CN115484543A

CN115484543A - Positioning method, vehicle-mounted device and computer readable storage medium

Info

Publication number: CN115484543A
Application number: CN202110606780.4A
Authority: CN
Inventors: 杨晓龙; 伍勇
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2021-05-31
Filing date: 2021-05-31
Publication date: 2022-12-16

Abstract

The application provides a positioning method, a vehicle-mounted device and a computer readable storage medium, wherein the method comprises the following steps: the positioning device carries out forward and reverse calculation on the sensor data and carries out fusion filtering to obtain a first combined positioning result, the first combined positioning result indicates the vehicle motion state of the vehicle at a plurality of moments, and the motion state comprises the vehicle position, the vehicle speed and the vehicle attitude; the positioning device obtains 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; the positioning device corrects the first combined positioning result according to the initial state variable estimated value of the vehicle, the position information of the vehicle at a plurality of moments and the difference value of the motion state increment of the vehicle to obtain a target combined positioning result. By implementing the method and the device, the post-processing positioning precision can be improved, and the requirements of high precision and high reliability positioning of the vehicle when the GNSS signal is interfered are met.

Description

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

Technical Field

The present disclosure relates to the field of Intelligent networked vehicles (ICV), and in particular, to a positioning method, a Vehicle-mounted device, and a computer-readable storage medium.

Background

With the development of the automatic driving technology, automatic driving becomes an important means for going out in the future. The automatic driving system framework at the present stage has core contents of 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 sensing module senses the physical world through a sensor; the sensor codes the physical world according to certain data and transmits the coded data to the sensing module, so that the sensing module can extract a physical world model through a related algorithm; the positioning module outputs the motion states of the vehicle, such as position, speed, attitude and the like relative to a world coordinate system through a positioning algorithm by utilizing the sensor data of the sensing module; the planning decision module integrates and decides by utilizing data such as a sensing module, a positioning module, a high-precision map and the like, finally transmits output to the control module, and controls an actuator on the vehicle through the control module; the cloud computing module can also receive all data on the vehicle in real time, and after the cloud computing module determines the position of the vehicle, the cloud computing module can provide better driving capability for the vehicle by utilizing computing capability and a database on the cloud end.

In an autopilot system, the positioning module is the core function module. The output of the positioning module is used for path planning and vehicle control, and meanwhile, the positioning module also assists a sensing 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 states of the vehicle, such as the position, the speed, the attitude and the like of the vehicle. At present, the motion state of a vehicle is determined by adopting a conventional Inertial Measurement Unit (IMU) and Global Navigation Satellite System (GNSS) fusion mode, 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 sheltering are difficult to meet.

Disclosure of Invention

The application provides a positioning method, a positioning system and a related device, which can optimize a result after multi-sensor fusion processing by constructing global variable modeling and solving a global optimal solution when a GNSS signal is interfered, so that the positioning precision is improved.

In a first aspect, a positioning method is provided, and the method includes: the positioning device acquires a first combined positioning result, wherein the first combined positioning result indicates vehicle motion states of the vehicle at a plurality of moments, and the motion states comprise a vehicle position, a vehicle speed and a vehicle posture; the first combined positioning result is a combined positioning result obtained by performing fusion filtering on a second combined positioning result and a third combined positioning result, the second combined positioning result is a result obtained by performing forward calculation on data acquired by a sensor of a vehicle, the third combined positioning result is a result obtained by performing reverse calculation on the data acquired by the sensor of the vehicle, and the data comprises position information of the vehicle at multiple moments acquired by a GNSS sensor; the positioning device obtains a starting state variable estimation value of the vehicle, position information of the vehicle at the multiple moments and a difference value of a motion state increment of the vehicle according to the first combined positioning result, wherein the starting state variable estimation value of the vehicle is a motion state of the vehicle at the starting moment in the first combined positioning result, and the difference value of the motion state increment of the vehicle is a difference value between a motion state increment between two adjacent moments in the multiple moments to be estimated and a motion state increment between the two adjacent moments in the first combined positioning result; and the positioning device corrects the first combined positioning result according to the initial state variable estimated value 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 to obtain a target combined positioning result.

By implementing the embodiment of the application, 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 posture of the vehicle, are modeled, the residual error model is constructed to realize global optimization, and the conventional post-processing result can be corrected and optimized by carrying out multiple iterative solution on the constructed model, so that the post-processing positioning precision can be improved, the high-precision and high-reliability positioning requirements of the vehicle when a GNSS signal is interfered are met, and the safety of the vehicle in the driving process is ensured.

In a possible implementation manner, the positioning apparatus determines a combined positioning result that satisfies 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 obtained by the GNSS sensor, and the third difference value is a difference between a motion state increment between two adjacent time instants to be estimated of the vehicle and a motion state change increment between the two adjacent time instants in the first combined positioning result.

By implementing the embodiment of the application, after the positioning device constructs the model aiming at the global state variables, the optimal solution is obtained by obtaining all the state variables, namely the state variables meeting the target constraint condition, 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 vehicle driving safety is ensured.

