CN118191891A - Vehicle positioning method, device, electronic equipment and readable storage medium - Google Patents

Vehicle positioning method, device, electronic equipment and readable storage medium Download PDF

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Publication number
CN118191891A
CN118191891A CN202410362990.7A CN202410362990A CN118191891A CN 118191891 A CN118191891 A CN 118191891A CN 202410362990 A CN202410362990 A CN 202410362990A CN 118191891 A CN118191891 A CN 118191891A
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vehicle
state
position information
current moment
moment
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CN118191891B (en
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李汪润
朱波
李元
徐开庭
于红超
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Chongqing Selis Phoenix Intelligent Innovation Technology Co ltd
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Chongqing Selis Phoenix Intelligent Innovation Technology Co ltd
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Priority to CN202410362990.7A priority Critical patent/CN118191891B/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
    • G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled
    • 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
    • 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
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0257—Hybrid positioning
    • G01S5/0268—Hybrid positioning by deriving positions from different combinations of signals or of estimated positions in a single positioning system
    • G01S5/02685—Hybrid positioning by deriving positions from different combinations of signals or of estimated positions in a single positioning system involving dead reckoning based on radio wave measurements

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Navigation (AREA)

Abstract

The application relates to the technical field of positioning, and provides a vehicle positioning method, a vehicle positioning device, electronic equipment and a readable storage medium. The method comprises the following steps: acquiring first measurement data and second measurement data of a vehicle at the current moment; determining first position information corresponding to the vehicle at the current moment according to the vehicle speed information measured by the vehicle inertia measuring unit at the current moment, and determining second position information corresponding to the vehicle at the current moment according to the position information of a plurality of ultra-wideband base stations at the current moment and each distance information corresponding to each ultra-wideband base station and the vehicle; and predicting the position information of the vehicle at the current moment through a factor graph model based on the first position information of the vehicle at the current moment and the second position information of the vehicle at the current moment to obtain the target position information of the vehicle at the current moment. The problem of in the prior art in satellite refusing the environment other positioning technology location inaccuracy is solved, the positioning accuracy of vehicle under the satellite refusing environment is improved.

Description

Vehicle positioning method, device, electronic equipment and readable storage medium
Technical Field
The present application relates to the field of positioning technologies, and in particular, to a vehicle positioning method, a device, an electronic apparatus, and a readable storage medium.
Background
Currently, global positioning navigation systems (Global Navigation SATELLITE SYSTEM, GNSS) are widely used for positioning vehicles, but in satellite refusing environments where tunnels, underground garages and buildings are severely shielded, communication between the vehicles and satellites is interrupted, resulting in failure of the GNSS positioning function. In the satellite rejection environment, common positioning techniques are: inertial measurement units (Inertial Measurement Unit, IMU), ultra wideband (Ultra Wideband Band, UWB), lidar, ultrasound, geomagnetism, bluetooth, etc. The positioning information obtained by means of the IMU is not absolute position information, and the positioning error of the IMU may diverge over time, failing to continuously provide reliable position information. In a wireless sensing positioning method represented by UWB, in a complex positioning environment, the non-line-of-sight error of UWB is large. Under the condition that GNSS signals fail, other positioning technologies have certain limitations and cannot accurately position the vehicle.
Disclosure of Invention
In view of the above, the embodiments of the present application provide a vehicle positioning method, apparatus, electronic device and readable storage medium, so as to solve the problem in the prior art that other positioning technologies are not accurately positioned under the condition that GNSS signals fail.
