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CN115484543B - Positioning method, vehicle-mounted device, and computer-readable storage medium - Google Patents

Positioning method, vehicle-mounted device, and computer-readable storage medium

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Publication number
CN115484543B
CN115484543B CN202110606780.4A CN202110606780A CN115484543B CN 115484543 B CN115484543 B CN 115484543B CN 202110606780 A CN202110606780 A CN 202110606780A CN 115484543 B CN115484543 B CN 115484543B
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China
Prior art keywords
vehicle
estimated
difference
positioning result
value
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CN202110606780.4A
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Chinese (zh)
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CN115484543A (en
Inventor
杨晓龙
伍勇
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Shenzhen Yinwang Intelligent Technology Co Ltd
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Huawei Technologies Co Ltd
Shenzhen Yinwang Intelligent Technology Co Ltd
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Priority to CN202110606780.4A priority Critical patent/CN115484543B/en
Publication of CN115484543A publication Critical patent/CN115484543A/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • 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
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Human Computer Interaction (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Navigation (AREA)

Abstract

本申请提供了一种定位方法、车载装置及计算机可读存储介质，该方法包括：定位装置对传感器数据进行正反向解算并进行融合滤波后得到第一组合定位结果，该第一组合定位结果指示了车辆在多个时刻下的车辆运动状态，所述运动状态包括车辆位置、车辆速度和车辆姿态；该定位装置根据第一组合定位结果得到车辆的起始状态变量估计值、车辆在多个时刻的位置信息和车辆的运动状态增量的差值；该定位装置根据车辆的起始状态变量估计值、车辆在多个时刻的位置信息和车辆的运动状态增量的差值，对第一组合定位结果进行修正，得到目标组合定位结果。实施本申请，可提高后处理定位精度，满足车辆在GNSS信号受干扰时的高精度和高可靠性定位要求。

The present application provides a positioning method, a vehicle-mounted device, and a computer-readable storage medium. The method includes: a positioning device performs forward and reverse calculations on sensor data and performs fusion filtering to obtain a first combined positioning result, wherein the first combined positioning result indicates the vehicle's motion state at multiple moments, wherein the motion state includes vehicle position, vehicle speed, and vehicle attitude; the positioning device obtains, based on the first combined positioning result, an estimated value of the vehicle's initial state variables, the vehicle's position information at multiple moments, and the difference between the vehicle's motion state increments; and the positioning device corrects the first combined positioning result based on the estimated value of the vehicle's initial state variables, the vehicle's position information at multiple moments, and the difference between the vehicle's motion state increments to obtain a target combined positioning result. Implementing the present application can improve post-processing positioning accuracy and meet the high-precision and high-reliability positioning requirements for vehicles when GNSS signals are interfered with.

Description

Positioning method, vehicle-mounted device and computer readable storage medium
Technical Field
The present application relates to the field of intelligent networking automobiles (ICV, INTELLIGENT CONNECTED VEHICLE), and in particular, to a positioning method, a vehicle-mounted device, and a computer-readable storage medium.
Background
With the development of automatic driving technology, automatic driving becomes an important means for future travel. The automatic driving system framework at the present stage comprises core contents such as a sensing module, a positioning module, a planning decision module, a control module, a high-precision map module, a cloud computing module and the like. The system comprises a sensing module, a positioning module, a planning decision module, a control module and a cloud computing module, wherein the sensing module senses a physical world through a sensor, the sensor encodes the physical world according to certain data and transmits the physical world to the sensing module, the sensing module can extract a physical world model through a related algorithm, the positioning module utilizes sensor data of the sensing module to output the motion state of a vehicle relative to a world coordinate system through the positioning algorithm, the planning decision module utilizes the sensing module, the positioning module, a high-precision map and other data to integrate and decide, finally, the planning decision module sends the output to the control module, then controls an actuator on the vehicle through the control module, the cloud computing module also receives all data on the vehicle in real time, and after the cloud computing module determines the position of the vehicle, the cloud computing module provides better driving capability for the vehicle through a computing capability database on a cloud end.
In an autopilot system, the positioning module is a core functional module. The positioning module outputs the control signals for path planning and vehicle control on one hand, and assists the perception system to obtain more accurate detection and tracking results.
In the prior art, a multi-sensor fusion positioning technology is mostly adopted to determine the motion state of a vehicle, such as the position, the speed, the gesture and the like of the vehicle. At present, a conventional inertial measurement unit (Inertial Measurement Unit, IMU) and a global satellite navigation system (Global Navigation SATELLITE SYSTEM, GNSS) are used for determining the motion state of a vehicle, and the high-precision and high-reliability positioning requirements of unmanned vehicles in typical urban road scenes such as urban canyons, GNSS signal shielding and the like are difficult to meet.
Disclosure of Invention
The application provides a positioning method, a positioning system and a related device, which can optimize the result after multi-sensor fusion processing by constructing global variable modeling and solving global optimal solution when GNSS signals are interfered, so as to improve positioning accuracy.
According to the first aspect, a positioning method is provided, the positioning method comprises the steps that a positioning device obtains a first combined positioning result, wherein the first combined positioning result indicates the vehicle motion state of a vehicle at a plurality of moments, the motion state comprises a vehicle position, a vehicle speed and a vehicle gesture, the first combined positioning result is a combined positioning result obtained by fusion filtering of a second combined positioning result and a third combined positioning result, the second combined positioning result is a result obtained by forward resolving data obtained by a sensor of the vehicle, the third combined positioning result is a result obtained by inverse resolving the data obtained by the sensor of the vehicle, the data comprises position information of the vehicle at the plurality of moments acquired by a GNSS sensor, the positioning device obtains an initial state variable estimated value of the vehicle, the position information of the vehicle at the plurality of moments and a difference value of motion state increments of the vehicle according to the first combined positioning result, the initial state variable estimated value of the vehicle is the vehicle in the first combined positioning result is the difference value of the motion state of the vehicle at the first combined positioning result, the position information of the vehicle at the plurality of moments is the motion state of the vehicle at the adjacent moment, the vehicle motion state of the vehicle delta is the estimated value between the two adjacent vehicle motion state delta values is obtained by the combined state of the vehicle delta between the two-delta position estimation results at the first moment, and the motion state delta position of the vehicle is obtained between the vehicle at the two adjacent vehicle delta position is estimated positions at the moment.
