CN115406439A - Vehicle positioning method, system, device and nonvolatile storage medium - Google Patents
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
- G01C21/1652—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments with ranging devices, e.g. LIDAR or RADAR
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
- G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
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- 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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Abstract
The application discloses a vehicle positioning method, a system, a device and a nonvolatile storage medium. Wherein, the method comprises the following steps: acquiring radar data, IMU data and UWB data, wherein the radar data, the IMU data and the UWB data are information data of a target vehicle and the surrounding environment of the target vehicle; determining first positioning data of the target vehicle according to the radar data and the IMU data; determining second positioning data of the target vehicle according to the UWB data; and determining confidence degrees of the first positioning data and the second positioning data, and fusing the first positioning data and the second positioning data according to the confidence degrees to obtain the target positioning data of the target vehicle. The method and the device solve the technical problems that in the prior art, vehicle positioning is mostly carried out by depending on satellite positioning signals, vehicle positioning cannot be carried out in the shielded environment of the satellite positioning signals, and positioning accuracy is poor by using a single type of positioning mode.
Description
Technical Field
The application relates to the technical field of automatic driving positioning, in particular to a vehicle positioning method, a vehicle positioning system, a vehicle positioning device and a non-volatile storage medium.
Background
With the rapid development of automobile electronization, interconnection and intellectualization, automatic driving as an important technology for solving future transportation has become a research hotspot and an important development direction of the automobile industry in the global scope. One of the core problems of automatic driving is positioning, and other automatic driving tasks such as path planning and decision control can be normally completed on the basis of the positioning. Most automatic driving vehicles in the prior art use a single type of positioning mode to position, but the problem of poor positioning accuracy exists in the single type of positioning mode, and the prior art mainly comprises the following modes:
currently, the most widely used Advanced Driver Assistance System (ADAS) industry is Global Navigation Satellite System (GNSS) technology. In an environment without shielding, the GNSS can provide information such as a three-dimensional position, a speed, an attitude and the like for the vehicle all the day, but in a closed traffic environment, because a satellite signal is shielded, the GNSS has a long-term failure phenomenon, and thus continuous and reliable positioning cannot be realized.
An Inertial Measurement Unit (IMU) is an autonomous navigation system which does not depend on external information and does not emit energy outwards, and position, speed and attitude information of a carrier can be obtained by measuring acceleration and angular velocity information of the carrier and carrying out integral operation. Due to their complementarity with GNSS, they are often integrated with GNSS to improve the accuracy of navigation. Due to the nature of inertial sensors, however, the IMU can accumulate large errors over long periods of independent operation, which can result in a dramatic drop in positioning accuracy.
In the case of a satellite signal being blocked, the problem can also be solved by deploying a certain number of ultra wideband technology (UWB) base stations, but if the area of the blocked area is large or is dispersed, the equipment and engineering cost required for realizing full coverage of the UWB signal in the area will be greatly increased.
In recent years, synchronous positioning and mapping (SLAM) technology has been developed in a scene where GNSS signals are blocked. One of the sensors commonly used in SLAM technology is Lidar (Lidar), which has a wide viewing angle, robustness in low light environments, and the ability to capture environmental details from a distance. However, the lidar SLAM only estimates the pose in the local frame, without a global frame, which means that the pose estimation is locally accurate but is prone to accumulate errors over time. In addition, when the vehicle travels to an area with fewer feature points, the lidar SLAM may also cause a decrease in positioning accuracy due to sparse point cloud.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the application provides a vehicle positioning method, a vehicle positioning system, a vehicle positioning device and a nonvolatile storage medium, and at least solves the technical problems that in the prior art, vehicle positioning cannot be performed in an environment where satellite positioning signals are blocked due to the fact that most of the vehicle positioning is performed by means of satellite positioning signals, and positioning accuracy is poor due to the fact that a single type of positioning mode is used.
