CN119164408A - Unmanned vehicle positioning method, system, terminal and storage medium - Google Patents
Unmanned vehicle positioning method, system, terminal and storage medium Download PDFInfo
- Publication number
- CN119164408A CN119164408A CN202411658533.9A CN202411658533A CN119164408A CN 119164408 A CN119164408 A CN 119164408A CN 202411658533 A CN202411658533 A CN 202411658533A CN 119164408 A CN119164408 A CN 119164408A
- Authority
- CN
- China
- Prior art keywords
- noise
- covariance matrix
- data
- observation
- positioning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
- G01C21/1652—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments with ranging devices, e.g. LIDAR or RADAR
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/183—Compensation of inertial measurements, e.g. for temperature effects
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/251—Fusion techniques of input or preprocessed data
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Automation & Control Theory (AREA)
- Mathematical Physics (AREA)
- Mathematical Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Engineering & Computer Science (AREA)
- Computational Mathematics (AREA)
- Electromagnetism (AREA)
- Operations Research (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Algebra (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention relates to the technical field of unmanned vehicles, in particular to a method, a system, a terminal and a storage medium for locating an unmanned vehicle, which comprise the steps of acquiring vehicle locating data and system noise data acquired by a plurality of sensors; the method comprises the steps of constructing a prediction equation and an observation equation of vehicle positioning based on vehicle positioning data and system noise data, constructing a covariance matrix based on the system noise data, calculating Kalman gain by using a Kalman algorithm based on the prediction equation, the observation equation and the covariance matrix of the vehicle positioning, and obtaining an optimal estimated value at the current moment. The invention effectively improves the positioning precision of the vehicle in a complex environment.
Description
Technical Field
The invention belongs to the technical field of unmanned vehicles, and particularly relates to a method, a system, a terminal and a storage medium for locating an unmanned vehicle.
Background
With the rapid development of automatic driving technology, vehicle positioning technology has become an important foundation for realizing unmanned driving. Accurate vehicle positioning is a precondition to ensure safe travel and correct decision making of unmanned vehicles. Currently, the commonly used vehicle positioning technology mainly relies on Global Navigation Satellite Systems (GNSS), inertial navigation systems (IMU) and other sensors (e.g. lidar, cameras). However, complexities in urban environments, such as high-rise occlusions, failure of satellite signals, and bursty traffic conditions, present significant challenges for vehicle positioning.
The existing vehicle positioning technology has a certain maturity, but has the main defects that firstly, single sensor dependence is strong, and most of unmanned vehicles are dependent on single GNSS (such as GPS and Beidou) as a main positioning source. However, GNSS is easily interfered by factors such as urban tall buildings, atmospheric conditions, and the like, and particularly in urban canyons, tunnels, and other areas, satellite signals may fail completely, thereby causing a decrease in vehicle positioning accuracy and even positioning failure. Second, the inertial navigation system (IMU) can provide motion information of the vehicle in a short time, but the error of the IMU can accumulate (drift error) with time, especially in the case of lack of GNSS signals, the accuracy of the IMU can be rapidly reduced. Third, the environmental awareness is limited in that a single GNSS or IMU is not able to accurately perceive the dynamic environment surrounding the vehicle, such as pedestrians, vehicles, traffic signals, etc. While lidar and cameras may provide rich environmental information, these sensors themselves may also be disturbed by factors such as light, weather, shadows, etc. in certain scenarios. Fourth, the existing single sensor positioning scheme is not high in positioning precision and poor in robustness, and the existing single sensor positioning scheme is often insufficient in positioning precision and poor in system robustness in complex environments, so that the conventional single sensor positioning scheme cannot cope with changes of various environmental conditions.
Disclosure of Invention
Aiming at the problems of strong single sensor dependence and large drift error when the IMU works independently in the prior art, the invention provides a method, a system, a terminal and a storage medium for locating an unmanned vehicle, which are used for solving the technical problems.