After the first combined positioning result is obtained through conventional post-processing, the motion state variables of the vehicle, namely the position, the speed and the gesture of the vehicle are modeled, a residual model is constructed to realize global optimization of the vehicle, the conventional post-processing result can be corrected and optimized through multiple iterative solutions of the constructed model, and therefore the post-processing positioning precision can be improved, the positioning requirements of the vehicle on high precision and high reliability when GNSS signals are interfered are met, and the safety of the vehicle in the driving process is ensured.
In one possible implementation manner, the positioning device determines a combined positioning result satisfying a target constraint condition as the target combined positioning result, wherein the target constraint condition is that the sum of a first difference value, a second difference value and a third difference value is minimum, the first difference value is a difference between a starting state value to be estimated of the vehicle and a starting state value in the first combined positioning result, the second difference value is a difference between position information to be estimated of the vehicle and position information of the vehicle acquired by the GNSS sensor, and the third difference value is a difference between a movement state increment between the adjacent two moments to be estimated of the vehicle and a movement state change increment between the adjacent two moments in the first combined positioning result.
After the embodiment of the application is implemented, the positioning device obtains the optimal solution for all the state variables after the model is built for the global state variables, namely obtains the state variables meeting the target constraint conditions, so that the first combined positioning result is optimized and corrected to obtain the target combined positioning result, the post-processing positioning precision can be effectively improved, and the running safety of the vehicle is ensured.
In another possible implementation manner, the motion state increment between the two adjacent moments to be estimated of the vehicle is a difference value between a motion state estimated value of the vehicle at a j moment and a motion state estimated value of the vehicle at an i moment, the motion state increment between the two adjacent moments in the first combined positioning result is a difference value between a motion state value of the vehicle at the j moment and a motion state value of the vehicle at the i moment, and the i moment is earlier than the j moment by a preset duration.
After the first combined positioning result is obtained, the positioning device can obtain the motion state values of the vehicle at different moments, predict the motion state estimation values of the vehicle at different moments, respectively obtain the increment of the motion state estimation values and the increment of the motion state values of the vehicle between two adjacent moments in different moments, finally obtain the difference value of the motion state increment of the vehicle at the two adjacent moments, and utilize the difference value as a constraint condition in the constructed residual error model to ensure that the constructed model can realize global optimization on the first combined positioning result and improve the positioning precision.
In another possible implementation manner, the motion state increment between two adjacent moments in the plurality of moments to be estimated includes a position information increment to be estimated, where the position information increment to be estimated is a difference between a position difference to be estimated and an initial speed integral value, the position information difference to be estimated is a difference between a position estimated value of the vehicle at a j-th moment and a position estimated value of the vehicle at an i-th moment, and the initial speed integral value is a speed integral of the vehicle from the i-th moment to the j-th moment, where the i-th moment is earlier than the j-th moment by a preset duration.
When the embodiment of the application is implemented, when the GNSS signal is interfered, the vehicle position information obtained by the sensor is inaccurate, the positioning precision is affected, the vehicle speed is restrained by a wheel speed meter and the like, and the precision is higher compared with the vehicle position, so that the integral increment of the initial speed between two adjacent moments can be used as the position restraint, and the post-processing positioning precision is finally improved.
In another possible implementation manner, the motion state increment between two adjacent moments in the plurality of moments to be estimated includes a position information increment to be estimated, where the position information increment to be estimated is a difference between a position difference to be estimated and a relative speed integral value, where the position information difference to be estimated is a difference between a position estimated value of the vehicle at a j-th moment and a position estimated value of the vehicle at an i-th moment, and the relative speed integral value is a speed integral of the vehicle from the i-th moment to the j-th moment, and the i-th moment is earlier than the j-th moment by a preset duration without considering a speed of the vehicle at the i-th moment.
According to the embodiment of the application, the positioning device further optimizes the initial speed between two adjacent moments by utilizing the characteristics that the vehicle speed has the constraint of the wheel speed meter and the short-time relative precision is higher, and further improves the post-processing positioning precision by utilizing the integral increment of the relative speed between the two adjacent moments as the position constraint under the condition that the position constraint is inaccurate due to the interference of the GNSS signal.
In another possible implementation, the vehicle sensor includes at least one of an IMU, a wheel speed meter, and a lidar.
In a second aspect, an embodiment of the present application provides a positioning device, which may include an acquiring unit configured to acquire a first combined positioning result, where the first combined positioning result indicates a vehicle motion state of a vehicle at a plurality of moments, the motion state includes a vehicle position, a vehicle speed, and a vehicle posture, the first combined positioning result is a combined positioning result obtained by fusion filtering a second combined positioning result and a third combined positioning result, the second combined positioning result is a structure obtained by forward resolving data acquired by a sensor of the vehicle, the third combined positioning result is a result obtained by inverse resolving data acquired by the sensor of the vehicle, the data includes position information of the vehicle at the plurality of moments acquired by a GNSS sensor, the information determining unit is configured to obtain a start state variable estimated value of the vehicle, a difference value between the position information of the vehicle at the plurality of moments and a motion state increment of the vehicle, the start state variable value of the vehicle is a difference value between the two adjacent vehicle motion state estimated positions at the start state of the vehicle, the difference value is a difference value between the two vehicle motion state estimated positions of the vehicle at the start state estimated moments, and the vehicle motion state of the vehicle in the adjacent vehicle at the plurality of moments, and obtaining a target combination positioning result.
In a possible implementation manner, the correction unit is specifically configured to determine a combined positioning result satisfying a target constraint condition as the target combined positioning result, where the target constraint condition is that a sum of a first difference value, a second difference value and a third difference value is minimum, the first difference value is a difference between a starting state value to be estimated of the vehicle and a starting state value in the first combined positioning result, the second difference value is a difference between position information to be estimated of the vehicle and position information of the vehicle acquired by the GNSS sensor, and the third difference value is a difference between a motion state increment between the two adjacent moments to be estimated of the vehicle and a motion change increment between the two adjacent moments in the first combined positioning result.
In another possible implementation manner, the motion state increment between the two adjacent moments to be estimated of the vehicle is a difference value between a motion state estimated value of the vehicle at a j moment and a motion state estimated value of the vehicle at an i moment, the motion state increment between the two adjacent moments in the first combined positioning result is a difference value between a motion state value of the vehicle at the j moment and a motion state value of the vehicle at the i moment, and the i moment is earlier than the j moment by a preset duration.
In another possible implementation manner, the motion state increment between two adjacent moments in the plurality of moments to be estimated comprises a position information increment to be estimated, wherein the position information increment to be estimated is a difference value between a position difference value to be estimated and an initial speed integral value, the position information difference value to be estimated is a difference value between a position estimated value of the vehicle at a j moment and a position estimated value of the vehicle at an i moment, the initial speed integral value is a speed integral of the vehicle from the i moment to the j moment, and the i moment is earlier than the j moment by a preset duration.