According to an aspect of an embodiment of the present application, there is provided a vehicle positioning method including: acquiring radar data, IMU data and UWB data, wherein the radar data, the IMU data and the UWB data are information data of a target vehicle and the surrounding environment of the target vehicle; determining first positioning data of the target vehicle according to the radar data and the IMU data; determining second positioning data of the target vehicle according to the UWB data; and determining confidence degrees of the first positioning data and the second positioning data, and fusing the first positioning data and the second positioning data according to the confidence degrees to obtain the target positioning data of the target vehicle.
Optionally, determining confidence degrees of the first positioning data and the second positioning data, and fusing the first positioning data and the second positioning data according to the confidence degrees to obtain the target positioning data of the target vehicle includes: calculating a first difference value between first positioning data and second positioning data at a first moment and a second difference value between the second positioning data at the first moment and a second moment, wherein the first moment is any moment in the vehicle positioning process, and the second moment is a next moment adjacent to the first moment; comparing the first difference value and the second difference value with a preset threshold value to obtain a first confidence coefficient of the first positioning data and a second confidence coefficient of the second positioning data; and fusing the first positioning data and the second positioning data according to the magnitude relation between the first confidence coefficient and the second confidence coefficient to obtain the target positioning data of the target vehicle.
Optionally, the fusing the first positioning data and the second positioning data according to the magnitude relationship between the first confidence degree and the second confidence degree, and obtaining the target positioning data of the target vehicle includes: under the condition that the first confidence coefficient is not greater than the second confidence coefficient, carrying out recursion operation on the first positioning data and the second positioning data by using a linear filter to obtain target positioning data; and under the condition that the first confidence coefficient is greater than the second confidence coefficient, determining the first positioning data as the target positioning data.
Optionally, the radar data includes point cloud data of an environment around the target vehicle, and determining the first positioning data of the target vehicle according to the radar data and the IMU data includes: establishing a first space coordinate system; extracting feature information in the point cloud data, and performing feature matching on feature information of a preset number of target moments and the same coordinate position to generate a point cloud map, wherein the target moments are any moments in the vehicle positioning process; determining first initial positioning data of a target vehicle in a point cloud map; and extracting pose data of the target vehicle according to the IMU data, and performing pose adjustment on the first initial positioning data according to the pose data to obtain first positioning data.
Optionally, determining initial positioning data of the target vehicle in the point cloud map comprises: according to the radar data, resolving the relative positions of the target vehicles at different moments, and matching the relative positions with the point cloud map in real time to obtain first initial positioning data.
Optionally, determining second positioning data of the target vehicle from the UWB data comprises: establishing a second space coordinate system by taking the position of the UWB module as a coordinate reference point; in a second space coordinate system, resolving UWB data to obtain second initial positioning data; and performing space coordinate conversion on the second initial positioning data to obtain second positioning data in a first space coordinate system, wherein the first space coordinate system is a coordinate system where the first positioning data is located.
There is also provided, in accordance with another aspect of an embodiment of the present application, a vehicle positioning system, including: the system comprises a radar module, an IMU module, a UWB module and a processor, wherein the radar module is fixed on a target vehicle, is connected with the processor and is used for acquiring radar data; the IMU module is fixed on the target vehicle, connected with the processor and used for acquiring IMU data; the UWB module is fixed in the surrounding environment of the target vehicle, connected with the processor and used for acquiring IMU data; the processor is used for determining first positioning data and second positioning data according to the radar data, the IMU data and the UWB data, and fusing the first positioning data and the second positioning data according to the confidence degrees of the first positioning data and the second positioning data to obtain target positioning data of the target vehicle.
According to still another aspect of an embodiment of the present application, there is also provided a vehicle positioning apparatus including: the system comprises a data acquisition module, a data acquisition module and a data processing module, wherein the data acquisition module is used for acquiring radar data, IMU data and UWB data, and the radar data, the IMU data and the UWB data are all information data of a target vehicle and the surrounding environment of the target vehicle; the first positioning module is used for determining first positioning data of the target vehicle according to the radar data and the IMU data; the second positioning module is used for determining second positioning data of the target vehicle according to the UWB data; and the fusion module is used for determining the confidence degrees of the first positioning data and the second positioning data, and fusing the first positioning data and the second positioning data according to the confidence degrees to obtain the target positioning data of the target vehicle.