In another possible implementation manner, the motion state increment between the two adjacent time instants to be estimated of the vehicle is a difference value between the motion state estimation value of the vehicle at the j time instant and the motion state estimation value of the vehicle at the i time instant; the motion state increment between the two adjacent moments in the first combined positioning result is the difference value 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 ith time is earlier than the jth time by a preset time length.

By implementing the embodiment of the application, after the positioning device obtains the first combined positioning result, the motion state values of the vehicle at different moments can be obtained, the motion state estimation values of the vehicle are obtained by prediction at different moments, then the increment of the motion state estimation values and the increment of the motion state values of the vehicle between two adjacent moments at different moments can be obtained respectively, finally the difference value of the motion state increments of the vehicle at two adjacent moments can be obtained, the difference value is used as a constraint condition in the constructed residual error model, the constructed model can be guaranteed to realize global optimization on the first combined positioning result, and the positioning accuracy is improved.

In another possible implementation manner, the increment of the motion state between two adjacent time instants in the multiple time instants to be estimated includes an increment of position information to be estimated, and the increment of the position information to be estimated is a difference value between a difference value of a position to be estimated and an initial speed integrated value, where the difference value of the position information to be estimated is a difference value between a position estimation value of the vehicle at a j time instant and a position estimation value of the vehicle at an i time instant, the initial speed integrated value is a speed integrated value of the vehicle from the i time instant to the j time instant, and the i time instant is earlier than the j time instant by a preset time length.

By implementing the embodiment of the application, when a GNSS signal is interfered, the vehicle position information acquired by the sensor is not accurate enough, the positioning precision is influenced, 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 increment of the motion state between two adjacent moments in the plurality of moments to be estimated includes an increment of position information to be estimated, and the increment of position information to be estimated is a difference value between a difference value of a position to be estimated and an integrated value of relative speed, wherein the difference value of position information to be estimated is a difference value between a position estimation value of the vehicle at a j-th moment and a position estimation value of the vehicle at an i-th moment, the integrated value of relative speed is a speed integration of the vehicle from the i-th moment to the j-th moment without considering the speed of the vehicle at the i-th moment, and the i-th moment is earlier than the j-th moment by a preset time length.

By implementing the embodiment of the application, the positioning device utilizes the characteristics that the vehicle speed is restricted by a wheel speed meter and the relative accuracy in a short time is high, further optimizes the initial speed between two adjacent moments under the condition that the position restriction is inaccurate due to the interference of a GNSS signal, and utilizes the integral increment of the relative speed between two adjacent moments as the position restriction, so that the post-processing positioning accuracy can be further improved.

In another possible implementation, the sensor of the vehicle 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 apparatus, which may include: the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a first combined positioning result, the first combined positioning result indicates a vehicle motion state of a vehicle at a plurality of moments, the motion state comprises a vehicle position, a vehicle speed and a vehicle attitude, 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 reverse resolving data acquired by the sensor of the vehicle, and the data comprises 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, initial state variable estimation values of the vehicle, position information of the vehicle at the multiple times, and differences between motion state increments of the vehicle, where the initial state variable estimation values of the vehicle are motion states of the vehicle at initial times in the first combined positioning result, and the differences between the motion state increments of the vehicle between two adjacent times in the multiple times to be estimated and the motion state increments between the two adjacent times in the first combined positioning result; and the correcting unit is used for correcting the first combined positioning result according to the initial state variable estimation value 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 to obtain a target combined positioning result.

In a possible implementation manner, the modifying unit is specifically configured to: 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 an initial state value to be estimated of the vehicle and an initial 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 the position information of the vehicle obtained by the GNSS sensor, and the third difference value is the difference between a motion state increment between two adjacent time instants to be estimated of the vehicle and a motion change increment between the two adjacent time instants in the first combined positioning result.

In another possible implementation manner, the motion state increment between two adjacent time instants in the plurality of time instants to be estimated includes a position information increment to be estimated; 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, wherein the position information difference value to be estimated is a difference value between a position estimation value of the vehicle at the j time and a position estimation value of the vehicle at the i time; the initial speed integrated value is the speed integrated value of the vehicle from the ith time to the jth time; and the ith time is earlier than the jth time by a preset time length.

In another possible implementation manner, the motion state increment between two adjacent time instants in the plurality of time instants to be estimated includes a position information increment to be estimated; the increment of the position information to be estimated is a difference value between a difference value of the position to be estimated and a relative speed integral value, wherein the difference value of the position information to be estimated is a difference value between a position estimation value of the vehicle at the j moment and a position estimation value of the vehicle at the i moment; the relative speed integrated value is a speed integration of the vehicle from the i-th time to the j-th time without considering the speed of the vehicle at the i-th time; and the ith time is earlier than the jth time by a preset time length.

In another possible implementation, the sensor of the vehicle includes: at least one of an inertial measurement unit IMU, a wheel speed meter, and a lidar.

In a third aspect, an embodiment of the present application further provides a positioning apparatus, where the positioning apparatus may include a memory and a processor, where the memory is used for storing a computer program, and the processor is configured to invoke the computer program to cause the positioning apparatus to execute the method provided in the first aspect or any implementation manner of the first aspect.