In a first aspect of an embodiment of the present application, there is provided a vehicle positioning method, including: acquiring first measurement data and second measurement data of a vehicle at the current moment, wherein the first measurement data is vehicle speed information measured by an inertial measurement unit on the vehicle, and the second measurement data is position information of a plurality of ultra-wideband base stations and distance information corresponding to the vehicle respectively by the ultra-wideband base stations; determining first position information corresponding to the vehicle at the current moment according to the vehicle speed information measured by the vehicle inertia measuring unit at the current moment, and determining second position information corresponding to the vehicle at the current moment according to the position information of a plurality of ultra-wideband base stations at the current moment and each distance information corresponding to the vehicle respectively by each ultra-wideband base station; and predicting the position information of the vehicle at the current moment through a factor graph model based on the first position information of the vehicle at the current moment and the second position information of the vehicle at the current moment to obtain the target position information of the vehicle at the current moment, wherein the factor graph model is constructed based on the historical vehicle speed information, the historical position information of a plurality of ultra-wideband base stations and the distance information of each ultra-wideband base station corresponding to the historical vehicle.
In a second aspect of the embodiment of the present application, there is provided a vehicle positioning device including: the acquisition module is used for acquiring first measurement data and second measurement data of the vehicle at the current moment, wherein the first measurement data is vehicle speed information measured by an inertial measurement unit on the vehicle, and the second measurement data is position information of a plurality of ultra-wideband base stations and distance information corresponding to the vehicle respectively by the ultra-wideband base stations; the calculation module is used for determining first position information corresponding to the vehicle at the current moment according to the vehicle speed information measured by the vehicle inertia measurement unit at the current moment, and determining second position information corresponding to the vehicle at the current moment according to the position information of a plurality of ultra-wideband base stations at the current moment and the distance information corresponding to the vehicle of each ultra-wideband base station respectively; the prediction module is used for predicting the position information of the vehicle at the current moment according to the first position information of the vehicle at the current moment and the second position information of the vehicle at the current moment to obtain the target position information of the vehicle at the current moment through the factor graph model, and the factor graph model is constructed based on the historical vehicle speed information, the historical position information of the ultra-wideband base stations and the distance information corresponding to the historical vehicle respectively.
In a third aspect of the embodiments of the present application, there is provided an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.
In a fourth aspect of the embodiments of the present application, there is provided a readable storage medium storing a computer program which, when executed by a processor, implements the steps of the above method.
Compared with the prior art, the embodiment of the application has the beneficial effects that: by acquiring first measurement data and second measurement data of a vehicle at a current moment, the first measurement data is vehicle speed information measured by an on-vehicle inertia measurement unit, and a preliminary position estimate of the vehicle at the current moment, namely first position information corresponding to the vehicle at the current moment, can be calculated based on the vehicle speed information measured by the on-vehicle inertia measurement unit at the current moment. The second measurement data is the position information of a plurality of ultra-wideband base stations at the current moment and the distance information of each ultra-wideband base station corresponding to the vehicle respectively, which are obtained through an ultra-wideband wireless positioning method, and the second position information of the vehicle corresponding to the current moment can be determined by utilizing a multilateral positioning principle and combining the known coordinates of the plurality of ultra-wideband base stations and the distance between the vehicle and each base station. Based on first position information corresponding to the current moment of the vehicle and second position information corresponding to the current moment of the vehicle, predicting the position information of the vehicle at the current moment of the vehicle through a factor graph model, integrating two positioning data of an inertia measurement unit and ultra-wideband, associating the first position information corresponding to the current moment of the vehicle with the second position information corresponding to the current moment of the vehicle through the factor graph model, fusing according to a maximum posterior probability principle, and predicting the position of the current moment of the vehicle to obtain target position information of the vehicle at the current moment of the vehicle. In the running process of the vehicle, first measurement data and second measurement data are collected in real time, corresponding first position information and second position information at all times are obtained through calculation, the first position information and the second position information are input into a factor graph model, iterative calculation is continuously carried out, and target position information of the vehicle at all times is updated, so that real-time positioning of the vehicle is achieved. Through the steps, the characteristics of continuous tracking capacity of the inertial measurement unit and high ultra-wideband positioning accuracy can be utilized, the inertial measurement unit and different positioning data sources of the ultra-wideband are effectively integrated based on the factor graph model to perform data fusion, the limitation of a single sensor is overcome by utilizing the mutual dependency relationship and uncertainty information between the inertial measurement unit and the ultra-wideband, the overall stability and accuracy of positioning are improved, the problem of inaccurate positioning of other positioning technologies under the condition of GNSS signal failure in the prior art is solved, and the positioning accuracy of a vehicle under the satellite rejection environment is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic view of an application scenario according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a vehicle positioning method according to an embodiment of the present application;
FIG. 3 is a flow chart of another method for locating a vehicle according to an embodiment of the present application;
FIG. 4 is a flow chart of yet another method for locating a vehicle according to an embodiment of the present application;
FIG. 5 is a schematic diagram of simulation analysis of a vehicle positioning method according to an embodiment of the present application;
Fig. 6 is a schematic structural diagram of a vehicle positioning device according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
A vehicle positioning method and apparatus according to embodiments of the present application will be described in detail with reference to the accompanying drawings.