In another possible implementation manner, the motion state increment between two adjacent moments in the plurality of moments to be estimated comprises a position information increment to be estimated, wherein the position information increment to be estimated is a difference value between a position difference value to be estimated and a relative speed integral value, the position information difference value to be estimated is a difference value between a position estimated value of the vehicle at a j moment and a position estimated value of the vehicle at an i moment, the relative speed integral value is a speed integral of the vehicle from the i moment to the j moment without considering the speed of the vehicle at the i moment, and the i moment is earlier than the j moment by a preset duration.
In another possible implementation, the vehicle sensor includes at least one of an inertial measurement unit IMU, a wheel speed meter, and a lidar.
In a third aspect, embodiments of the present application further provide a positioning device, which may include a memory for storing a computer program and a processor configured to invoke the computer program to cause the positioning device to perform the method provided by the first aspect or any implementation of the first aspect.
In a fourth aspect, an embodiment of the present application further provides a vehicle, where the vehicle includes the second aspect, any implementation manner of the second aspect, the third aspect, or the positioning device of any implementation manner of the third aspect.
In a fifth aspect, embodiments of the present application further provide a computer readable storage medium storing a computer program, which when executed by a processor, implements the method of the first aspect or any implementation manner of the first aspect.
In a sixth aspect, embodiments of the present application further provide a computer program which, when executed by a processor, implements the positioning method provided by the first aspect or any implementation manner of the first aspect.
Further combinations of the present application may be made to provide further implementations based on the implementations provided in the above aspects.
Drawings
FIG. 1a is a schematic diagram of an inventive concept provided by an embodiment of the present application;
FIG. 1b is a functional block diagram of a vehicle 100 according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a sensing subsystem according to an embodiment of the present application;
fig. 3a is a schematic flow chart of a positioning method according to an embodiment of the present application;
FIG. 3b is a graph of the test results of the GNSS method, the bi-directional filtering method and the method according to the present application, respectively, for a boulevard with more serious GNSS signal shielding;
FIG. 3c is a graph of the results of testing by the GNSS method, the bi-directional filtering method and the method according to the present application under the situation without GNSS signals;
FIG. 3d is a graph of the results of testing by the GNSS method, the bi-directional filtering method and the method according to the present application under the situation without GNSS signals;
Fig. 4 is a schematic structural diagram of a positioning device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of another positioning device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made more fully hereinafter with reference to the accompanying drawings, in which it is shown, however, only some, but not all embodiments of the application are shown.
The terms "first" and "second" and the like in the description and in the drawings are used for distinguishing between different objects or for distinguishing between different processes of the same object and not for describing a particular sequential order of objects. Furthermore, references to the terms "comprising" and "having" and any variations thereof in the description of the present application are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or apparatus. It should be noted that, in the embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or method of design described herein as "exemplary" or "for example" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion. In embodiments of the application, "A and/or B" means both A and B, A or B. "A, and/or B, and/or C" means any one of A, B, C, or any two of A, B, C, or A and B and C.
In order to better understand the technical solution described in the present application, the following first explains related technical terms related to the embodiments of the present application:
the positioning method provided by the embodiment of the application can be applied to application scenes of vehicle positioning, and can also be applied to other application scenes, such as vehicle navigation.
In some possible embodiments, the vehicle according to the embodiments of the present application may be an autonomous vehicle or a non-autonomous vehicle. An automatic driving vehicle is also called an unmanned vehicle, a computer driving vehicle or a wheel type mobile robot, and is an intelligent vehicle for realizing unmanned through a computer system. In practical applications, autonomous vehicles rely on artificial intelligence, visual computing, radar, monitoring devices, and global positioning systems to cooperate, allowing computer devices to operate motor vehicles automatically and safely without any human initiative.
The execution main body of the positioning method provided by the embodiment of the application can be electronic equipment in a vehicle or a positioning device in the electronic equipment. The positioning means described above may be implemented by software and/or hardware, for example.
The electronic device according to the embodiment of the application may include, but is not limited to, a host computer (or referred to as an industrial personal computer) in a vehicle.
The inertial measurement unit (Inertial Measurement Unit, IMU) involved in the embodiments of the present application is a device for measuring the carrier tri-axial attitude angle and acceleration. Typically, an IMU may include, but is not limited to, three rate gyroscopes and three linear accelerometers, with the gyroscopes and accelerometers being directly attached to a carrier (e.g., a vehicle). The gyroscope and the accelerometer are respectively used for measuring angular motion information and linear motion information of the carrier, so that the computer equipment can calculate the heading, the gesture, the speed, the position and the like of the carrier according to the measured data information.
In the multi-sensor fusion positioning technique, it is necessary to determine a vehicle motion state such as a vehicle position, a vehicle speed, a vehicle posture, and the like. Based on the existing method of determining the vehicle motion state by adopting a conventional inertial measurement unit and global satellite navigation system (Global Navigation SATELLITE SYSTEM, GNSS) fusion mode, the problem that unmanned positioning requirements on high precision and high reliability in urban canyon, GNSS signal shielding and other typical urban road scenes are difficult to meet is solved, and other common fusion positioning schemes have the defects of large calculation burden, low engineering realization and the like.
Based on the above problems, the present application proposes a new positioning method. The concept of the method may be as shown in fig. 1 a. Firstly, acquiring sensor data, for example, the sensor data source can comprise data acquired through an IMU, data acquired through a GNSS and data acquired through a WSS, then, carrying out forward resolving on the acquired sensor data, namely, sequentially carrying out data processing on the sensor data according to established resolving logic to obtain a second combined positioning result, carrying out reverse resolving on the sensor data, namely, carrying out data processing on the sensor data according to the sequence opposite to the established resolving logic to obtain a third combined positioning result, then, carrying out fusion filtering on the second combined positioning result and the third combined positioning result to obtain a first combined positioning result, wherein the first combined positioning result indicates the vehicle motion state of a vehicle at a plurality of moments, and finally, carrying out optimization on the first combined positioning result to obtain a target combined positioning result. For example, the optimization mode can comprise the steps of obtaining an initial state variable estimated value of the vehicle, position information of the vehicle at a plurality of moments and a difference value of a motion state increment of the vehicle according to the motion state of the vehicle at the plurality of moments, so that the motion state of the vehicle can be corrected through the initial state variable estimated value of the vehicle, the position information of the vehicle at the plurality of moments and the difference value of the motion state increment of the vehicle. According to the implementation mode, on the basis of conventional post-processing of the multi-sensor data, a residual model is further built on the motion state variables of the vehicle to realize global optimization of the motion state variables, global optimal solutions are obtained, and conventional post-processing results are corrected and optimized, so that the post-processing positioning precision is improved, and the requirements of high-precision and high-reliability positioning of unmanned vehicles in typical urban road scenes such as urban canyons and GNSS signal shielding can be met. The method can meet the requirements of unmanned high-precision and high-reliability positioning in typical urban road scenes such as urban canyons, GNSS signal shielding and the like, and provides convenience for constructing a high-precision map. When the vehicle is driven by the high-precision map, the safety of the vehicle in the driving process can be improved.