In a fourth aspect, embodiments of the present application further provide a vehicle, where the vehicle includes the positioning device described in the second aspect, any one of the implementation manners of the second aspect, the third aspect, or any one of the implementation manners of the third aspect.

In a fifth aspect, this application further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements the method of the first aspect or any implementation manner of the first aspect.

In a sixth aspect, this embodiment of the present application further provides a computer program, which when executed by a processor, implements the positioning method provided in the foregoing first aspect or any one of the foregoing implementations of the first aspect.

The present application can further combine to provide more implementations on the basis of the implementations provided by the above aspects.

Drawings

FIG. 1a is a schematic diagram of the 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 disclosure;

fig. 2 is a schematic structural diagram of a sensing subsystem according to an embodiment of the present disclosure;

fig. 3a is a schematic flowchart of a positioning method according to an embodiment of the present application;

fig. 3b is a diagram illustrating results of tests performed by a GNSS method, a bidirectional filtering method and the method in a shade road with severe GNSS signal shielding according to the embodiment of the present application;

fig. 3c is a diagram illustrating results of a test performed by a GNSS method, a bidirectional filtering method and the method in the present application in a scenario without GNSS signals according to an embodiment of the present application;

fig. 3d is a diagram illustrating results of a test performed by a GNSS method, a bidirectional filtering method and the method in the present application in a scenario without GNSS signals according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of a positioning apparatus according to an embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of another positioning apparatus provided in the embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application are described below clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments.

The terms "first" and "second" and the like in the specification and drawings of the present application are used for distinguishing different objects or for distinguishing different processes for the same object, and are not used for describing a specific order of the objects. Furthermore, the terms "including" and "having," and any variations thereof, as referred to in the description of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly 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 "for example" are used to mean serving as examples, illustrations or descriptions. Any embodiment or design method described herein as "exemplary" or "e.g.," should not be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion. In the examples of the present application, "A and/or B" means both A and B, and A or B. "A, and/or B, and/or C" means any one of A, B, C, or, means any two of A, B, C, or, means A and B and C.

For better understanding of the technical solutions described in the present application, the following first explains the 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 an application scene 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 wheeled mobile robot, and is an intelligent vehicle which realizes unmanned driving through a computer system. In practical applications, autonomous vehicles rely on the cooperative use of artificial intelligence, vision computing, radar, surveillance devices, and global positioning systems to allow computer devices to operate motor vehicles automatically and safely without any human-active operations.

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 above-described positioning means may be implemented by software and/or hardware, for example.

The electronic devices mentioned above in the embodiments of the present application may include, but are not limited to: a master control computer (alternatively referred to as an industrial personal computer) in the vehicle.

An Inertial Measurement Unit (IMU) referred to in the embodiments of the present application is a device for measuring three-axis attitude angles and accelerations of a carrier. Generally, IMUs may include, but are not limited to: three rate gyros and three line accelerometers; the gyroscope and accelerometer are attached directly 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 conveniently calculate information such as the heading, the attitude, the speed and the position of the carrier according to the measured data information.

In the multi-sensor fusion positioning technology, the motion state of the vehicle, such as the position, the speed, the attitude and the like of the vehicle, needs to be determined. The vehicle motion state is determined based on the conventional mode of fusing an inertial measurement unit and a Global Navigation Satellite System (GNSS), so that the problems of high-precision and high-reliability positioning requirements of unmanned vehicles in typical urban road scenes such as urban canyons and GNSS signal sheltering are difficult to meet, and other common fusion positioning schemes have the defects of large calculation burden, low engineering realizability and the like.

Based on the above problems, the present application proposes a new positioning method. The concept of this method can be seen in fig. 1 a. First, sensor data is acquired, for example, the sensor data source may include data acquired by an IMU, data acquired by a GNSS, and data acquired by a WSS; then, carrying out forward calculation on the acquired sensor data, namely carrying out data processing on the sensor data in sequence according to a set calculation logic to obtain a second combined positioning result, carrying out reverse calculation on the sensor data, namely carrying out data processing on the sensor data according to a sequence opposite to the set calculation logic to obtain a third combined positioning result; then, performing 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 the vehicle at multiple moments; and finally, optimizing the first combined positioning result to obtain a target combined positioning result. For example, the optimization method may include: and obtaining the initial state variable estimation value of the vehicle, the position information of the vehicle at a plurality of moments and the difference value of the motion state increment of the vehicle according to the motion states of the vehicle at a plurality of moments, so that the motion state of the vehicle can be corrected through the initial state variable estimation value of the vehicle, the position information of the vehicle at a 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 multi-sensor data, a residual error model is further constructed for the motion state variables of the vehicle to realize global optimization of the motion state variables, a global optimal solution is obtained, and the conventional post-processing result is corrected and optimized, so that the post-processing positioning precision is improved, and the requirements of high-precision and high-reliability positioning under typical urban road scenes such as urban canyons and GNSS signal shielding in an unmanned mode can be met. The method can meet the high-precision and high-reliability positioning requirements of unmanned driving in typical urban road scenes such as urban canyons and GNSS signal shelters, and provides convenience for construction of high-precision maps. When the vehicle is driven through 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 according to an embodiment of the present disclosure. In some embodiments, the vehicle 100 may be configured in a fully autonomous mode or a partially autonomous mode, or a manual driving mode.