In a first aspect, the present invention provides a method for locating an unmanned vehicle, comprising:
acquiring vehicle positioning data and system noise data acquired by a plurality of sensors;
Constructing a prediction equation and an observation equation of vehicle positioning based on the vehicle positioning data and the system noise data;
constructing a covariance matrix based on system noise data;
Based on a prediction equation, an observation equation and a covariance matrix of vehicle positioning, a Kalman algorithm is used for calculating Kalman gain, and an optimal estimated value at the current moment is obtained.
Further, the vehicle positioning data collected by the plurality of sensors comprises first positioning data and second positioning data;
The first positioning data comprise vehicle position data collected by the IMU, the second positioning data comprise point cloud data collected by the laser radar, and the system noise data comprise system state noise and system observation noise.
Further, the constructing a prediction equation and an observation equation of the vehicle positioning based on the vehicle positioning data and the system noise data includes:
Based on the first positioning data and the system state noise, constructing a prediction equation of vehicle positioning The prediction equation of the vehicle positioning is thatWherein, the method comprises the steps of, wherein,As the vehicle position at the time k,For an optimal estimation of the vehicle position at time k-1,In the event of a system state noise,Is a known system input;
based on the second positioning data and the system observation noise, an observation equation is constructed The observation equation isWherein, the method comprises the steps of, wherein,As the point cloud data of the laser radar at the k moment,Noise is observed for the system.
Further, constructing a covariance matrix based on the system noise data, comprising:
Constructing covariance matrix of system state noise based on system state noise System state noiseObeying the mean value is 0 and covariance matrixIs recorded as the Gaussian distribution of (2)~N(0,);
Based on system observation noise, constructing covariance matrix of system observation noiseSystem observation noiseObeying the mean value is 0 and covariance matrixIs recorded as the Gaussian distribution of (2)~N(0,)。
Further, based on a prediction equation, an observation equation and a covariance matrix of vehicle positioning, calculating a kalman gain by using a kalman algorithm to obtain an optimal estimated value at the current moment, including:
the prediction equation is developed by using first-order Taylor to obtain Wherein, the method comprises the steps of, wherein,For an a priori estimate of the vehicle position at time k,The optimal estimated value of the vehicle position at the moment k-1;
The observation equation is developed by using a first-order Taylor to obtain WhereinIs an observed estimate;
For a pair of Performing first order partial derivative calculation to obtain a state transition matrix;
For a pair ofPerforming first-order partial derivative calculation to obtain an observation transfer matrix;
Based on state transition matrix、Covariance matrix corresponding to k-1 timeObtaining a priori estimated value of a covariance matrix at k momentThe said=+;
Priori estimated value based on k moment covariance matrixTransfer matrix for observation、Calculation of Kalman gainThe said=Wherein, the method comprises the steps of, wherein,Is the kalman gain at time k,Is thatIs a transposed matrix of (a);
based on Kalman gain Prior estimate of vehicle positionObtaining the optimal estimated value of the current momentThe said=。
In a second aspect, the present invention provides an unmanned vehicle positioning system comprising:
The data acquisition module is used for acquiring vehicle positioning data and system noise data acquired by the plurality of sensors;
The equation construction module is used for constructing a prediction equation and an observation equation of vehicle positioning based on the vehicle positioning data and the system noise data;
the covariance matrix construction module is used for constructing a covariance matrix based on system noise data;
And the optimal estimated value calculation module is used for calculating Kalman gain by using a Kalman algorithm based on a prediction equation, an observation equation and a covariance matrix of vehicle positioning to obtain an optimal estimated value at the current moment.
Further, the equation construction module includes:
a predictive equation construction unit for constructing a predictive equation of vehicle positioning based on the first positioning data and the system state noise The prediction equation of the vehicle positioning is thatWherein, the method comprises the steps of, wherein,As the vehicle position at the time k,For an optimal estimation of the vehicle position at time k-1,In the event of a system state noise,Is a known system input;
An observation equation construction unit for constructing an observation equation based on the second positioning data and the system observation noise The observation equation isWherein, the method comprises the steps of, wherein,As the point cloud data of the laser radar at the k moment,Noise is observed for the system.