Fig. 1 is a schematic view of an application scenario according to an embodiment of the present application. The method is applied to the factor graph model and comprises the following steps: the system comprises a first measurement factor node, a second measurement factor node, a first state transfer factor node, a second state transfer factor node, a first variable node, a second variable node and a fusion variable node.
As shown in fig. 1, f (zi|xi) in the image is a first measurement factor node, where the first measurement factor node may be used to characterize an effect of first position information corresponding to each moment of time of the vehicle obtained from the inertia measurement unit on a vehicle state, and a probability dependency relationship between measurement data (i.e., the first position information) and a state variable (i.e., the first state prediction amount) is defined. The first measurement factor node f (zi|xi) is a probability density function, subject to gaussian distribution. By means of the first metrology factor node, the actual observed data (i.e. the first position information) can be compared and corrected with the predicted value (i.e. the first state predicted amount), thereby updating the system's estimate of the vehicle position.
The f (Zi|Yi) in the image is a second measurement factor node, and the second measurement factor node can be used for representing the influence of second position information corresponding to the vehicle at each moment acquired from ultra-wideband on the vehicle state, so that the probability dependency relationship between measurement data (namely second position information) and state variables (namely second state pre-measurement) is defined. The second measurement factor node f (zi|yi) is a probability density function, subject to gaussian distribution. By means of the second metrology factor node, the actual observed data (i.e. the second position information) can be compared and corrected with the predicted value (i.e. the second state predicted amount) to update the system's estimate of the vehicle position.
The f (xi|xi-1) in the image is a first state transfer factor node, which can be used to characterize how the vehicle state evolves over time in successive time intervals, and can represent the transition from one time state (e.g., X1) to the next time state (e.g., X2), reflecting the law of the change in the first state prediction amount of the vehicle from one time to the next, taking into account noise and uncertainty in the course of the state change. The first state transition factor node is connected with state first variable nodes with different time steps in the factor graph model, and can represent probability distribution of state transition and predict the state (position) of the vehicle at the next moment. The first state transition factor node is a probability density function, which follows a gaussian distribution.
The image f (yi|yi-1) is a second state transition factor node, which may be used to characterize how the vehicle state evolves over time in consecutive time intervals, may represent a transition from one time state (e.g., Y1) to the next time state (e.g., Y2), reflects the law of change of the second state prediction amount of the vehicle from one time to the next, and considers noise and uncertainty in the course of the state change. The second state transition factor node is connected with a second variable node of a state of different time steps in the factor graph model, and can represent probability distribution of state transition and predict the state (position) of the vehicle at the next moment. The second state transition factor node is a probability density function, which follows a gaussian distribution.
In the image, X1 and X2 … … Xi are first variable nodes, and X0 is GPS data of the vehicle obtained based on a global positioning navigation system at the initial moment. The first variable node is used for representing unknown variables (first state pre-measurement) of the vehicle, and in the application, the first variable node is the object to be estimated in the factor graph model at different time points, and is connected with the first measurement factor node and the first state transfer factor node, and the best estimated value of the first variable node and the probability distribution thereof can be obtained by calculating the influence of the first measurement factor node and the first state transfer factor node on the corresponding first variable node, so that the best estimated value of the vehicle position obtained based on the measurement data of the inertia measurement unit is determined.