In the present embodiment, the vehicle 100 may include at least the following subsystems: a sensing subsystem 101, a decision making subsystem 102 and an execution subsystem 103. Wherein,

the sensing subsystem 101 may include at least a sensor. In particular, the sensors may include internal sensors and external sensors; the internal sensor is used to monitor the state of the vehicle, and may include at least one of a vehicle speed sensor, an acceleration sensor, an angular velocity sensor, and the like. External sensors are primarily used to monitor the external environment surrounding the vehicle, and may include, for example, video sensors and radar sensors; 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 mainly detects various data such as the distance between a surrounding object and the vehicle and the shape of the surrounding object by transmitting the electromagnetic wave and then receiving the electromagnetic wave reflected by the surrounding object.

For example, a plurality of radar sensors are distributed throughout the exterior of the vehicle 100. A subset of the plurality of radar sensors are 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, thereby locating objects behind the vehicle 100 when the vehicle 100 is backed up. Other radar sensors may be located on the sides of the vehicle 100, thereby locating objects, such as other vehicles 100, that are laterally adjacent to the vehicle 100. For example, a LIDAR (light detection and ranging) sensor may be mounted on the vehicle 100, for example, in a rotating structure mounted on top of the vehicle 100. The rotating LIDAR sensors 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, an imaging sensor such as a camera, video camera, or other similar image capture 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 around the vehicle 100 in a 360 ° pattern. The imaging sensor may capture not only visible spectrum images, but also infrared spectrum images.

For example, a Global Positioning System (GPS) sensor may be located on the vehicle 100 to provide the controller with geographic coordinates and coordinate generation time related to the location of the vehicle 100. The GPS includes an antenna for receiving GPS satellite signals and a GPS receiver coupled to the antenna. For example, GPS may provide the geographic coordinates and time of a finding when the object is viewed 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 Navigation Satellite System (GNSS) 202, a Lidar (Lidar) 203, a Wheel Speed Sensor (WSS) 204, a fused position processing unit 205, and an antenna 206, wherein,

an Inertial Measurement Unit (IMU) 201 may output an angular velocity and an acceleration of the vehicle at high frequencies;

a Global Navigation Satellite System (GNSS) 202 may output a position and a velocity at a phase center of a GNSS corresponding antenna 206;

the laser radar 203 scans the surrounding environment of the vehicle through laser beams to obtain a large amount of point clouds, and can output the position and the course angle of the laser head installation position through matching processing with a high-precision point cloud map recorded in advance;

the wheel speed meter 204 outputs the speed of forward movement at the point where the tire contacts 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) through a wired manner (such as a serial port, a Network port, a Controller Area Network (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 making subsystem 102 may include at least an Electronic Control Unit (ECU), a map database, and an object database. Specifically, the ECU, also known as a "traveling computer" or a "vehicle-mounted computer", is a microcomputer controller dedicated to automobiles. The ECU is composed of a Microprocessor (MCU), a memory (e.g., a read only memory ROM, a random access memory RAM), an input/output interface, an analog-to-digital converter, and a large-scale integrated circuit such as a shaping circuit and a driving circuit. In some possible embodiments, the decision making subsystem 102 may also include a communication unit. The ECU is a computing device for controlling the vehicle 100, and performs a decision-making control function. For example, the ECU is connected to a bus and communicates with other devices through the bus. For example, the ECU may acquire information transmitted from internal and external sensors, a map database, and an HMI, and output corresponding information to the HMI and the actuator. For example, the ECU loads a program stored in the ROM into the RAM, and the CPU runs the program in the RAM to realize the automatic driving function. In practical applications, the decision making subsystem 102 may include one ECU or a plurality of 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 surrounding objects. The ECU may acquire the vehicle own state information based on the output information of the internal sensor. The ECU plans a driving path according to the information, outputs a corresponding control signal to the actuator, and executes corresponding transverse and longitudinal movement by the actuator.

In the 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 exchanging) communication. For example, data interaction may be performed with surrounding vehicles, roadside communication devices, cloud servers. For example, a radio coupled to an antenna may be located in the vehicle 100 to provide wireless communication for the system. The radios are used to operate any wireless communication technology or wireless standard, including but not limited to WiFi (IEEE 802.11), cellular (e.g., one or more of Global System for Mobile Communications (GSM), code Division Multiple Access (CDMA), time Division Multiple Access (TDMA), long Term Evolution (LTE), new air interface (New Radio)).

In the embodiment of the present application, content information or feature information of a corresponding object may be stored in the object database. For example, the contents of the reticle are identified. The object database to be described may be contained in the map database and does not necessarily exist separately.