Fig. 1b is a functional block diagram of a vehicle 100 provided by an embodiment of the present application. In some embodiments, the vehicle 100 may be configured in a fully or partially autonomous mode, or in a manual mode.
In an embodiment of the present application, vehicle 100 may include at least a sensing subsystem 101, a decision subsystem 102, and an execution subsystem 103. Wherein, the
The sensing subsystem 101 may include at least a sensor. Specifically, the sensors may include an internal sensor for monitoring a state of the vehicle and an external sensor, and may include at least one of a vehicle speed sensor, an acceleration sensor, an angular velocity sensor, and the like. The external sensor is mainly used for monitoring the external environment around the vehicle, and can comprise a video sensor and a radar sensor, wherein the video sensor is used for acquiring and monitoring image data of the surrounding environment of the vehicle, the radar sensor is used for acquiring and monitoring electromagnetic wave data of the surrounding environment of the vehicle, and various data such as the distance between the surrounding object and the vehicle, the appearance of the surrounding object and the like are detected mainly by transmitting electromagnetic waves and then receiving electromagnetic waves reflected by the surrounding object.
For example, a plurality of radar sensors are distributed outside the entire vehicle 100. A subset of the plurality of radar sensors is coupled to the front of the vehicle 100 to locate objects in front of the vehicle 100. One or more other radar sensors may be located at the rear of the vehicle 100 to locate objects behind the vehicle 100 when the vehicle 100 is backing. Other radar sensors may be located on the side of the vehicle 100 to locate objects such as other vehicles 100 that are laterally proximate to the vehicle 100. For example, a laser detection AND RANGING (LIDAR) sensor may be mounted on the vehicle 100, e.g., the LIDAR sensor is mounted in a rotating structure mounted on top of the vehicle 100. The rotating LIDAR sensor can then transmit light signals around the vehicle 100 in a 360 ° pattern, thereby constantly mapping all objects around the vehicle 100 as the vehicle 100 moves.
For example, a camera, video camera, or other similar image capturing sensor may be mounted on the vehicle 100 to capture images as the vehicle 100 moves. Multiple imaging sensors may be placed on all sides of the vehicle 100 to capture images of the surroundings of the vehicle 100 in a 360 deg. pattern. The imaging sensor may capture not only a visible spectrum image but also an infrared spectrum image.
For example, a global positioning system (Global Positioning System, GPS) sensor may be located on the vehicle 100 to provide geographic coordinates and coordinate generation times related to the location of the vehicle 100 to the controller. The GPS includes an antenna for receiving GPS satellite signals and a GPS receiver coupled to the antenna. For example, GPS may provide geographic coordinates and time of findings when an object is observed in an image or another sensor.
In some embodiments, as shown in FIG. 2, the sensing subsystem 101 may include an Inertial Measurement Unit (IMU) 201, a global satellite navigation system (GNSS) 202, a Lidar 203, a wheel speed Sensor (WHEEL SPEED Sensor, WSS) 204, a fused positioning processing unit 205, and an antenna 206, wherein,
An Inertial Measurement Unit (IMU) 201 can output angular velocity and acceleration of the vehicle at high frequency;
Global satellite navigation system (GNSS) 202 may output a position and velocity at a phase center of GNSS corresponding antenna 206;
the laser radar 203 scans the surrounding environment of the vehicle through laser beams to obtain a large number of point clouds, and can output the position and course angle of the laser head at the installation position through matching with a pre-recorded high-precision point cloud map;
Wheel speed meter 204 outputs the forward speed of movement at the point of contact of the tire with the ground.
The processing unit of the sensor outputs data in real time, the data is transmitted to the fusion positioning processing unit 205 (such as an embedded platform) in a wired mode (such as a serial port, a network port, a controller area network (Controller Area Network, a CAN) bus and the like), and the fusion positioning processing unit 205 obtains a target combination positioning result through the positioning method provided by the application.
The decision subsystem 102 may include at least an electronic control unit (Electronic Control Unit, ECU), a map database, an object database. Specifically, an ECU, also called a "car running computer", "car mounted computer", etc., is a microcomputer controller for an automobile. The ECU is composed of a microprocessor (Microcontroller Unit, MCU), a memory (e.g., ROM, RAM), an input/output interface, an analog-to-digital converter, and a large-scale integrated circuit such as a shaping and driving circuit. In some possible embodiments, the decision subsystem 102 may also include a communication unit. Among them, the ECU is a computing device for controlling the vehicle 100, and executes a decision control function. For example, the ECU is connected to a bus and communicates with other devices via the bus. For example, the ECU may acquire information communicated from internal and external sensors, map databases, and HMI, and output corresponding information to the HMI and the actuator. For example, the ECU loads a program stored in the ROM to the RAM, and the CPU runs the program in the RAM to realize the autopilot function. In practice, decision subsystem 102 may include one ECU or may include multiple ECUs. The ECU may identify static and/or dynamic targets around the vehicle, for example, based on external sensors to obtain target monitoring results. The ECU may monitor the speed, direction, etc. of the surrounding objects. The ECU may acquire vehicle own state information based on output information of the internal sensors. The ECU plans the driving path based on the information, and outputs corresponding control signals to the actuators, which perform corresponding lateral and longitudinal movements.
In an embodiment of the present application, the positioning device may include, but is not limited to, the ECU described above.
In the embodiment of the present application, the communication unit is configured to perform V2X (Vehicle to everything, i.e., vehicle to X) communication. For example, data interaction with surrounding vehicles, roadside communication devices, cloud servers may be performed. For example, a radio coupled to an antenna may be located in the vehicle 100, providing wireless communication for the system. The Radio is used to operate any wireless communication technology or wireless standard including, but not limited to, wiFi (IEEE 802.11), cellular (e.g., global system for mobile communications (Global System for Mobile Communications, GSM), code division multiple access (Code Division Multiple Access, CDMA), time division multiple access (Time Division Multiple Access, TDMA), long term evolution (Long Term Evolution, LTE), new Radio, the Radio may include multiple radios such that the controller may communicate over a wireless channel using multiple Radio technologies.