Further, the covariance matrix construction module comprises:
A first covariance matrix construction unit for constructing a covariance matrix of the system state noise based on the system state noise System state noiseObeying the mean value is 0 and covariance matrixIs recorded as the Gaussian distribution of (2)~N(0,);
A second covariance matrix construction unit for constructing a covariance matrix of the system observation noise based on the system observation noiseSystem observation noiseObeying the mean value is 0 and covariance matrixIs recorded as the Gaussian distribution of (2)~N(0,)。
In a third aspect, a terminal is provided, including:
A processor, a memory, wherein,
The memory is used for storing a computer program,
The processor is configured to call and run the computer program from the memory, so that the terminal performs the method of the terminal as described above.
In a fourth aspect, there is provided a computer storage medium having instructions stored therein which, when run on a computer, cause the computer to perform the method of the above aspects.
The unmanned vehicle positioning method, the unmanned vehicle positioning system, the unmanned vehicle positioning terminal and the unmanned vehicle storage medium have the beneficial effects that the unmanned vehicle positioning system can combine the advantages of the inertial measurement unit and the laser radar point cloud data to position the vehicle by fusing the data of a plurality of sensors. IMU can provide high frequency motion information and lidar provides accurate spatial data of the environment. Through combining the two, the positioning accuracy of the vehicle in a complex environment can be effectively improved under the condition that GNSS signals are weak or disturbed.
According to the unmanned vehicle positioning method, the unmanned vehicle positioning system, the unmanned vehicle positioning terminal and the unmanned vehicle storage medium, the prediction equation and the observation equation of vehicle positioning are fused by using the Kalman filtering algorithm, and the covariance matrix is constructed based on noise data, so that uncertainty in sensor data can be better processed. Even if noise exists in the data acquired by the sensor, the Kalman filtering can dynamically adjust the positioning result according to the noise characteristics, so that the vehicle can maintain high-precision positioning even in an environment with large noise interference, and the robustness of the system is enhanced.
The unmanned vehicle positioning method, the unmanned vehicle positioning system, the unmanned vehicle positioning terminal and the unmanned vehicle storage medium have the advantage of high instantaneity through the Kalman filtering algorithm. During the running process of the vehicle, the system can dynamically adjust the positioning result of the vehicle by continuously updating the predicted value and the observed value of the vehicle position. By means of the rapid iterative calculation of the state equation and the observation equation at each moment, the system can rapidly obtain the optimal estimated value at the current moment, so that the real-time positioning requirement of the unmanned vehicle is guaranteed, and safe and efficient operation is guaranteed.
According to the unmanned vehicle positioning method, the unmanned vehicle positioning system, the unmanned vehicle positioning terminal and the unmanned vehicle storage medium, the prediction equation and the observation equation are linearized through the first-order Taylor expansion, and the unmanned vehicle positioning system can adapt to the defect that the motion state of the unmanned vehicle is nonlinear in a dynamic complex running environment. The linearization process enables the Kalman filtering to be effectively applied to complex vehicle motion models, ensuring that the system can accurately track the position and attitude changes of the vehicle.
According to the unmanned vehicle positioning method, the unmanned vehicle positioning system, the unmanned vehicle positioning terminal and the unmanned vehicle storage medium, the state is updated by combining the observation data of the laser radar and utilizing the Kalman filter, and the unmanned vehicle positioning system can effectively correct accumulated errors caused by the IMU. This observation calibration mechanism reduces the accumulation of positioning errors and further improves the stability of long-term positioning. In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic flow diagram of a system of one embodiment of the present invention.
Fig. 2 is a schematic block diagram of a method of one embodiment of the invention.
Fig. 3 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In order to make the technical solution of the present invention better understood by those skilled in the art, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
The unmanned vehicle positioning method provided by the embodiment of the invention is executed by the computer equipment, and correspondingly, the unmanned vehicle positioning system is operated in the computer equipment.
FIG. 1 is a schematic flow chart of a method of one embodiment of the invention. The execution body of fig. 1 may be an unmanned automobile positioning system. The order of the steps in the flow chart may be changed and some may be omitted according to different needs.