In the embodiment of the application, the map database is used for storing map information; in some possible embodiments, a 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; for example, the connection relationship between roads, the positions of lane lines, the number of lane lines, and other objects around the roads; as another example, information about traffic signs (e.g., location, height of traffic lights, content of signs such as speed limit signs, continuous curves, crawls, etc.), trees around roads, building information, etc. The aforementioned information is associated with a geographic location. In addition, map information may also be used for localization, in combination with sensory data. In some possible embodiments, the stored map information may be two-dimensional information or may be 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 braking force according to the control signal received from the ECU; the steering actuator controls a steering system through a control signal from the ECU; 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 illustrative purposes only, and 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 flowchart of a positioning method provided in 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.

Specifically, after acquiring the sensor data, the positioning device further processes the sensor data to obtain a first combined positioning result, where the first combined positioning result indicates a vehicle motion state of the vehicle at multiple moments, where the motion state includes a vehicle position, a vehicle speed, and a vehicle attitude; the first combined positioning result is a combined positioning result obtained by fusion filtering the second combined positioning result and the third combined positioning result; the second combined positioning result is obtained by forward resolving the sensor data acquired by the vehicle; and the third combined positioning result is a result obtained by reversely calculating the sensor data.

In embodiments of the present application, the sensor data may include, but is not limited to, data acquired by an Inertial Measurement Unit (IMU), data acquired by a Global Navigation Satellite System (GNSS), data acquired by a Lidar (Lidar), data acquired by a Wheel Speed Sensor (WSS).

It should be noted that, the positions and attitudes of the sensors such as the Inertial Measurement Unit (IMU), the Global Navigation Satellite System (GNSS), the Lidar (Lidar), and the Wheel Speed Sensor (WSS) are different, so that the output data thereof are not always under the same reference, the IMU-based inertial navigation solution is a reference that is used for resolving the position and the speed, the GNSS is a reference that is used for determining the position and the speed by using the phase center of the receiver antenna, the Lidar is a reference for estimating the pose by using the geometric center of the laser transmitter thereof, and the WSS is a reference that is used for outputting the speed by using the contact point between the wheel and the ground. Through the implementation mode, convenience is provided for subsequently improving the positioning accuracy of the vehicle.

For example, the vehicle motion state may be expressed as shown in equation (1):

X＝[P V A] (1)

wherein P represents a position; v represents a speed; a represents a posture.

In the embodiment of the present application, the second combined positioning result and the third combined positioning result may be fusion-filtered 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, which is not repeated herein.

Step S302: the positioning device calculates and obtains the estimated value of the initial state variable of the vehicle, the increment of the position information to be estimated of the vehicle between two adjacent moments and the difference of the increment of the motion state of the vehicle between two adjacent moments.

Specifically, after obtaining the first combined positioning result, the positioning apparatus further calculates a combined positioning result that satisfies 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 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 an increment of position information to be estimated of the vehicle between two adjacent time instants, and the third difference value is a difference of an increment of a moving state of the vehicle between two adjacent time instants.

In the embodiment of the present application, the estimated value of the initial state variable (i.e., the first difference value) of the vehicle may be obtained by equation (2):

wherein,

representing prior information; x ₀ Indicating the state of motion of the vehicle at the starting time.

In the embodiment of the present application, the GNSS information (i.e., the second difference) of the vehicle may be obtained by formula (3):

wherein P represents position information in a motion state to be estimated; p _gnss Represents a GNSS position acquired by GNSS;

representing a posture matrix; l ^b Representing the lever arm values of the antenna in the IMU coordinate system.

In the embodiment of the present application, the difference (i.e., the third difference) of the motion state increment of the vehicle may be obtained by formula (4):

r _{pva_od} ＝r1-r2 (4)

wherein r1 represents the increment of the motion state to be estimated, and r2 represents the increment of the motion state determined by the first combined positioning result.

In a possible embodiment, the above r1, r2 may satisfy the following formula:

wherein (X) _j -X _i ) Representing a difference between the estimated value of the moving state of the vehicle at the j-th time and the estimated value of the moving state of the vehicle at the i-th time; (X) _{j_filter} -X _{i_filter} ) Representing the difference between the value of the vehicle's motion state at the time j and the value of the vehicle's motion state at the time i.

In a possible embodiment, the position information in r1 and r2 may further satisfy the following formula:

wherein (P) _j -P _i ) Representing a difference between a position estimate of a vehicle at a time j and a position estimate of the vehicle at a time i;

representing the speed integral of the vehicle from the i-th time instant to the j-th time instant determined on the basis of the first combined positioning result.

wherein (P) _j -P _i -V _i * dt) represents a difference between a position estimate for the vehicle at a time j and a position estimate for the vehicle at a time i;

indicating the speed integral of the vehicle from time i to time j, regardless of the speed of the vehicle at time i. By the implementation mode, the influence of the speed error of the vehicle at the ith moment on the determination of the integral of the vehicle from the ith moment to the jth moment by using the first combined positioning result 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 estimation value of the vehicle, the increment of the position information to be estimated of the vehicle between two adjacent moments and the difference value of the motion state increment of the vehicle between two adjacent moments to obtain a target combined positioning result.