According to yet another aspect of the embodiments of the present application, there is also provided a vehicle comprising a processor for executing a program, wherein the program when executed performs a vehicle positioning method.
According to still another aspect of the embodiments of the present application, there is also provided a non-volatile storage medium including a stored program, wherein the apparatus in which the non-volatile storage medium is controlled to execute the vehicle localization method when the program is executed.
In the embodiment of the application, radar data, IMU data and UWB data are obtained, wherein the radar data, the IMU data and the UWB data are information data of a target vehicle and the surrounding environment of the target vehicle; determining first positioning data of the target vehicle according to the radar data and the IMU data; determining second positioning data of the target vehicle according to the UWB data; confirm the confidence coefficient of first positioning data and second positioning data, and fuse first positioning data and second positioning data according to the confidence coefficient, obtain target vehicle's object location data's mode, through the positioning technology with laser radar, IMU and UWB field end signal fusion, the promotion location effect has been reached, real-time, it is stable, and the purpose of accurate discernment vehicle position, and then solved because prior art relies on satellite positioning signal to carry out vehicle positioning mostly, cause and can't carry out vehicle positioning under the environment that satellite positioning signal is sheltered from, and there is the poor technical problem of location accuracy in the positioning mode that uses single kind.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic diagram of a process flow of a vehicle positioning method according to an embodiment of the application;
FIG. 2 is a schematic diagram of a flow framework of a vehicle positioning method based on fusion of three positioning sources, namely a lidar, an IMU and a UWB, provided by an embodiment of the application;
FIG. 3 is a schematic diagram of a deployment scenario of a UWB base station provided according to an embodiment of the application;
FIG. 4 is a schematic structural diagram of a vehicle positioning system provided in accordance with an embodiment of the present application;
FIG. 5 is a schematic structural diagram of a vehicle positioning device provided in accordance with an embodiment of the present invention;
fig. 6 is a hardware structure block diagram of a computer terminal (or an electronic device) in a vehicle for implementing a method for vehicle positioning according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In order to facilitate the understanding of the embodiments of the present application by those skilled in the art, some technical terms or terms related to the embodiments of the present application will be explained as follows:
global Positioning System (GPS): the new generation space satellite navigation positioning system is developed by the United states of land, sea, air and the three military.
Real-time kinematic (RTK) carrier-phase differential technique: the method is a difference method for processing the observed quantity of the carrier phases of two measuring stations in real time, and the carrier phases acquired by a reference station are sent to a user receiver for calculating the difference and clearing coordinates.
Advanced Driving Assistance System (Advanced Driving Assistance System, ADAS): various sensors (millimeter wave radar, laser radar, single/binocular camera and satellite navigation) arranged on the automobile are utilized to sense the surrounding environment at any time in the driving process of the automobile, collect data, identify, detect and track static and dynamic objects, and calculate and analyze the system by combining navigation map data, so that a driver can perceive possible dangers in advance, and the comfort and the safety of automobile driving are effectively improved.
Localization and Mapping in time (SLAM): the robot starts to move from an unknown position in an unknown environment, self-positioning is carried out according to the position and the map in the moving process, and meanwhile, an incremental map is built on the basis of self-positioning, so that the autonomous positioning and navigation of the robot are realized.
Inertial Measurement Unit (IMU): for measuring the three-axis attitude angle (or angular velocity) and acceleration of the object.
Ultra Wide Band (UWB): the wireless carrier communication technology adopts a nanosecond non-sine wave narrow pulse to transmit data instead of a sine carrier, so that the occupied frequency spectrum range is wide.
WGS-84 Coordinate System (World geographic System-1984 Coordinate System): is a geocentric coordinate system adopted internationally.