In order to facilitate understanding of the present invention, the principle of the method for locating an unmanned vehicle according to the present invention is used to further describe the method for locating an unmanned vehicle according to the present invention in combination with the process of locating an unmanned vehicle in the embodiment.
Specifically, as shown in fig. 1, the method for positioning the unmanned vehicle includes:
s1, acquiring vehicle positioning data and system noise data acquired by a plurality of sensors.
The first positioning data comprise vehicle position data collected by the IMU, the second positioning data comprise point cloud data collected by the laser radar, and the system noise data comprise system state noise and system observation noise.
Specifically, in this step, the system collects vehicle positioning data from different sensors while recording system noise. This is the basis of the positioning process, data derived from the following sensors:
IMU sensors provide acceleration and angular velocity data of the vehicle per second. For example, the IMU provides acceleration information every 0.1 seconds. Assume at the moment The acceleration acquired by the IMU sensor isAngular velocity ofS. This means that the vehicle is accelerating during a small turn, these data constituting the first positioning data of the system.
Lidar-for example, lidar may be mounted on top of a vehicle, scanning hundreds of thousands of point cloud data per second, helping to measure the distance of the vehicle from surrounding objects (e.g., buildings, road blocks, etc.). At the moment of timeThe laser radar reports that the distance between the vehicle and the right building isThis data constitutes second positioning data.
S2, constructing a prediction equation and an observation equation of vehicle positioning based on the vehicle positioning data and the system noise data.
Based on the first positioning data and the system state noise, constructing a prediction equation of vehicle positioningThe prediction equation of the vehicle positioning is thatWherein, the method comprises the steps of, wherein,As the vehicle position at the time k,For an optimal estimation of the vehicle position at time k-1,In the event of a system state noise,Is a known system input. Based on the second positioning data and the system observation noise, an observation equation is constructedThe observation equation isWherein, the method comprises the steps of, wherein,As the point cloud data of the laser radar at the k moment,Noise is observed for the system.
S3, constructing a covariance matrix based on the system noise data.
Constructing covariance matrix of system state noise based on system state noiseSystem state noiseObeying the mean value is 0 and covariance matrixIs recorded as the Gaussian distribution of (2)~N(0,). Based on system observation noise, constructing covariance matrix of system observation noiseSystem observation noiseObeying the mean value is 0 and covariance matrixIs recorded as the Gaussian distribution of (2)~N(0,)。
And S4, calculating a Kalman gain by using a Kalman algorithm based on a prediction equation, an observation equation and a covariance matrix of the vehicle positioning to obtain an optimal estimated value at the current moment.
The prediction equation is developed by using first-order Taylor to obtainWherein, the method comprises the steps of, wherein,For an a priori estimate of the vehicle position at time k,The optimal estimated value of the vehicle position at the moment k-1. The observation equation is developed by using a first-order Taylor to obtainWhereinTo observe the estimated value. For a pair ofPerforming first order partial derivative calculation to obtain a state transition matrix. For a pair ofPerforming first-order partial derivative calculation to obtain an observation transfer matrix. Based on state transition matrix、Covariance matrix corresponding to k-1 timeObtaining a priori estimated value of a covariance matrix at k momentThe said=+. Priori estimated value based on k moment covariance matrixTransfer matrix for observation、Calculation of Kalman gainThe said=Wherein, the method comprises the steps of, wherein,Is the kalman gain at time k,Is thatIs a transposed matrix of (a). Based on Kalman gainPrior estimate of vehicle positionObtaining the optimal estimated value of the current momentThe said=。
In some embodiments, the pluggable module material management system may include a plurality of functional modules comprised of computer program segments. The computer program of each program segment in the pluggable module material management system may be stored in a memory of a computer device and executed by at least one processor to perform (see fig. 1 for details) the functions of pluggable module material management.