In a possible embodiment, the positioning device establishes the target constraint condition after obtaining the difference between the initial state variable estimated value of the vehicle, the increment of the position information to be estimated of the vehicle between two adjacent moments and the increment of the motion state of the vehicle between two adjacent moments, and determines the combined positioning result meeting the target constraint condition as the target combined positioning result. Wherein the target constraint condition is that the target constraint condition is the minimum after the first difference, the second difference and the third difference.

In a possible embodiment, the target constraint may be expressed as:

wherein σ _prior Covariance information representing an initial state variable estimate of the vehicle; sigma _gnss Covariance information representing GNSS information of the vehicle; sigma _{pva_od} Covariance information indicating an increment of a moving state of the vehicle.

It should be noted that the target constraint condition may also be other modifications of the above equation (5) or equivalent equations, and this embodiment of the present application is not limited in this respect.

In a possible embodiment, the combined positioning result satisfying the above target constraint condition can be obtained by a non-linear optimization manner (e.g., least squares). For how to obtain the combined positioning result satisfying the target constraint condition through a nonlinear optimization manner, please refer to the prior art, which is not described herein. Of course, the combined positioning result meeting the above target constraint condition may also be obtained in other manners, which is not specifically limited in this embodiment of the application.

By implementing the embodiment of the application, on the basis of utilizing multi-sensor bidirectional processing fusion, when a GNSS signal is interfered, a residual error model is constructed by utilizing the position, speed and attitude of a vehicle so as to carry out global optimization processing on the state variables, and through repeated iterative solution, the optimal solution of all the state variables can be determined, and the correction and optimization of a conventional post-processing result are realized, so that 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 that the speed is constrained by a wheel speed meter and the like, the precision is high, the integral of the speed is adopted to replace the position for position constraint, the initial speed between two adjacent moments can be optimized, and the integral increment of the relative speed is used as the position constraint, so that the post-processing positioning precision is further improved.

The foregoing embodiment mainly explains 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 provided by the present application with reference to specific examples:

r1 and r2 satisfy

In the case of the positioning error, as shown in fig. 3b, the GNSS method, the bidirectional filtering method and the test result of the method are respectively adopted for the shade road with the severe GNSS signal shielding provided by the embodiment of the present application, and as can be known from fig. 3b, the method provided by the present application can obviously reduce the influence of the poor GNSS signal on the positioning error.

R1 and r2 satisfy

In the case of the positioning method, as shown in fig. 3c, as shown in the scenario without GNSS signals (for example, a simulated tunnel scenario), the GNSS method, the bidirectional filtering method, and the result of the test performed by the method in the present application are respectively adopted according to the embodiment of the present application, and as can be known from fig. 3c, the positioning accuracy in the absence of GNSS signals can be significantly improved by the method in the present application.

R1 and r2 satisfy

In the case of the present invention, as shown in fig. 3d, as shown in the scenario without GNSS signals (for example, a simulated tunnel scenario), a result of the test performed by respectively adopting the GNSS method, the bidirectional filtering method, and the method of the present invention is provided, and it can be known from fig. 3d that the positioning accuracy in the absence of GNSS signals can be significantly improved by the method provided by the present invention.

In conclusion, the method provided by the application can correct the motion state of the vehicle through the initial state variable estimation value of the vehicle, the difference value of 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 driving in typical urban road scenes such as urban canyons and GNSS signal sheltering, provides convenience for driving of the vehicle, and can also improve the safety of the vehicle in the driving process.

The method of the embodiments of the present application is described above in detail, and in order to better implement the above-mentioned aspects of the embodiments of the present application, the following also provides related devices for implementing the above-mentioned aspects.

Referring to fig. 4, fig. 4 is a schematic structural diagram of a positioning apparatus provided in an embodiment of the present application, where the positioning apparatus may be an execution main 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 obtaining unit 410, an information determining unit 420, and a correcting unit 430. Wherein,

an obtaining unit 410, configured to obtain a first combined positioning result, where the first combined positioning result indicates vehicle motion states of a vehicle at multiple times, where the motion states include a vehicle position, a vehicle speed, and a vehicle attitude, 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 obtained by a sensor of the vehicle, the third combined positioning result is a result obtained by reverse resolving data obtained by the sensor of the vehicle, and the data includes position information of the vehicle obtained by a GNSS sensor at the multiple times;

an information determining unit 420, configured to obtain, according to the first combined positioning result, initial state variable estimated values of the vehicle, position information of the vehicle at the multiple time instants, and differences between motion state increments of the vehicle, where the initial state variable estimated values of the vehicle are motion states of the vehicle at the initial time instants in the first combined positioning result, and the differences between the motion state increments of the vehicle between two adjacent time instants in the multiple time instants to be estimated and the motion state increments between the two adjacent time instants in the first combined positioning result;

a correcting unit 430, configured to correct the first combined positioning result according to the initial state variable estimation value of the vehicle, the position information of the vehicle at the multiple times, and the difference between the motion state increments of the vehicle, so as to obtain a target combined positioning result.