Global Navigation Satellite System (GNSS): gnss positioning is an observation that uses pseudoranges, ephemeris, satellite transmit times, etc. from a set of satellites, while the user clock error must also be known. The global navigation satellite system is a space-based radio navigation positioning system that can provide users with all-weather three-dimensional coordinates and speed and time information at any location on the earth's surface or in near-earth space.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
In accordance with an embodiment of the present application, there is provided a method embodiment of vehicle localization, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a schematic diagram of a flow of a method for locating a vehicle according to an embodiment of the present application, where the method includes the following steps, as shown in fig. 1:
step S102, radar data, IMU data and UWB data are obtained, wherein the radar data, the IMU data and the UWB data are all information data of a target vehicle and the surrounding environment of the target vehicle;
in the embodiment, the radar data is information data of a target vehicle and the surrounding environment of the target vehicle, which are acquired by a laser radar SLAM positioning module, wherein the laser radar adopts a vehicle-mounted solid-state laser radar, specifically, the laser radar adopts multi-solid-state laser radar for splicing, and the radar SLAM provides 10Hz real-time positioning; the IMU data is information data of a target vehicle and the surrounding environment of the target vehicle, which are acquired by an IMU module, and the IMU module is fixed on the target vehicle and provides a relative positioning source of 100 Hz; UWB data is the information data of target vehicle and target vehicle surrounding environment that UWB module gathered, and UWB location module is as the field end location, fixes in the parking building scene, can provide stable location output when the vehicle drives into the parking scene, as the basis that fuses the location.
In this embodiment, a target vehicle is positioned by a positioning method based on fusion of three positioning sources, namely, a laser radar, an IMU and a UWB, and fig. 2 is a schematic diagram of a flow framework of a vehicle positioning method based on fusion of three positioning sources, namely, a laser radar, an IMU and a UWB, according to an embodiment of the present application, as shown in fig. 2, wherein a laser radar and an IMU module form a vehicle-end positioning source, and a UWB module is used as a field-end positioning source, so that positioning accuracy is increased by combining the positioning sources of a vehicle end and a field end.
Step S104, determining first positioning data of the target vehicle according to the radar data and the IMU data;
in some embodiments of the present application, the radar data includes point cloud data of an environment surrounding the target vehicle, and determining the first positioning data of the target vehicle based on the radar data and the IMU data includes: establishing a first space coordinate system; extracting feature information in the point cloud data, and performing feature matching on feature information of the same coordinate position at preset number of target moments to generate a point cloud map, wherein the target moments are any moments in the vehicle positioning process; determining first initial positioning data of a target vehicle in a point cloud map; and extracting pose data of the target vehicle according to the IMU data, and performing pose adjustment on the first initial positioning data according to the pose data to obtain first positioning data.
In some embodiments of the present application, determining initial positioning data of the target vehicle in the point cloud map comprises: according to the radar data, resolving the relative positions of the target vehicles at different moments, and matching the relative positions with a point cloud map in real time to obtain first initial positioning data.
In this embodiment, 5 solid-state laser radars are adopted as positioning sources, and are respectively deployed above a roof, point cloud stitching is performed first, and a 360-degree angle of view is formed by 5 solid-state laser radars. And the spliced radar point cloud is used as the input of the SLAM positioning module. And meanwhile, the IMU is deployed in the middle of a trunk of the vehicle, and IMU data is used as supplementary input of radar SLAM positioning.
Specifically, a radar SLAM module needs to establish a map and then match, firstly, the point cloud characteristics of the solid-state laser radar are extracted, then in the map establishing process, a target vehicle keeps the vehicle speed of 10km/h and slowly establishes a map from an entrance to an exit, a line-surface structure is adopted for point cloud characteristic matching, a point cloud map is generated through loop detection, and finally, real-time matching is carried out according to the point cloud map established in advance to obtain 10Hz positioning information, namely the first initial positioning data. Meanwhile, the SLAM positioning result is compensated by using the 100Hz positioning output (namely the pose data) of the IMU, and the final positioning of the vehicle end, namely the first positioning data, is obtained.