In this embodiment, the pluggable module material management system may be divided into a plurality of functional modules according to the functions performed by the pluggable module material management system, as shown in fig. 2. Functional modules of the system 200 may include a data acquisition module 210, an equation construction module 220, a covariance matrix construction module 230, and an optimal estimate calculation module 240. The module referred to in the present invention refers to a series of computer program segments capable of being executed by at least one processor and of performing a fixed function, stored in a memory. In the present embodiment, the functions of the respective modules will be described in detail in the following embodiments.
The data acquisition module is used for acquiring vehicle positioning data and system noise data acquired by the plurality of sensors;
The equation construction module is used for constructing a prediction equation and an observation equation of vehicle positioning based on the vehicle positioning data and the system noise data;
the covariance matrix construction module is used for constructing a covariance matrix based on system noise data;
And the optimal estimated value calculation module is used for calculating Kalman gain by using a Kalman algorithm based on a prediction equation, an observation equation and a covariance matrix of vehicle positioning to obtain an optimal estimated value at the current moment.
Alternatively, as one embodiment of the present invention, the equation construction module includes:
a predictive equation construction unit for constructing a predictive equation of vehicle positioning based on the first positioning data and the system state noise The prediction equation of the vehicle positioning is thatWherein, the method comprises the steps of, wherein,As the vehicle position at the time k,For an optimal estimation of the vehicle position at time k-1,In the event of a system state noise,Is a known system input;
An observation equation construction unit for constructing an observation equation based on the second positioning data and the system observation noise The observation equation isWherein, the method comprises the steps of, wherein,As the point cloud data of the laser radar at the k moment,Noise is observed for the system.
Optionally, as an embodiment of the present invention, the covariance matrix building module includes:
A first covariance matrix construction unit for constructing a covariance matrix of the system state noise based on the system state noise System state noiseObeying the mean value is 0 and covariance matrixIs recorded as the Gaussian distribution of (2)~N(0,);
A second covariance matrix construction unit for constructing a covariance matrix of the system observation noise based on the system observation noiseSystem observation noiseObeying the mean value is 0 and covariance matrixIs recorded as the Gaussian distribution of (2)~N(0,)。
Fig. 3 is a schematic structural diagram of a terminal 300 according to an embodiment of the present invention, where the terminal 300 may be used to execute the method for positioning an unmanned vehicle according to the embodiment of the present invention.
The terminal 300 may include a processor 310, a memory 320, and a communication unit 330. The components may communicate via one or more buses, and it will be appreciated by those skilled in the art that the configuration of the server as shown in the drawings is not limiting of the invention, as it may be a bus-like structure, a star-like structure, or include more or fewer components than shown, or may be a combination of certain components or a different arrangement of components.
The memory 320 may be used to store instructions for execution by the processor 310, and the memory 320 may be implemented by any type of volatile or non-volatile memory terminal or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk. The execution of the instructions in memory 320, when executed by processor 310, enables terminal 300 to perform some or all of the steps in the method embodiments described below.
The processor 310 is a control center of the storage terminal, connects various parts of the entire electronic terminal using various interfaces and lines, and performs various functions of the electronic terminal and/or processes data by running or executing software programs and/or modules stored in the memory 320, and invoking data stored in the memory. The processor may be comprised of an integrated circuit (INTEGRATED CIRCUIT, simply referred to as an IC), for example, a single packaged IC, or may be comprised of multiple packaged ICs connected to one another for the same function or for different functions. For example, the processor 310 may include only a central processing unit (Central Processing Unit, CPU for short). In the embodiment of the invention, the CPU can be a single operation core or can comprise multiple operation cores.
And a communication unit 330 for establishing a communication channel so that the storage terminal can communicate with other terminals. Receiving user data sent by other terminals or sending the user data to other terminals.
The present invention also provides a computer storage medium in which a program may be stored, which program may include some or all of the steps in the embodiments provided by the present invention when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random-access memory (random access memory RAM), or the like.
Therefore, the system can combine the advantages of the inertial measurement unit and the laser radar point cloud data to position the vehicle by fusing the data of a plurality of sensors. IMU can provide high frequency motion information and lidar provides accurate spatial data of the environment. Through combining the two, the positioning accuracy of the vehicle in a complex environment can be effectively improved under the condition that GNSS signals are weak or disturbed.