As an embodiment, the modifying unit 430 is specifically configured to: 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 an initial state value to be estimated of the vehicle and an initial 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 obtained by the GNSS sensor, and the third difference value is the difference between a motion state increment between two adjacent time instants to be estimated of the vehicle and a motion change increment between the two adjacent time instants in the first combined positioning result.

As an embodiment, the increment of the motion state between the two adjacent moments to be estimated by the vehicle is the difference between the estimated value of the motion state of the vehicle at the j time and the estimated value of the motion state of the vehicle at the i time; the motion state increment between the two adjacent moments in the first combined positioning result is the difference value 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 ith time is earlier than the jth time by a preset time length.

As an embodiment, the motion state increment between two adjacent time instants in the plurality of time instants to be estimated includes a position information increment to be estimated; 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, wherein the position information difference value to be estimated is a difference value between a position estimation value of the vehicle at the j time and a position estimation value of the vehicle at the i time; the initial speed integrated value is the speed integrated value of the vehicle from the ith time to the jth time; and the ith time is earlier than the jth time by a preset time length.

As an embodiment, the motion state increment between two adjacent time instants in the plurality of time instants to be estimated includes a position information increment to be estimated; 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, wherein the position information difference value to be estimated is a difference value between a position estimation value of the vehicle at the j time and a position estimation value of the vehicle at the i time; the relative speed integrated value is a speed integration of the vehicle from the i-th time to the j-th time without considering the speed of the vehicle at the i-th time; and the ith time is earlier than the jth time 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-mentioned structure of the positioning device is only an example, and should not constitute a specific limitation, and the various units of the positioning device may be added, reduced or combined as required. In addition, operations and/or functions of each unit in the positioning apparatus are not described herein again for brevity in order to implement the corresponding flow of the method described in fig. 3 a.

Referring to fig. 5, fig. 5 is a schematic structural diagram of another positioning device provided in the embodiments of the present application. As shown in fig. 5, the positioning apparatus 500 includes: a processor 510, a communication interface 520 and a memory 530, said processor 510, communication interface 520 and memory 530 being interconnected by an internal bus 540. It should be understood that the positioning apparatus 500 may be a terminal device or a vehicle-mounted device, and is applied in a vehicle-mounted ethernet.

The processor 510 may be formed by one or more general-purpose processors, such as a Central Processing Unit (CPU), or a combination of a CPU and a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof.

The bus 540 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus 540 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 5, but not only one bus or type of bus.

Memory 530 may include volatile memory (volatile memory), such as Random Access Memory (RAM); the memory 530 may also include a non-volatile memory (non-volatile memory), such as a read-only memory (ROM), a flash memory (flash memory), a Hard Disk Drive (HDD), or a solid-state drive (SSD); memory 530 may also include combinations of the above.

It should be noted that the memory 530 of the positioning apparatus 500 stores computer programs, and the processor 510 executes these computer programs, so as to make the positioning apparatus 500 execute the method in the embodiment shown in fig. 3 a.

An embodiment of the present application further provides a computer-readable storage medium, where the computer-readable storage medium is used to store a computer program, and the computer program enables a positioning apparatus to execute the method in the embodiment shown in fig. 3 a.

Embodiments of the present application further provide a computer program, which is operable to cause an electronic device to perform the method in the embodiment shown in fig. 3 a.

It will be understood that each of the elements and algorithm steps described in connection with the embodiments disclosed in the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware, as will be appreciated by one of ordinary skill in the art. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. 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 would appreciate that the functions described in connection with the various illustrative logical blocks, modules, and algorithm steps disclosed in the various embodiments disclosed herein may be implemented as hardware, software, firmware, or any combination thereof. If implemented in software, the functions described in the various illustrative logical blocks, modules, and steps may be stored on or transmitted over as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. The computer-readable medium may include a computer-readable storage medium, which corresponds to a tangible medium, such as a data storage medium, or any communication medium including a 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. A data storage medium may be any available medium 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 herein. The computer program product may include a computer-readable medium.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. The units described as separate parts may or may not be physically separate, and parts displayed 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 can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by 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

1. A method of positioning, comprising:

acquiring a first combined positioning result; wherein the first combined positioning result indicates vehicle motion states of the vehicle at a plurality of moments, the motion states including vehicle position, vehicle speed, and vehicle attitude; the first combined positioning result is a combined positioning result obtained by fusion filtering of the second combined positioning result and the third combined positioning result; the second combined positioning result is obtained by forward resolving data acquired by a sensor of the vehicle; the third combined positioning result is obtained by reversely resolving data acquired by a sensor of a vehicle, wherein the data comprises position information of the vehicle at a plurality of moments acquired by a Global Navigation Satellite System (GNSS) sensor;

obtaining a starting state variable estimated value of the vehicle, position information of the vehicle at the plurality of moments and a difference value of a motion state increment of the vehicle according to the first combined positioning result; 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; 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 multiple 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 estimation value 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 to obtain a target combined positioning result.