Step S106, determining second positioning data of the target vehicle according to the UWB data;
in some embodiments of the present application, determining second positioning data of the target vehicle from the UWB data comprises: establishing a second space coordinate system by taking the position of the UWB module as a coordinate reference point; in a second space coordinate system, resolving UWB data to obtain second initial positioning data; and performing space coordinate conversion on the second initial positioning data to obtain second positioning data in a first space coordinate system, wherein the first space coordinate system is a coordinate system where the first positioning data is located.
In the embodiment, the UWB module comprises a plurality of UWB base stations, UWB is used as a positioning source, the transverse and longitudinal precision of the positioning source can reach 20cm, and the cost is controllable. UWB base stations are arranged in a parking scene, each UWB base station is spaced by 20 meters, fig. 3 is a schematic diagram of the deployment situation of the UWB base stations provided by the embodiment of the application, as shown in fig. 3, UWB indoor high-precision positioning is used for carrying out accurate positioning in a complex parking scene, and the UWB indoor high-precision positioning method has the resolving capability of resisting multipath and narrow-band interference.
Specifically, the target vehicle receives 4 or more UWB base station signals closest to the target vehicle to acquire the UWB data, performs position calculation by using a positioning engine to obtain the second initial positioning data, and converts a calculated position coordinate system into a coordinate system set by a user (i.e., the first spatial coordinate system) to obtain the second positioning data. In this embodiment, the first space coordinate system is a WGS-84 coordinate system.
And S108, determining confidence degrees of the first positioning data and the second positioning data, and fusing the first positioning data and the second positioning data according to the confidence degrees to obtain the target positioning data of the target vehicle.
In some embodiments of the present application, determining confidence degrees of the first positioning data and the second positioning data, and fusing the first positioning data and the second positioning data according to the confidence degrees, to obtain the target positioning data of the target vehicle includes: calculating a first difference value between first positioning data and second positioning data at a first moment and a second difference value between the second positioning data at the first moment and a second moment, wherein the first moment is any moment in the vehicle positioning process, and the second moment is a next moment adjacent to the first moment; comparing the first difference value and the second difference value with a preset threshold value to obtain a first confidence coefficient of the first positioning data and a second confidence coefficient of the second positioning data; and fusing the first positioning data and the second positioning data according to the magnitude relation between the first confidence coefficient and the second confidence coefficient to obtain the target positioning data of the target vehicle.
In this embodiment, the UWB signal quality and the deviation from the vehicle-end positioning are monitored in real time, that is, the first difference is obtained, and meanwhile, the quality of the UWB positioning signal is determined according to the second difference, if the UWB positioning signal jumps by more than ± 15cm, it is determined that the UWB positioning signal is not available, and the positioning error between the vehicle end and the field end should be less than 20cm under normal conditions. And calculating comprehensive confidence degrees according to the first difference value and the second difference value, wherein the comprehensive confidence degrees comprise the first confidence degree of the first positioning data and the second confidence degree of the second positioning data.
In some embodiments of the present application, the fusing the first positioning data and the second positioning data according to a magnitude relationship between the first confidence and the second confidence, and obtaining the target positioning data of the target vehicle includes: under the condition that the first confidence coefficient is not greater than the second confidence coefficient, carrying out recursion operation on the first positioning data and the second positioning data by using a linear filter to obtain target positioning data; and under the condition that the first confidence coefficient is greater than the second confidence coefficient, determining the first positioning data as the target positioning data.
In this embodiment, the deviation of real-time supervision UWB signal quality and car end location data, UWB effective position data are as the correction data of car end location data, and car end data when UWB location data exist and shelter from when sheltering from, realize the stable effective output of indoor location true value.
Specifically, when the confidence coefficient (i.e., the second confidence coefficient) of the UWB positioning is high, the system outputs the accurate position of the UWB, and simultaneously, the UWB effective position data is used as the correction data of the vehicle-end positioning, and the two are fused by using a kalman filter algorithm (i.e., the linear filter).