According to the unmanned vehicle positioning method, the unmanned vehicle positioning system, the unmanned vehicle positioning terminal and the unmanned vehicle storage medium, the prediction equation and the observation equation of vehicle positioning are fused by using the Kalman filtering algorithm, and the covariance matrix is constructed based on noise data, so that uncertainty in sensor data can be better processed. Even if noise exists in the data acquired by the sensor, the Kalman filtering can dynamically adjust the positioning result according to the noise characteristics, so that the vehicle can maintain high-precision positioning even in an environment with large noise interference, and the robustness of the system is enhanced.
The unmanned vehicle positioning method, the unmanned vehicle positioning system, the unmanned vehicle positioning terminal and the unmanned vehicle storage medium have the advantage of high instantaneity through the Kalman filtering algorithm. During the running process of the vehicle, the system can dynamically adjust the positioning result of the vehicle by continuously updating the predicted value and the observed value of the vehicle position. By means of the rapid iterative calculation of the state equation and the observation equation at each moment, the system can rapidly obtain the optimal estimated value at the current moment, so that the real-time positioning requirement of the unmanned vehicle is guaranteed, and safe and efficient operation is guaranteed.
According to the unmanned vehicle positioning method, the unmanned vehicle positioning system, the unmanned vehicle positioning terminal and the unmanned vehicle storage medium, the prediction equation and the observation equation are linearized through the first-order Taylor expansion, and the unmanned vehicle positioning system can adapt to the defect that the motion state of the unmanned vehicle is nonlinear in a dynamic complex running environment. The linearization process enables the Kalman filtering to be effectively applied to complex vehicle motion models, ensuring that the system can accurately track the position and attitude changes of the vehicle.
According to the unmanned vehicle positioning method, the unmanned vehicle positioning system, the unmanned vehicle positioning terminal and the unmanned vehicle storage medium, the state is updated by combining the observation data of the laser radar and utilizing the Kalman filter, and the unmanned vehicle positioning system can effectively correct accumulated errors caused by the IMU. The observation calibration mechanism reduces accumulation of positioning errors, further improves stability of long-term positioning, and technical effects achieved by the embodiment can be seen from the above description, and will not be repeated here.
It will be apparent to those skilled in the art that the techniques of embodiments of the present invention may be implemented in software plus a necessary general purpose hardware platform. Based on such understanding, the technical solution in the embodiments of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium such as a U-disc, a mobile hard disc, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, etc. various media capable of storing program codes, including several instructions for causing a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the method described in the embodiments of the present invention.
The same or similar parts between the various embodiments in this specification are referred to each other. In particular, for the terminal embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and reference should be made to the description in the method embodiment for relevant points.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with respect to each other may be through some interface, indirect coupling or communication connection of systems or modules, electrical, mechanical, or other form.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
Although the present invention has been described in detail by way of preferred embodiments with reference to the accompanying drawings, the present invention is not limited thereto. Various equivalent modifications and substitutions may be made in the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and it is intended that all such modifications and substitutions be within the scope of the present invention/be within the scope of the present invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411658533.9A CN119164408A (en) | 2024-11-20 | 2024-11-20 | Unmanned vehicle positioning method, system, terminal and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411658533.9A CN119164408A (en) | 2024-11-20 | 2024-11-20 | Unmanned vehicle positioning method, system, terminal and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN119164408A true CN119164408A (en) | 2024-12-20 |