In the image, Y1 and Y2 … … Yi are second variable nodes, and Y0 is GPS data of the vehicle obtained based on a global positioning navigation system at the initial moment. The second variable node is used for representing unknown variables (second state pre-measurement) of the vehicle, and in the application, the second variable node is the object to be estimated in the factor graph model at different time points, and is connected with the second measurement factor node and the second state transfer factor node, and the best estimated value of the second variable node and the probability distribution thereof can be obtained by calculating the influence of the second measurement factor node and the second state transfer factor node on the corresponding second variable node, so that the best estimated value of the vehicle position based on ultra-wideband measurement data is determined.
In the image, XY1 and XY2 … … XYi are all fusion variable nodes, the fusion variable nodes are fusion state variables obtained by fusing the first state prediction amount and the second state prediction amount, and the fusion variable nodes represent optimal estimated values obtained through interaction and information fusion of the first variable nodes and the second variable nodes in the factor graph model. The fusion variable node integrates the optimal estimation of the vehicle position from the information of the inertial measurement unit and the ultra-wideband for the factor graph model, so that errors can be eliminated, and the positioning precision can be improved.
Fig. 2 is a schematic flow chart of a vehicle positioning method according to an embodiment of the present application. As shown in fig. 2, the vehicle positioning method includes:
Step 201, acquiring first measurement data and second measurement data of a vehicle at a current moment, wherein the first measurement data is vehicle speed information measured by an inertial measurement unit on the vehicle, and the second measurement data is position information of a plurality of ultra-wideband base stations and distance information corresponding to the vehicle by the ultra-wideband base stations.
In some embodiments, an inertial measurement unit (Inertial Measurement Unit, IMU) plays an important role in real-time positioning of a vehicle on which the IMU is mounted, based on which the speed information of the vehicle can be measured, including angular speed and acceleration. The core components of the IMU include gyroscopes and accelerometers. The motion state of the vehicle can be monitored in real time through the IMU, the acceleration information and the angular velocity information of the current vehicle are obtained, and the displacement and the steering change of the vehicle are obtained by integrating the acceleration and the angular velocity data, so that the first position information of the current moment of the vehicle is obtained.
In some embodiments, a plurality of Ultra-Wideband (UWB) base stations are deployed within the vehicle's travel range, each UWB base station having known fixed location coordinates, i.e., location information of the above-mentioned UWB base stations. The vehicle-mounted UWB tag is arranged on the vehicle, the vehicle-mounted UWB tag transmits or receives the ultra-wideband pulse signals, and after each UWB base station receives the signals, each distance information of each ultra-wideband base station corresponding to the vehicle can be calculated by accurately measuring the round trip flight time or the arrival time difference of the signals. The number of UWB base stations is at least 3. The vehicle position can be calculated through a geometric algorithm by receiving signals of the vehicle-mounted UWB tag through at least three UWB base stations. Along with the movement of the vehicle, the vehicle-mounted UWB tag continuously exchanges signals with each UWB base station, and the positioning data are continuously updated, so that the real-time positioning of the vehicle is realized. The first measurement data and the second measurement data at the current moment can be integrated later, and the vehicle positioning with higher precision and reliability can be realized under the refusing environment of the global positioning navigation system (Global Navigation SATELLITE SYSTEM, GNSS).
Step 202, determining first position information corresponding to the vehicle at the current moment according to the vehicle speed information measured by the vehicle inertia measuring unit at the current moment, and determining second position information corresponding to the vehicle at the current moment according to the position information of a plurality of ultra wideband base stations at the current moment and each distance information corresponding to each ultra wideband base station and the vehicle respectively.
In some embodiments, the IMU may measure acceleration and angular velocity of the vehicle, and may derive displacement of the vehicle along a straight line by integrating the acceleration twice, and may derive steering information of the vehicle by integrating the angular velocity. Based on the position of the vehicle at the starting time and the displacement information obtained by continuous integration, the position of the vehicle can be updated in real time. According to the vehicle speed information measured by the vehicle inertia measuring unit at the current moment, the first position information corresponding to the vehicle at the current moment can be calculated.