Specifically, the states of the kalman filter are a translation error, a rotation angle error, and a gyroscope offset error of the IMU, and mathematically, a model of the system is as shown in formula (1):
where Δ t is the integration time of the system, ω m And omega b Respectively representing the measured value of angular velocity and the gyroscope bias. R denotes the rotation matrix generated by the current rotation, δ p t+1 Indicating the position error at time t +1, δ θ t+1 Represents the angular error at time t +1, δ b t+1 Indicating the offset error, δ p, at time t +1 t Indicating the position error at time t, δ θ t Representing the angle error at time t, δ b t Representing the offset error at time t, v x For speed, I is the identity matrix.
The three states respectively correspond to a translation error, an error of a rotation angle, and a gyroscope bias error of the IMU, as shown in equation (2):
wherein v is x 、v y 、v z The components of the velocity v in the x, y, z directions, respectively.
Let the state transition matrix in equation (1) be F, then the covariance matrix P update of the state follows the mathematical process of equations (3) - (6) below:
P＝FPF T (3)
K＝PH T (HPH T +V) -1 (4)
δx＝K(y-h(x)) (5)
P＝(I-KH T )P (6)
wherein, K is Kalman gain, y is measured value, V is observation error, x is state value, function H is transformation relation between state value and measured value, H is Jacobian matrix of function H about state, and I is unit matrix.
Through the steps, radar data, IMU data and UWB data are obtained, wherein the radar data, the IMU data and the UWB data are information data of the target vehicle and the surrounding environment of the target vehicle; determining first positioning data of the target vehicle according to the radar data and the IMU data; determining second positioning data of the target vehicle according to the UWB data; confirm the confidence coefficient of first locating data and second locating data to fuse first locating data and second locating data according to the confidence coefficient, obtain target vehicle's object location data, reached and promoted location effect, real-time, stable, and the accurate purpose of discerning the vehicle position, and then solved because prior art relies on satellite positioning signal to carry out vehicle location mostly, cause and can't carry out vehicle location under the environment that satellite positioning signal is sheltered from, and there is the poor technical problem of location accuracy in the positioning mode who uses single kind.
Example 2
According to an embodiment of the invention, there is also provided an embodiment of a vehicle positioning system. Fig. 4 is a schematic structural diagram of a vehicle positioning system provided in an embodiment of the invention. As shown in fig. 4, the system includes a radar module, an IMU module, a UWB module, and a processor:
the radar module 40 is fixed on the target vehicle, connected with the processor and used for acquiring radar data;
the IMU module 42 is fixed on the target vehicle, connected with the processor and used for acquiring IMU data;
a UWB module 44 fixed in the environment surrounding the target vehicle, connected to the processor, for acquiring IMU data;
and the processor 46 is configured to determine the first positioning data and the second positioning data according to the radar data, the IMU data and the UWB data, and fuse the first positioning data and the second positioning data according to confidence degrees of the first positioning data and the second positioning data to obtain target positioning data of the target vehicle.
It should be noted that the vehicle positioning system provided in this embodiment may be used to execute the vehicle positioning method shown in fig. 1, and therefore, the explanation about the vehicle positioning method is also applicable to the embodiment of this application, and is not repeated herein.
According to the embodiment of the invention, the embodiment of the vehicle positioning device is also provided. Fig. 5 is a schematic structural diagram of a vehicle positioning device provided in an embodiment of the invention. As shown in fig. 5, the apparatus includes:
the data acquisition module 50 is configured to acquire radar data, IMU data, and UWB data, where the radar data, IMU data, and UWB data are all information data of the target vehicle and an environment around the target vehicle;
a first positioning module 52, configured to determine first positioning data of the target vehicle according to the radar data and the IMU data;
a second positioning module 54, configured to determine second positioning data of the target vehicle according to the UWB data;
and the fusion module 56 is configured to determine confidence degrees of the first positioning data and the second positioning data, and fuse the first positioning data and the second positioning data according to the confidence degrees to obtain the target positioning data of the target vehicle.