Family
ID=93878935
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202411658533.9A Pending CN119164408A (en) | 2024-11-20 | 2024-11-20 | Unmanned vehicle positioning method, system, terminal and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN119164408A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119636802A (en) * | 2025-01-03 | 2025-03-18 | 奇瑞汽车股份有限公司 | Vehicle control method, device, equipment, storage medium and product |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109059906A (en) * | 2018-06-26 | 2018-12-21 | 上海西井信息科技有限公司 | Vehicle positioning method, device, electronic equipment, storage medium |
| JP2021038989A (en) * | 2019-09-03 | 2021-03-11 | 株式会社ゼンリンデータコム | Vehicle-mounted device, state estimation method, and program |
| CN114739402A (en) * | 2022-04-22 | 2022-07-12 | 合众新能源汽车有限公司 | Fusion positioning method, medium and electronic equipment |
| CN115760929A (en) * | 2022-11-14 | 2023-03-07 | 柳州五菱新能源汽车有限公司 | Vehicle fusion positioning method, device, equipment and medium |
-
2024
- 2024-11-20 CN CN202411658533.9A patent/CN119164408A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109059906A (en) * | 2018-06-26 | 2018-12-21 | 上海西井信息科技有限公司 | Vehicle positioning method, device, electronic equipment, storage medium |
| JP2021038989A (en) * | 2019-09-03 | 2021-03-11 | 株式会社ゼンリンデータコム | Vehicle-mounted device, state estimation method, and program |
| CN114739402A (en) * | 2022-04-22 | 2022-07-12 | 合众新能源汽车有限公司 | Fusion positioning method, medium and electronic equipment |
| CN115760929A (en) * | 2022-11-14 | 2023-03-07 | 柳州五菱新能源汽车有限公司 | Vehicle fusion positioning method, device, equipment and medium |
Non-Patent Citations (1)
| Title |
|---|
| 刘洋等: "基于激光雷达与IMU融合的农业机器人定位方法", 《智慧农业》, vol. 2024, no. 3, 28 June 2024 (2024-06-28), pages 94 - 106 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119636802A (en) * | 2025-01-03 | 2025-03-18 | 奇瑞汽车股份有限公司 | Vehicle control method, device, equipment, storage medium and product |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP7179110B2 (en) | Positioning method, device, computing device, computer-readable storage medium and computer program | |
| CN110208823B (en) | Method for determining a data profile for determining a vehicle position based on satellites | |
| CN113405545B (en) | Positioning method, device, electronic device and computer storage medium | |
| CN114264301B (en) | Vehicle-mounted multi-sensor fusion positioning method, device, chip and terminal | |
| CN113959457B (en) | Positioning method and device for automatic driving vehicle, vehicle and medium | |
| CN112946681B (en) | Laser radar positioning method fusing combined navigation information | |
| CN114234969B (en) | Navigation positioning method and device and electronic equipment | |
| CN112556696B (en) | Object positioning method and device, computer equipment and storage medium | |
| US12055643B2 (en) | Method for real-time position estimate correction of a movable object | |
| JP2016133605A (en) | Map generation system and map generation method | |
| CN112767545A (en) | Point cloud map construction method, device, equipment and computer storage medium | |
| CN119164408A (en) | Unmanned vehicle positioning method, system, terminal and storage medium | |
| CN118276143A (en) | A target motion state estimation method for unmanned aerial vehicles | |
| CN112835370A (en) | Vehicle positioning method, device, computer-readable storage medium, and processor | |
| CN114609659B (en) | Real-time correction method and system for vehicle positioning data in integrated navigation system | |
| CN113179480B (en) | Method and device for locating a vehicle | |
| CN115388886A (en) | Vehicle positioning method, device and equipment | |
| CN117553774B (en) | Carrier positioning and attitude determination method and device based on multi-sensor fusion | |
| CN109710594B (en) | Map data validity judging method and device and readable storage medium | |
| CN111397602A (en) | High-precision positioning method and device integrating broadband electromagnetic fingerprint and integrated navigation | |
| CN119335571A (en) | A method, device, electronic device and storage medium for processing positioning data | |
| CN115616642B (en) | A method, device, equipment and storage medium for correcting position data | |
| CN118169725A (en) | Vehicle positioning method, device, equipment and medium | |
| CN120141463B (en) | Navigation method combining satellite, inertial navigation, spectrum map and magnetometer and application | |
| CN117553794A (en) | Unmanned equipment state estimation method, unmanned equipment state estimation device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20241220 |
|
| RJ01 | Rejection of invention patent application after publication |