In some embodiments, UWB technology is used to accurately measure the time of flight or time difference of arrival between each base station and the on-board UWB tag on the vehicle by transmitting and receiving ultra wideband pulse signals, thereby calculating each distance information from each base station to the vehicle. If the number of UWB base stations is three, a trilateral positioning method can be utilized to construct a geometric relationship according to the position information of the three base stations and the corresponding distance information between each base station and the vehicle, and the second position information of the vehicle at the current moment is calculated. Specifically, as shown in fig. 3, three UWB base stations are deployed in a GNSS rejection environment, and the vehicle itself mounts a vehicle-mounted UWB tag. In the case where the two-dimensional position coordinates of the base station a, the base station b, and the base station c are known as (x 0,y0)、(x1,y1) and (x 2,y2), respectively, and the distances d 0、d0 and d 2 of the respective base stations from the on-vehicle UWB tag at the present time, the second position information (x, y) corresponding to the vehicle at the present time can be obtained by the following formula.
The first position information corresponding to the vehicle at the current moment and the second position information corresponding to the vehicle at the current moment can be combined through the technology of the factor graph model, the accuracy and the robustness of vehicle positioning can be further improved, the advantages of the two are complemented, the positioning error is reduced, and the positioning accuracy in the GNSS rejection environment of the vehicle is improved.
Step 203, predicting the position information of the vehicle at the current moment according to the first position information of the vehicle at the current moment and the second position information of the vehicle at the current moment, so as to obtain the target position information of the vehicle at the current moment, wherein the factor graph model is constructed based on the historical vehicle speed information, the historical position information of a plurality of ultra-wideband base stations and the distance information corresponding to the historical vehicle respectively by each ultra-wideband base station.
In some embodiments, the factor graph model is constructed based on historical vehicle speed information and historical location information of a plurality of ultra-wideband base stations and respective distance information of respective ultra-wideband base stations corresponding to the historical vehicle. Through the information, a rich data basis can be provided for the factor graph model, so that the factor graph model can learn potential relations between the vehicle position and the first position information of the vehicle and the second position information of the vehicle. The factor graph model can express causal relationships and probability distributions between the vehicle position and first position information of the vehicle and second position information of the vehicle, and is beneficial to integrating data of different information sources. Theoretically, the more and more comprehensive the historical data, the stronger the learning ability of the factor graph model is, and the prediction accuracy is correspondingly improved.
In some embodiments, the factor graph model integrates first position information corresponding to the vehicle at each moment, second position information corresponding to the vehicle at each moment and variable states of the vehicle at each moment, the variable states at each moment can be used for representing optimal position estimation of the vehicle at each moment, the relation between the variable states can be captured through the factor graph model, and the first position information corresponding to the vehicle at the current moment and the second position information corresponding to the vehicle at the current moment can be fused through Bayesian reasoning or maximum posterior probability and the like, so that an optimal estimated value of the position of the vehicle at the current moment, namely target position information of the vehicle at the current moment, is obtained. The factor graph model is used for fusing the positioning data of the IMU and the UWB, so that the divergence of the positioning error of the IMU can be restrained, the advantages of the two positioning technologies are combined, the position information of the vehicle is updated and optimized in real time, the method is suitable for vehicle positioning in the GNSS refusing environment, and the accuracy and the stability of vehicle positioning are improved.