In the embodiment of the application, the object database can store the content information or the characteristic information of the corresponding object. For example, the contents of the reticle are identified. The object database to be described may be included in the map database, and does not necessarily exist alone.
In some possible embodiments, a hard disk drive (HARD DISK DRIVE, HDD) may be used as a data storage device for the map database. It will be appreciated that the map database may contain rich location information, such as connection relationships between roads, locations of lane lines, number of lane lines, and other objects around the roads, etc., and traffic sign information (e.g., location of traffic lights, altitude, content of signs such as speed limit signs, continuous curves, slow curves, etc.), trees around the roads, building information, etc. The aforementioned information is associated with a geographic location. In addition, map information may also be used for positioning, in combination with the sensed data. In some possible embodiments, the stored map information may be two-dimensional information or three-dimensional information.
The implement subsystem 103 may include at least actuators for controlling lateral and/or longitudinal movement of the vehicle 100. For example, the brake actuator controls the braking system and the force of the brake based on control signals received from the ECU, the steering actuator controls the steering system via control signals from the ECU, and in some possible embodiments the steering system may be an electronic steering system or a mechanical steering system.
It should be noted that the elements of the system in fig. 1b are for illustration purposes only and that other systems including more or fewer components may be used to perform any of the methods disclosed herein.
Referring to fig. 3a, fig. 3a is a schematic flow chart of a positioning method according to an embodiment of the present application, where the method may include, but is not limited to, the following steps:
Step S301, the positioning device obtains a first combined positioning result.
The positioning device further processes the sensor data to obtain a first combined positioning result after the sensor data are obtained, wherein the first combined positioning result indicates a vehicle motion state of the vehicle at a plurality of moments, the motion state comprises a vehicle position, a vehicle speed and a vehicle posture, the first combined positioning result is a combined positioning result obtained by fusion filtering of a second combined positioning result and a third combined positioning result, the second combined positioning result is a result obtained by forward resolving of the sensor data obtained by the vehicle, and the third combined positioning result is a result obtained by reverse resolving of the sensor data.
In an embodiment of the present application, the sensor data may include, but is not limited to, data acquired through an Inertial Measurement Unit (IMU), data acquired through a global satellite navigation system (GNSS), data acquired through a Lidar, and data acquired through a wheel speed meter (WSS).
It should be noted that, the positions and the attitudes of the Inertial Measurement Unit (IMU), the global satellite navigation system (GNSS), the laser radar (Lidar), the Wheel Speed Sensor (WSS) and other sensors are different, so that the output data are not usually under the same reference standard, for example, the inertial navigation solution based on the IMU is a reference standard which is calculated by using the geometric center of the IMU as the position and the speed, the GNSS uses the phase center of the receiver antenna as the reference standard for positioning and speed determination, the Lidar uses the geometric center of the laser transmitter as the reference standard for pose estimation, and the WSS uses the contact point between the wheel and the ground as the reference standard for outputting the speed, so that in order to better determine the motion state error variable of the vehicle, the output of the sensors such as the GNSS, lidar and the WSS should be converted into the unified reference standard which is consistent with the inertial navigation solution. By the implementation mode, convenience is provided for subsequently improving the positioning accuracy of the vehicle.
Illustratively, the vehicle motion state may be expressed as shown in formula (1):
X=[P V A] (1)
Wherein P represents position, V represents speed, and A represents attitude.
In the embodiment of the application, the second combined positioning result and the third combined positioning result can be subjected to fusion filtering through Kalman filtering (KALMAN FILTERING) to obtain the first combined positioning result. For a specific implementation of how to perform fusion filtering on the second combined positioning result and the third combined positioning result through kalman filtering, refer to the prior art, and are not repeated herein.
Step S302, the positioning device calculates the initial state variable estimated value of the vehicle, the position information increment to be estimated between two adjacent moments of the vehicle and the difference value of the motion state increment of the vehicle between the two adjacent moments.
Specifically, after the first combined positioning result is obtained, the positioning device further calculates the combined positioning result meeting the target constraint condition based on the first combined positioning result. Optionally, the target constraint condition may include that a sum of a first difference value, a second difference value and a third difference value is minimum, where the first difference value is a difference between a start state value to be estimated of the vehicle and a start state value in the first combined positioning result, the second difference value is a position information increment to be estimated of the vehicle between two adjacent moments, and the third difference value is a difference value of a motion state increment of the vehicle between the two adjacent moments.
In the embodiment of the present application, the estimated value of the initial state variable of the vehicle (i.e., the first difference value) may be obtained by the formula (2):
Wherein, the Representing a priori information, and X 0 represents the state of motion of the vehicle at the start time.
In the embodiment of the present application, GNSS information (i.e., the second difference value) of the vehicle may be obtained through formula (3):
Wherein, P represents the position information in the motion state to be estimated, P gnss represents the GNSS position obtained by GNSS; Representing the pose matrix, and l b represents the lever arm value of the antenna in the IMU coordinate system.
In the embodiment of the present application, the difference value of the increment of the movement state of the vehicle (i.e., the third difference value) may be obtained by the formula (4):
rpva_od=r1-r2 (4)
Wherein r1 represents a motion state increment to be estimated, and r2 represents a motion state increment determined by the first combined positioning result.
In one possible embodiment, r1 and r2 may satisfy the following formula:
Wherein (X j-Xi) represents the difference between the estimated state of motion of the vehicle at the j-th time and the estimated state of motion of the vehicle at the i-th time, and (X j_filter-Xi_filter) represents the difference between the estimated state of motion of the vehicle at the j-th time and the estimated state of motion of the vehicle at the i-th time.
In a possible embodiment, the above positional information in r1 and r2 may also satisfy the following formula:
Wherein (P j-Pi) represents a difference between a position estimate of the vehicle at the j-th time and a position estimate of the vehicle at the i-th time; and representing a speed integral of the vehicle from the ith time to the jth time determined based on the first combined positioning result.