It should be noted that the vehicle positioning device provided in the present embodiment can be used to execute the vehicle positioning method shown in fig. 1, and therefore, the explanation of the vehicle positioning method is also applicable to the embodiment of the present application, and is not repeated herein.
There is also provided, in accordance with an embodiment of the present invention, an embodiment of a vehicle for implementing a method for vehicle localization, the vehicle including a computer terminal (or electronic device). Fig. 6 is a block diagram of a hardware structure of a computer terminal (or electronic device) in a vehicle for implementing a method for vehicle positioning according to an embodiment of the present invention. As shown in fig. 6, the computer terminal 60 (or electronic device 60) may include one or more processors (shown here as 602a, 602b, … …,602 n) (which may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 604 for storing data, and a transmission module 606 for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 6 is only an illustration and is not intended to limit the structure of the electronic device. For example, the computer terminal 60 may also include more or fewer components than shown in FIG. 6, or have a different configuration than shown in FIG. 6.
It should be noted that the one or more processors and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuit may be a single stand-alone processing module, or incorporated in whole or in part into any of the other elements in the computer terminal 60 (or electronic device). As referred to in the embodiments of the application, the data processing circuit acts as a processor control (e.g. selection of a variable resistance termination path connected to the interface).
The memory 604 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the vehicle positioning method in the embodiment of the present application, and the processor executes various functional applications and data processing by executing the software programs and modules stored in the memory 604, so as to implement the vehicle positioning method described above. The memory 604 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 604 may further include memory located remotely from the processor, which may be connected to the computer terminal 60 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission module 606 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 60. In one example, the transmission device 606 includes a Network adapter (NIC) that can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 606 can be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computer terminal 60 (or electronic device).
It should be noted that, in some alternative embodiments, the computer terminal (or electronic device) in the vehicle shown in fig. 6 may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that fig. 6 is only one example of a particular specific example and is intended to illustrate the types of components that may be present in the computer device (or electronic device) described above.
It should be noted that the computer terminal (or the electronic device) in the vehicle for vehicle positioning shown in fig. 6 is used for executing the method for vehicle positioning shown in fig. 1, and therefore the explanation of the method for vehicle positioning described above is also applicable to the electronic device in the vehicle for vehicle positioning, and is not repeated here.
According to an embodiment of the present application, there is also provided an embodiment of a non-volatile storage medium including a stored program, wherein the program, when executed, controls an apparatus in which the non-volatile storage medium is located to perform the following method of vehicle localization: acquiring radar data, IMU data and UWB data, wherein the radar data, the IMU data and the UWB data are information data of a target vehicle and the surrounding environment of the target vehicle; determining first positioning data of the target vehicle according to the radar data and the IMU data; determining second positioning data of the target vehicle according to the UWB data; and determining the confidence degrees of the first positioning data and the second positioning data, and fusing the first positioning data and the second positioning data according to the confidence degrees to obtain the target positioning data of the target vehicle.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple 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, units or modules, and may be in an electrical 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 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 integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes 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: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A vehicle positioning method, characterized by comprising:
acquiring radar data, IMU data and UWB data, wherein the radar data, the IMU data and the UWB data are information data of a target vehicle and the surrounding environment of the target vehicle;
determining first positioning data of the target vehicle according to the radar data and the IMU data;
determining second positioning data of the target vehicle according to the UWB data;
and determining confidence degrees of the first positioning data and the second positioning data, and fusing the first positioning data and the second positioning data according to the confidence degrees to obtain the target positioning data of the target vehicle.