Based on the vehicle positioning method provided by the application, the first measurement data and the second measurement data of the vehicle at the current moment are obtained, the first measurement data are the vehicle speed information measured by the inertial measurement unit on the vehicle, the inertial measurement unit can provide the acceleration and the angular speed of the vehicle in real time, and the preliminary position estimation of the vehicle at the current moment, namely the first position information corresponding to the vehicle at the current moment, can be calculated based on the vehicle speed information measured by the inertial measurement unit on the vehicle at the current moment. The second measurement data is the position information of a plurality of ultra-wideband base stations at the current moment and the distance information of each ultra-wideband base station corresponding to the vehicle respectively, which are obtained through an ultra-wideband wireless positioning method, and the position of the vehicle, namely the second position information of the vehicle corresponding to the current moment, can be determined by utilizing a multilateral positioning principle and combining the coordinates of the plurality of ultra-wideband base stations and the distances from the vehicle to each base station. Based on first position information corresponding to the current moment of the vehicle and second position information corresponding to the current moment of the vehicle, predicting the position information of the vehicle at the current moment of the vehicle through a factor graph model, integrating two positioning data of an inertia measurement unit and ultra-wideband, associating the first position information corresponding to the current moment of the vehicle with the second position information corresponding to the current moment of the vehicle through the factor graph model, fusing according to a maximum posterior probability principle, and predicting the position of the current moment of the vehicle to obtain target position information of the vehicle at the current moment of the vehicle. In the running process of the vehicle, first measurement data and second measurement data are collected in real time, corresponding first position information and second position information at all times are obtained through calculation, the first position information and the second position information are input into a factor graph model, iterative calculation is continuously carried out, and target position information of the vehicle at all times is updated, so that real-time positioning of the vehicle is achieved. The factor graph model is constructed and trained based on historical vehicle speed information, historical position information of a plurality of ultra-wideband base stations and distance information corresponding to the historical vehicles of the ultra-wideband base stations. Through the steps, the characteristics of continuous tracking capacity of the inertial measurement unit and high ultra-wideband positioning accuracy can be utilized, the inertial measurement unit and different positioning data sources of the ultra-wideband are effectively integrated based on the factor graph model to perform data fusion, the limitation of a single sensor is overcome by utilizing the mutual dependency relationship and uncertainty information between the inertial measurement unit and the ultra-wideband, the problem of error accumulation of the inertial measurement unit after long-term use is restrained, the overall stability and accuracy of positioning are improved, the problem of inaccurate positioning of other positioning technologies under the condition of GNSS signal failure in the prior art is solved, and the positioning accuracy of a vehicle under a satellite rejection environment is improved.
In some embodiments, before predicting the position information of the vehicle at the current moment by the factor graph model, the method further includes: acquiring first measurement data of a vehicle at each historical moment and second measurement data of the vehicle at each historical moment; constructing a first measurement factor node and a second measurement factor node based on a measurement equation, wherein the first measurement factor node is connected with first measurement data of the vehicle at each historical moment and a corresponding first variable node, and the second measurement factor node is connected with second measurement data of the vehicle at each historical moment and a corresponding second variable node; the position information of the vehicle at each history moment is used as a fusion variable node, a first state transfer factor node and a second state transfer factor node are constructed based on a state equation, the first state transfer factor node is connected with the first variable node at the history moment before and after, the second state transfer factor node is connected with the second variable node at the history moment before and after, and the fusion variable node is obtained by fusion of the corresponding first state transfer factor node and the second state transfer factor node; and constructing a factor graph model according to the first measurement factor node, the second measurement factor node, the first state transfer factor node, the second state transfer factor node, the first variable node, the second variable node and the fusion variable node.
In some embodiments, the first measurement data and the second measurement data of the vehicle at each historical moment are obtained by measuring through an inertial measurement unit, and may be acceleration and angular velocity of the vehicle at each historical moment. The second measurement data of the vehicle at each historical moment is obtained through UWB measurement, namely, the measurement value of the distance between the vehicle and a plurality of UWB base stations, and the position of the vehicle can be calculated by combining the known positions of the base stations. The first measurement data and the second measurement data provide critical information about the vehicle position and the movement state. The first measurement data and the second measurement data have different accuracy and characteristics, and their combined use can improve the accuracy and robustness of the position estimation.
In some embodiments, the first and second metrology factor nodes are constructed based on metrology equations, including inertial measurement unit state equations and ultra-wideband state equations. The inertial measurement unit state equation is: z IMU＝HIMUX+KIMUVIMU, wherein Z IMU is a quantity measurement, i.e. first measurement data, H IMU is a measurement matrix, K IMU is a noise matrix based on an inertial measurement unit, V IMU is error noise based on the inertial measurement unit, and X represents a vehicle state estimated based on the first measurement data at the current time. The ultra-wideband equation is: z UWB＝HUWBY+KUWBVUWB, wherein Z UWB is a measurement, namely second measurement data, H UWB is a measurement matrix, K UWB is an ultra-wideband based noise matrix, V UWB is an ultra-wideband based error noise, and Y represents a vehicle state estimated based on the second measurement data at the current moment.