In a possible embodiment, the above positional information in r1 and r2 may also satisfy the following formula:
wherein (P j-Pi-Vi x dt) represents the difference between the estimated position of the vehicle at the j-th moment and the estimated position of the vehicle at the i-th moment; The speed integral of the vehicle from the i-th time to the j-th time is shown without considering the speed of the vehicle at the i-th time. By the implementation mode, the influence of the speed error of the vehicle at the ith moment on the fact that the first combined positioning result is used for determining the integral of the vehicle from the ith moment to the jth moment can be avoided, and the positioning accuracy can be further improved.
Step S303, the positioning device corrects the motion state of the vehicle according to the initial state variable estimated value of the vehicle, the position information increment to be estimated between two adjacent moments of the vehicle and the difference value of the motion state increment of the vehicle between the two adjacent moments of the vehicle, and a target combination positioning result is obtained.
In one possible embodiment, the positioning device establishes a target constraint condition after acquiring a difference value of an initial state variable estimated value of the vehicle, an information increment of a position to be estimated of the vehicle between two adjacent moments and a motion state increment of the vehicle between the two adjacent moments, and determines a combined positioning result meeting the target constraint condition as a target combined positioning result. The target constraint condition is that the first difference value, the second difference value and the third difference value are the smallest.
In one possible embodiment, the above target constraint may be expressed as:
Wherein σ prior represents covariance information of the estimated value of the initial state variable of the vehicle, σ gnss represents covariance information of the GNSS information of the vehicle, and σ pva_od represents covariance information of the increment of the motion state of the vehicle.
It should be noted that the target constraint condition may be other variations of the above formula (5) or an equivalent formula, which is not limited in particular in the embodiment of the present application.
In one possible embodiment, the combined positioning result satisfying the above target constraint can be obtained by a nonlinear optimization method (e.g., least squares method). For how to obtain the combined positioning result satisfying the above target constraint condition by the nonlinear optimization mode, please refer to the prior art, and a detailed description is omitted here. Of course, the combined positioning result satisfying the above target constraint condition may be obtained in other manners, which is not limited in particular by the embodiment of the present application.
By implementing the embodiment of the application, on the basis of the two-way processing fusion of a plurality of sensors, when GNSS signals are interfered, a residual model is constructed by utilizing a plurality of state variables such as the position, the speed and the gesture of the vehicle so as to carry out global optimization processing on the state variables, and the optimal solution of all the state variables can be determined through repeated iterative solution, so that the correction and optimization of the conventional post-processing result are realized, the post-processing positioning precision is improved, the high-precision and high-reliability positioning requirements of the vehicle are met, and the driving safety of the vehicle is ensured. In addition, the positioning device utilizes the characteristics of high accuracy of the speed with the constraint of a wheel speed meter and the like, adopts the integral substitution position of the speed to carry out position constraint, can optimize the initial speed between two adjacent moments, and utilizes the integral increment of the relative speed as the position constraint, thereby further improving the post-processing positioning accuracy.
The foregoing embodiments focus on how to optimize the first combined positioning result to obtain the target combined positioning result. The following describes the effects that can be achieved by the method according to the present application in combination with specific examples:
At r1 and r2 satisfy In the case of the present application, as shown in fig. 3b, the test results of the GNSS method, the bidirectional filtering method and the method of the present application are respectively adopted in the boulevard with serious GNSS signal shielding provided by the embodiment of the present application, and it can be known from fig. 3b that the method provided by the present application can obviously reduce the influence of poor GNSS signals on positioning errors.
At r1 and r2 satisfyIn the case of the present application, as shown in fig. 3c, the results of testing the GNSS method, the bi-directional filtering method, and the method of the present application under the situation without GNSS signals (for example, the simulated tunnel scenario) are provided in the embodiment of the present application, and as can be known from fig. 3c, the method of the present application can obviously improve the positioning accuracy when no GNSS signals are present.
At r1 and r2 satisfyIn the case of the present application, as shown in fig. 3d, the results of the tests performed by the GNSS method, the bi-directional filtering method, and the method of the present application are shown in fig. 3d, where the results of the tests performed by the method of the present application are shown in the scene without GNSS signals (for example, in the simulated tunnel scene), and it can be known from fig. 3d that the positioning accuracy of the method of the present application can be obviously improved.
In summary, the method provided by the application can correct the vehicle motion state by the difference value of the initial state variable estimated value of the vehicle, the position information increment to be estimated of the vehicle and the motion state increment of the vehicle, can meet the high-precision and high-reliability positioning requirements of unmanned in typical urban road scenes such as urban canyons, GNSS signal shielding and the like, provides convenience for driving the vehicle, and can also improve the safety in the driving process of the vehicle.
The foregoing details of the method according to the embodiments of the present application are provided for the purpose of better implementing the foregoing aspects of the embodiments of the present application, and accordingly, the following provides relevant apparatuses for implementing the foregoing aspects in conjunction therewith.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a positioning device according to an embodiment of the present application, where the positioning device may be an execution body in the embodiment of the method described in fig. 3a, and may execute the method and steps in the embodiment of the positioning method described in fig. 3 a. As shown in fig. 4, the positioning apparatus 400 may include an acquisition unit 410, an information determination unit 420, and a correction unit 430. Wherein, the
An obtaining unit 410, configured to obtain a first combined positioning result, where the first combined positioning result indicates a vehicle motion state of a vehicle at a plurality of moments, where the motion state includes a vehicle position, a vehicle speed, and a vehicle posture, the first combined positioning result is a combined positioning result obtained by fusion filtering a second combined positioning result and a third combined positioning result, the second combined positioning result is a structure obtained by forward resolving data acquired by a sensor of the vehicle, and the third combined positioning result is a result obtained by backward resolving data acquired by the sensor of the vehicle, where the data includes position information of the vehicle at the plurality of moments acquired by a GNSS sensor;
An information determining unit 420, configured to obtain, according to the first combined positioning result, an estimated value of a start state variable of the vehicle, position information of the vehicle at the multiple moments, and a difference value of an increment of a motion state of the vehicle, where the estimated value of the start state variable of the vehicle is a motion state of the vehicle at the start moment in the first combined positioning result, and the difference value of the increment of the motion state of the vehicle is a difference value between an increment of the motion state between two adjacent moments in the multiple moments to be estimated and an increment of the motion state between the two adjacent moments in the first combined positioning result;
and the correction unit 430 is configured to correct the first combined positioning result according to the estimated value of the initial state variable of the vehicle, the position information of the vehicle at the multiple moments, and the difference value of the motion state increment of the vehicle, so as to obtain a target combined positioning result.