2. The vehicle positioning method according to claim 1, wherein determining confidence degrees of the first positioning data and the second positioning data, and fusing the first positioning data and the second positioning data according to the confidence degrees to obtain the target positioning data of the target vehicle comprises:
calculating a first difference value between the first positioning data and the second positioning data at a first moment and a second difference value between the second positioning data at the first moment and a second moment, wherein the first moment is any moment in a vehicle positioning process, and the second moment is a next moment adjacent to the first moment;
comparing the first difference and the second difference with a preset threshold to obtain a first confidence coefficient of the first positioning data and a second confidence coefficient of the second positioning data;
and fusing the first positioning data and the second positioning data according to the magnitude relation between the first confidence coefficient and the second confidence coefficient to obtain the target positioning data of the target vehicle.
3. The vehicle positioning method according to claim 2, wherein the fusing the first positioning data and the second positioning data according to a magnitude relationship between the first confidence degree and the second confidence degree to obtain the target positioning data of the target vehicle comprises:
under the condition that the first confidence coefficient is not greater than the second confidence coefficient, carrying out recursion operation on the first positioning data and the second positioning data by using a linear filter to obtain the target positioning data;
and determining the first positioning data as the object positioning data when the first confidence is greater than the second confidence.
4. The vehicle localization method of claim 1, wherein the radar data comprises point cloud data of an environment surrounding the target vehicle, and wherein determining the first localization data of the target vehicle from the radar data and the IMU data comprises:
establishing a first space coordinate system;
extracting feature information in the point cloud data, and performing feature matching on the feature information of the same coordinate position at preset number of target moments to generate a point cloud map, wherein the target moments are any moments in the vehicle positioning process;
determining first initial positioning data of the target vehicle in the point cloud map;
and extracting pose data of the target vehicle according to the IMU data, and performing pose adjustment on the first initial positioning data according to the pose data to obtain the first positioning data.
5. The vehicle localization method of claim 4, wherein determining initial localization data of the target vehicle in the point cloud map comprises:
and resolving the relative position of the target vehicle at different moments according to the radar data, and matching the relative position with the point cloud map in real time to obtain the first initial positioning data.
6. The vehicle positioning method of claim 1, wherein determining second positioning data of the target vehicle from the UWB data comprises:
establishing a second space coordinate system by taking the position of the UWB module as a coordinate reference point;
resolving the UWB data in the second space coordinate system to obtain second initial positioning data;
and performing space coordinate conversion on the second initial positioning data to obtain the second positioning data in a first space coordinate system, wherein the first space coordinate system is a coordinate system where the first positioning data is located.
7. A vehicle positioning system, comprising: a radar module, an IMU module, a UWB module, and a processor, wherein,
the radar module is fixed on a target vehicle, connected with the processor and used for acquiring radar data;
the IMU module is fixed on the target vehicle, is connected with the processor and is used for acquiring IMU data;
the UWB module is fixed in the surrounding environment of the target vehicle, is connected with the processor and is used for acquiring IMU data;
the processor is used for determining first positioning data and second positioning data according to the radar data, the IMU data and the UWB data, and fusing the first positioning data and the second positioning data according to confidence degrees of the first positioning data and the second positioning data to obtain target positioning data of the target vehicle.
8. A vehicle positioning device, comprising:
the system comprises a data acquisition module, a data acquisition module and a data processing module, wherein the data acquisition module is used for acquiring radar data, IMU data and UWB data, and the radar data, the IMU data and the UWB data are all information data of a target vehicle and the surrounding environment of the target vehicle;
the first positioning module is used for determining first positioning data of the target vehicle according to the radar data and the IMU data;
the second positioning module is used for determining second positioning data of the target vehicle according to the UWB data;
and the fusion module is used for determining the confidence degrees of the first positioning data and the second positioning data, and fusing the first positioning data and the second positioning data according to the confidence degrees to obtain the target positioning data of the target vehicle.
9. A vehicle comprising a processor, wherein the processor is configured to run a program, wherein the program is configured to perform the vehicle localization method of any one of claims 1-6 when executed.
10. A non-volatile storage medium, comprising a stored program, wherein the program, when executed, controls a device in which the non-volatile storage medium is located to perform the vehicle localization method according to any one of claims 1 to 6.
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