2. The method of claim 1, wherein said modifying the first combined positioning result based on the initial state variable estimate of the vehicle, the position information of the vehicle at the plurality of time instances, and the difference in the motion state increment of the vehicle to obtain a target combined positioning result comprises:

determining the combined positioning result meeting the target constraint condition as the target combined positioning result; 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 an initial state value to be estimated of the vehicle and an initial 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 obtained by the GNSS sensor, and the third difference value is the difference between a motion state increment between the two adjacent moments to be estimated of the vehicle and a motion state change increment between the two adjacent moments in the first combined positioning result.

3. The method according to any one of claims 1-2, wherein the increment of the motion state of the vehicle between the two adjacent time instants to be estimated is the difference between the estimated value of the motion state of the vehicle at the j-th time instant and the estimated value of the motion state of the vehicle at the i-th time instant; the motion state increment between the two adjacent moments in the first combined positioning result is the difference value 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 ith time is earlier than the jth time by a preset time length.

4. The method of any one of claims 1-2, wherein the increment of the motion state between two adjacent time instants in the plurality of time instants to be estimated comprises an increment of position information to be estimated; the increment of the position information to be estimated is a difference value between a difference value of the position to be estimated and an initial speed integral value, wherein the difference value of the position information to be estimated is a difference value between a position estimation value of the vehicle at the j moment and a position estimation value of the vehicle at the i moment; the initial speed integrated value is the speed integrated value of the vehicle from the ith time to the jth time; and the ith moment is earlier than the jth moment by preset time length.

5. The method of any one of claims 1-2, wherein the increment of the motion state between two adjacent time instants in the plurality of time instants to be estimated comprises an increment of position information to be estimated; 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, wherein the position information difference value to be estimated is a difference value between a position estimation value of the vehicle at the j time and a position estimation value of the vehicle at the i time; the relative speed integrated value is a speed integration of the vehicle from the i-th time to the j-th time without considering the speed of the vehicle at the i-th time; and the ith moment is earlier than the jth moment by preset time length.

6. The method of any one of claims 1-5, wherein the sensors of the vehicle comprise: at least one of an inertial measurement unit IMU, a wheel speed meter, and a lidar.

7. A positioning device, comprising:

the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a first combined positioning result, the first combined positioning result indicates a vehicle motion state of a vehicle at a plurality of moments, the motion state comprises a vehicle position, a vehicle speed and a vehicle attitude, 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 reverse resolving data acquired by the sensor of the vehicle, and the data comprises 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, initial state variable estimation values of the vehicle, position information of the vehicle at the multiple times, and differences between motion state increments of the vehicle, where the initial state variable estimation values of the vehicle are motion states of the vehicle at initial times in the first combined positioning result, and the differences between the motion state increments of the vehicle between two adjacent times in the multiple times to be estimated and the motion state increments between the two adjacent times in the first combined positioning result;

and the correcting unit is used for correcting the first combined positioning result according to the initial state variable estimation value 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 to obtain a target combined positioning result.

8. The apparatus according to claim 7, wherein the modification unit is specifically configured to:

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 an initial state value to be estimated of the vehicle and an initial 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 the position information of the vehicle obtained by the GNSS sensor, and the third difference value is the difference between a motion state increment between two adjacent time instants to be estimated of the vehicle and a motion change increment between the two adjacent time instants in the first combined positioning result.

9. The apparatus according to claim 7 or 8, characterized in that the increment of the moving state between the two adjacent time instants at which the vehicle is to be estimated is a difference between the estimated value of the moving state of the vehicle at the j-th time instant and the estimated value of the moving state of the vehicle at the i-th time instant; the motion state increment between the two adjacent moments in the first combined positioning result is the difference value 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 ith time is earlier than the jth time by a preset time length.

10. The apparatus of claim 7 or 8, wherein the increment of the motion state between two adjacent time instants in the plurality of time instants to be estimated comprises an increment of position information to be estimated; the increment of the position information to be estimated is a difference value between a difference value of the position to be estimated and an initial speed integral value, wherein the difference value of the position information to be estimated is a difference value between a position estimation value of the vehicle at the j moment and a position estimation value of the vehicle at the i moment; the initial speed integrated value is the speed integrated value of the vehicle from the ith time to the jth time; and the ith time is earlier than the jth time by a preset time length.

11. The apparatus of claim 7 or 8, wherein the increment of motion state between two adjacent time instants in the plurality of time instants to be estimated comprises an increment of position information to be estimated; the increment of the position information to be estimated is a difference value between a difference value of the position to be estimated and a relative speed integral value, wherein the difference value of the position information to be estimated is a difference value between a position estimation value of the vehicle at the j moment and a position estimation value of the vehicle at the i moment; the relative speed integrated value is a speed integration of the vehicle from the i-th time to the j-th time without considering the speed of the vehicle at the i-th time; and the ith time is earlier than the jth time by a preset time length.

12. The apparatus of any one of claims 7-11, 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, wherein execution of computer instructions stored by the memory causes the positioning device to perform the method of any of claims 1-6.

14. A vehicle, characterized in that the vehicle comprises a positioning device according to any 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 of any one of claims 1-6.