As an embodiment, the correction unit 430 is specifically configured to determine, as the target combined positioning result, a combined positioning result that meets a target constraint condition, where the target constraint condition is that a sum of a first difference value, a second difference value and a third difference value is minimum, the first difference value is a difference between a start state value to be estimated of the vehicle and a start state value in the first combined positioning result, the second difference value is a difference between position information to be estimated of the vehicle and position information of the vehicle acquired by the GNSS sensor, and the third difference value is a difference between a motion state increment between the two adjacent moments to be estimated of the vehicle and a motion change increment between the two adjacent moments in the first combined positioning result.
As one embodiment, the motion state increment between the two adjacent moments to be estimated of the vehicle is the difference between the motion state estimated value of the vehicle at the j moment and the motion state estimated value of the vehicle at the i moment, the motion state increment between the two adjacent moments in the first combined positioning result is the difference between the motion state value of the vehicle at the j moment and the motion state value of the vehicle at the i moment, and the i moment is earlier than the preset duration at the j moment.
As one embodiment, the motion state increment between two adjacent moments in the plurality of moments to be estimated comprises a position information increment to be estimated, wherein the position information increment to be estimated is a difference value between a position difference value to be estimated and an initial speed integral value, the position information difference value to be estimated is a difference value between a position estimated value of the vehicle at a j moment and a position estimated value of the vehicle at an i moment, the initial speed integral value is a speed integral of the vehicle from the i moment to the j moment, and the i moment is earlier than the j moment for a preset duration.
As one embodiment, the motion state increment between two adjacent moments in the plurality of moments to be estimated comprises a position information increment to be estimated, wherein the position information increment to be estimated is a difference value between a position difference value to be estimated and a relative speed integral value, the position information difference value to be estimated is a difference value between a position estimated value of the vehicle at a j moment and a position estimated value of the vehicle at an i moment, the relative speed integral value is a speed integral of the vehicle from the i moment to the j moment without considering the speed of the vehicle at the i moment, and the i moment is earlier than the j moment by a preset time length.
As one embodiment, the sensor of the vehicle includes at least one of an inertial measurement unit IMU, a wheel speed meter, and a lidar.
It should be understood that the above-described structure of the positioning device is merely an example, and should not be construed as a specific limitation, and the respective units of the positioning device may be added, reduced, or combined as needed. In addition, the operations and/or functions of each unit in the positioning device are respectively for implementing the corresponding flow of the method described in fig. 3a, and are not repeated herein for brevity.
Referring to fig. 5, fig. 5 is a schematic structural diagram of another positioning device according to an embodiment of the present application. As shown in fig. 5, the positioning device 500 includes a processor 510, a communication interface 520, and a memory 530, which are interconnected by an internal bus 540. It should be understood that the positioning device 500 may be a terminal device or a vehicle-mounted device, and is applied to a vehicle-mounted ethernet.
The processor 510 may be comprised of one or more general purpose processors, such as a central processing unit (central processing unit, CPU), or a combination of CPU and hardware chips. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (programmable logic device, PLD), or a combination thereof. The PLD may be a complex programmable logic device (complex programmable logic device, CPLD), a field-programmable gate array (FPGA) GATE ARRAY, general-purpose array logic (GENERIC ARRAY logic, GAL), or any combination thereof.
Bus 540 may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, or the like. The bus 540 may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, only one thick line is shown in fig. 5, but not only one bus or one type of bus.
The memory 530 may include volatile memory (RAM) such as random access memory (random access memory), non-volatile memory (non-volatile memory) such as read-only memory (ROM), flash memory (flash memory), hard disk (HARD DISK DRIVE, HDD) or solid state disk (solid-state disk) (SSD), and the memory 530 may also include combinations of the above.
It should be noted that, the memory 530 of the positioning device 500 stores computer programs, and the processor 510 executes these computer programs, so that the positioning device 500 performs the method in the embodiment shown in fig. 3 a.
The embodiment of the application also provides a computer readable storage medium for storing a computer program for causing a positioning device to perform the method as in the embodiment shown in fig. 3a described above.
Embodiments of the present application also provide a computer program operable to cause an electronic device to perform the method of the embodiment shown in fig. 3a described above.
It will be appreciated by those of ordinary skill in the art that the various exemplary elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Those of skill in the art will appreciate that the functions described in connection with the various illustrative logical blocks, modules, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware, software, firmware, or any combination thereof. If implemented in software, the functions described by the various illustrative logical blocks, modules, and steps may be stored on a computer readable medium or transmitted as one or more instructions or code and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media corresponding to tangible media, such as data storage media, or communication media including any medium that facilitates transfer of a computer program from one place to another (e.g., according to a communication protocol). In this manner, a computer-readable medium may generally correspond to (1) a non-transitory tangible computer-readable storage medium, or (2) a communication medium, such as a signal or carrier wave. Data storage media may be any available media that can be accessed by one or more computers or one or more processors to retrieve instructions, code and/or data structures for implementing the techniques described in this disclosure. The computer program product may include a computer-readable medium.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form. The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. The storage medium includes a U disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (15)

1. A positioning method, comprising:
The method comprises the steps of obtaining a first combined positioning result, wherein the first combined positioning result indicates the vehicle motion state of a vehicle at a plurality of moments, the motion state comprises the vehicle position, the vehicle speed and the vehicle posture, the first combined positioning result is a combined positioning result obtained by fusion filtering of a second combined positioning result and a third combined positioning result, the second combined positioning result is a result obtained by forward resolving of data obtained by a sensor of the vehicle, the third combined positioning result is a result obtained by reverse resolving of the data obtained by the sensor of the vehicle, and the data comprises position information of the vehicle at the plurality of moments, which is obtained by a GNSS sensor of a global satellite navigation system;
Obtaining an initial state variable estimated value of the vehicle, position information of the vehicle at a plurality of moments and a difference value of motion state increment of the vehicle according to the first combined positioning result, wherein the initial state variable estimated value of the vehicle is the motion state of the vehicle at the initial moment in the first combined positioning result, and the difference value of the motion state increment of the vehicle is the difference value between the motion state increment between two adjacent moments in the plurality of moments to be estimated and the motion state increment between the two adjacent moments in the first combined positioning result;
And correcting the first combined positioning result according to the initial state variable estimated value of the vehicle, the position information of the vehicle at the plurality of moments and the difference value of the motion state increment of the vehicle to obtain a target combined positioning result.
