理器可以为中央处理单元(central processing unit，CPU)，该处理器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。
还应理解，本申请实施例中，该存储器可以包括只读存储器和随机存取存储器，并向处理器提供指令和数据。
在本申请实施例中，“第一”、“第二”以及各种数字编号仅为描述方便进行的区分，并不用来限制本申请实施例的范围。例如，区分不同的管路、通孔等。
应理解，在本申请的各种实施例中，上述各过程的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统、装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。
所述功能如果以软件功能单元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(read-only memory，ROM)、随机存取存储器(random access memory，RAM)、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以所述权利要求的保护范围为准。

Claims (18)

  1. 一种数据处理方法，其特征在于，所述方法应用于车辆，所述车辆包括一个或者多个传感器，所述方法包括：
    根据所述车辆在第一时刻的位姿信息和第一模型，确定所述车辆在第二时刻的第一位姿信息，所述第一模型为从所述第一时刻到所述第二时刻的位姿估计模型；
    获取所述一个或者多个传感器采集的数据；
    根据所述第一位姿信息以及所述数据，确定所述车辆在所述第二时刻的第二位姿信息。
  2. 根据权利要求1所述的方法，其特征在于，所述根据所述车辆在第一时刻的位姿信息和第一模型，确定所述车辆在第二时刻的第一位姿信息，包括：
    根据所述车辆在所述第一时刻的位姿信息和所述第一模型，确定所述车辆在所述第一时刻的初始状态转移矩阵；
    根据所述初始状态转移矩阵，确定所述第一位姿信息。
  3. 根据权利要求1或2所述的方法，其特征在于，所述方法还包括：
    根据所述车辆在所述第一时刻的协方差信息和所述第一模型，确定所述车辆在所述第二时刻的第一协方差信息；
    根据所述第一协方差信息以及所述数据，确定所述车辆在所述第二时刻的第二协方差信息。
  4. 根据权利要求1至3中任一项所述的方法，其特征在于，所述根据所述第一位姿信息以及所述数据，确定所述车辆在所述第二时刻的第二位姿信息之前，所述方法还包括：
    获取第一标定结果，所述第一标定结果包括在线标定结果和/或离线标定结果；
    其中，所述根据所述第一位姿信息以及所述数据，确定所述车辆在所述第二时刻的第二位姿信息，包括：
    根据所述第一标定结果，对所述数据进行误差补偿，得到误差补偿后的数据；
    根据所述第一位姿信息以及所述误差补偿后的数据，确定所述第二位姿信息。
  5. 根据权利要求4所述的方法，其特征在于，所述第一标定结果包括轮速刻度系数、惯性测量单元IMU零偏、杆臂参数中的一个或者多个。
  6. 根据权利要求4或5所述的方法，其特征在于，所述根据所述第一标定结果，对所述数据进行误差补偿之前，所述方法还包括：
    对所述数据进行校验，所述校验包括合理性校验和交叉校验中的一种或者多种。
  7. 根据权利要求1至6中任一项所述的方法，其特征在于，所述根据所述第一位姿信息以及所述数据，确定所述车辆在所述第二时刻的第二位姿信息，包括：
    根据所述第一位姿信息以及所述数据进行最优估计，得到所述第二位姿信息。
  8. 一种数据处理装置，其特征在于，包括：
    确定单元，用于根据车辆在第一时刻的位姿信息和第一模型，确定所述车辆在第二时刻的第一位姿信息，所述第一模型为从所述第一时刻到所述第二时刻的位姿估计模型；
    获取单元，用于获取一个或者多个传感器采集的数据；
    所述确定单元，还用于根据所述第一位姿信息以及所述数据，确定所述车辆在所述第二时刻的第二位姿信息。
  9. 根据权利要求8所述的装置，其特征在于，所述确定单元具体用于：
    根据所述车辆在所述第一时刻的位姿信息和所述第一模型，确定所述车辆在所述第一时刻的初始状态转移矩阵；
    根据所述初始状态转移矩阵，确定所述第一位姿信息。
  10. 根据权利要求8或9所述的装置，其特征在于，
    所述确定单元，还用于根据所述车辆在所述第一时刻的协方差信息和所述第一模型，确定所述车辆在所述第二时刻的第一协方差信息；
    根据所述第一协方差信息以及所述数据，确定所述车辆在所述第二时刻的第二协方差信息。
  11. 根据权利要求8至10中任一项所述的装置，其特征在于，
    所述获取单元，还用于在所述确定单元根据所述第一位姿信息以及所述数据，确定所述车辆在所述第二时刻的第二位姿信息之前，获取第一标定结果，所述第一标定结果包括在线标定结果和/或离线标定结果；
    所述确定单元具体用于：根据所述第一标定结果，对所述数据进行误差补偿，得到误差补偿后的数据；
    根据所述第一位姿信息以及所述误差补偿后的数据，确定所述第二位姿信息。
  12. 根据权利要求11所述的装置，其特征在于，所述第一标定结果包括轮速刻度系数、IMU零偏、杆臂参数中的一个或者多个。
  13. 根据权利要求11或12所述的装置，其特征在于，所述装置还包括：
    校验单元，用于在所述确定单元根据所述第一标定结果，对所述数据进行误差补偿之前，对所述数据进行校验，所述校验包括合理性校验和交叉校验中的一种或者多种。
  14. 根据权利要求8至13中任一项所述的装置，其特征在于，所述确定单元具体用于：根据所述第一位姿信息以及所述数据进行最优估计，得到所述第二位姿信息。
  15. 一种数据处理装置，其特征在于，包括：
    存储器，用于存储计算机指令；
    处理器，用于执行所述存储器中存储的计算机指令，以使得所述装置执行如权利要求1至7中任一项所述的方法。
  16. 一种车辆，其特征在于，所述车辆包括如权利要求8至15中任一项所述的装置。
  17. 一种计算机可读存储介质，其特征在于，所述计算机可读介质存储有程序代码，当所述程序代码在计算机上运行时，使得计算机执行如权利要求1至7中任意一项所述的方法。
  18. 一种芯片，其特征在于，所述芯片包括处理器与数据接口，所述处理器通过所述数据接口读取存储器上存储的指令，以执行如权利要求1至7中任一项所述的方法。
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