2. The method of claim 1, wherein the correcting the first combined location result based on the estimated value of the starting state variable of the vehicle, the positional information of the vehicle at the plurality of times, and the difference in the increment of the movement state of the vehicle, comprises:
Determining a combined positioning result meeting a target constraint condition as the target combined positioning result, wherein the target constraint condition is that the sum of a first difference value, a second difference value and a third difference value is minimum, the first difference value is the difference between a starting state value to be estimated of the vehicle and a starting state value in the first combined positioning result, the second difference value is the difference between position information to be estimated of the vehicle and position information of the vehicle acquired by the GNSS sensor, and the third difference value is the difference between the increment of the movement state between two adjacent moments to be estimated of the vehicle and the increment of the movement state change between the two adjacent moments in the first combined positioning result.
3. The method of claim 2, wherein the motion state increment between the two adjacent moments to be estimated of the vehicle is a difference between a motion state estimated value of the vehicle at a j-th moment and a motion state estimated value of the vehicle at an i-th moment, wherein the motion state increment between the two adjacent moments in the first combined positioning result is a difference between a motion state value of the vehicle at the j-th moment and a motion state value of the vehicle at the i-th moment, and wherein the i-th moment is earlier than the j-th moment by a preset duration.
4. The method of any of claims 1-2, wherein the motion state delta between two adjacent moments of the plurality of moments to be estimated comprises a position information delta to be estimated, wherein the position information delta to be estimated is a difference between a position difference to be estimated and an initial velocity integral value, wherein the position information difference to be estimated is a difference between a position estimated value of the vehicle at a j-th moment and a position estimated value of the vehicle at an i-th moment, wherein the initial velocity integral value is a velocity integral of the vehicle from the i-th moment to the j-th moment, and wherein the i-th moment is earlier than the j-th moment by a preset duration.
5. The method of any of claims 1-2, wherein the motion state delta between two adjacent moments of the plurality of moments to be estimated comprises a position information delta to be estimated, wherein the position information delta to be estimated is a difference between a position difference to be estimated and a relative velocity integral value, wherein the position information difference to be estimated is a difference between a position estimate of the vehicle at a j-th moment and a position estimate of the vehicle at an i-th moment, wherein the relative velocity integral value is a velocity integral of the vehicle from the i-th moment to the j-th moment irrespective of a velocity of the vehicle at the i-th moment, and wherein the i-th moment is earlier than the j-th moment by a preset duration.
6. The method of claim 1 or 2, wherein the vehicle sensor comprises at least one of an inertial measurement unit IMU, a wheel speed meter, and a lidar.
7. A positioning device, comprising:
The system comprises an acquisition unit, a first combination positioning result, a second combination positioning unit and a third combination positioning unit, wherein the first combination positioning result indicates the vehicle motion state of a vehicle at a plurality of moments, the motion state comprises the vehicle position, the vehicle speed and the vehicle posture, the first combination positioning result is a combination positioning result obtained by fusion filtering of a second combination positioning result and a third combination positioning result, the second combination positioning result is a structure obtained by forward resolving of data acquired by a sensor of the vehicle, the third combination positioning result is a result obtained by backward resolving of the data acquired by the sensor of the vehicle, and the data comprises the position information of the vehicle at the plurality of moments acquired by a GNSS sensor;
An information determining unit, configured to obtain, according to the first combined positioning result, an estimated value of a start state variable of the vehicle, position information of the vehicle at the multiple moments, and a difference value of an increment of a motion state of the vehicle, where the estimated value of the start state variable of the vehicle is a motion state of the vehicle at the start moment in the first combined positioning result, and the difference value of the increment of the motion state of the vehicle is a difference value between an increment of the motion state between two adjacent moments in the multiple moments to be estimated and an increment of the motion state between the two adjacent moments in the first combined positioning result;
And the correction unit is used for correcting the first combined positioning result according to the initial state variable estimated value of the vehicle, the position information of the vehicle at the plurality of moments and the difference value of the motion state increment of the vehicle to obtain a target combined positioning result.
8. The apparatus of claim 7, wherein the correction unit is specifically configured to:
and determining a combined positioning result meeting a target constraint condition as the target combined positioning result, wherein the target constraint condition is that the sum of a first difference value, a second difference value and a third difference value is minimum, the first difference value is the difference between a starting state value to be estimated of the vehicle and a starting state value in the first combined positioning result, the second difference value is the difference between position information to be estimated of the vehicle and position information of the vehicle acquired by the GNSS sensor, and the third difference value is the difference between the motion state increment between two adjacent moments to be estimated of the vehicle and the motion change increment between the two adjacent moments in the first combined positioning result.
9. The apparatus of claim 8, wherein the delta motion state between the two adjacent moments to be estimated of the vehicle is a difference between an estimated motion state value of the vehicle at a j-th moment and an estimated motion state value of the vehicle at an i-th moment, wherein the delta motion state between the two adjacent moments in the first combined positioning result is a difference between the estimated motion state value of the vehicle at the j-th moment and the estimated motion state value of the vehicle at the i-th moment, and wherein the i-th moment is earlier than the j-th moment by a preset duration.
10. The apparatus of claim 7 or 8, wherein the motion state delta between two adjacent time instants of the plurality of time instants to be estimated comprises a position information delta to be estimated, the position information delta to be estimated being a difference between a position difference to be estimated and an initial velocity integral value, wherein the position information difference to be estimated is a difference between a position estimate of the vehicle at a j-th time instant and a position estimate of the vehicle at an i-th time instant, the initial velocity integral value is a velocity integral of the vehicle from the i-th time instant to the j-th time instant, and the i-th time instant is earlier than the j-th time instant by a preset duration.
11. The apparatus of claim 7 or 8, wherein the motion state delta between two adjacent ones of the plurality of time instants to be estimated comprises a position information delta to be estimated, the position information delta to be estimated being a difference between a position difference to be estimated and a relative velocity integral value, wherein the position information difference to be estimated is a difference between a position estimate of the vehicle at a j-th time instant and a position estimate of the vehicle at an i-th time instant, the relative velocity integral value is a velocity integral of the vehicle from the i-th time instant to the j-th time instant irrespective of a velocity of the vehicle at the i-th time instant, and the i-th time instant is earlier than the j-th time instant by a preset duration.
12. The apparatus of claim 7 or 8, wherein the vehicle sensor comprises at least one of an inertial measurement unit IMU, a wheel speed meter, and a lidar.
13. A positioning device comprising a memory and a processor, the processor executing computer instructions stored in the memory, causing the positioning device to perform the method of any one of claims 1-6.
14. A vehicle, characterized in that it comprises a positioning device according to any one of claims 7-13.
15. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program, which, when executed by a processor, implements the method according to any of claims 1-6.
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