In some embodiments, a first measurement factor node is constructed through an inertial measurement unit state equation, first measurement data of the vehicle at each historical moment is associated with a corresponding first variable node in the factor graph model, the first measurement factor node can represent a constraint relation between the first measurement data of the vehicle at each historical moment and the corresponding first variable node, and the first measurement data is fused into the whole model through the structure of the factor graph model. And constructing a second measurement factor node through an ultra-wide band state equation, establishing a connection between second measurement data of the vehicle at each historical moment and a corresponding second variable node in the factor graph model, wherein the second measurement factor node can represent a constraint relation between the second measurement data of the vehicle at each historical moment and the corresponding second variable node, and integrating the second measurement data into the whole model through the structure of the factor graph model. The first variable node corresponds to a vehicle state estimated based on the first measurement data, and the second variable node corresponds to a vehicle state estimated based on the second measurement data.
In some embodiments, the position information of the vehicle at each historical moment is used as a fusion variable node, and the first state transfer factor node and the second state transfer factor node are constructed based on a state equation, so that the continuity and change of the vehicle state can be captured in the factor graph model. The first state transition factor node may be used to characterize a state transition process of the vehicle, which is deduced from the first measurement data in the time series, describing a law of a change of the vehicle state over time, the first state transition factor node being linked to a first variable node of the vehicle, which is based on the first measurement data at an adjacent moment. The second state transition factor node may be used to characterize a state transition process of the vehicle, which is deduced from the second measurement data in the time series, describing a law of a change of the vehicle state over time, the second state transition factor node linking a second variable node of the vehicle based on the second measurement data at an adjacent moment. The fusion variable nodes are the corresponding first variable nodes and second variable nodes, the change rule of the vehicle motion state and measurement data of various sources are comprehensively considered through a maximum posterior probability estimation method, the global optimal position of the vehicle at the current moment is estimated, and the fusion variable nodes are solved, namely the estimated target position information of the vehicle, so that the state of the vehicle can be predicted more comprehensively, and the accuracy of position estimation is improved.
In some embodiments, a complete factor graph model is constructed according to the first measurement factor node, the second measurement factor node, the first state transfer factor node, the second state transfer factor node, the first variable node, the second variable node and the fusion variable node, and accurate estimation and prediction of vehicle position information can be achieved by comprehensively considering the dependency relationship among measurement data, state transfer and variables through the factor graph model.
In some embodiments, predicting, by the factor graph model, the position information of the vehicle at the current time based on the first position information of the vehicle at the current time and the second position information of the vehicle at the current time, to obtain the target position information of the vehicle at the current time includes: calculating first position information corresponding to the current moment through the first measurement factor node, the first state transition factor node and the first state quantity of the vehicle at the previous moment to obtain a first state pre-measurement value of the vehicle corresponding to the current moment and related to the inertia measurement unit; calculating second position information corresponding to the current moment through the second measurement factor node, the second state transfer factor node and the second state quantity of the vehicle at the previous moment to obtain a second state pre-measurement value of the vehicle corresponding to the current moment and related to the inertia measurement unit; and carrying out fusion processing on the basis of the vehicle first state prediction quantity corresponding to the current moment and the vehicle second state prediction quantity corresponding to the current moment and related to the ultra-wideband to obtain the target position information of the vehicle at the current moment.
In some embodiments, the first position information corresponding to the current moment is calculated through a first measurement factor node, a first state transition factor node and a first state quantity of the vehicle at the previous moment in the factor graph model, the first measurement factor node reflects a constraint relation between the first position information calculated through measurement of the inertia measurement unit and the first state quantity of the vehicle, the first state transition factor node describes a change rule of the state of the vehicle along with time, a mean value and a variance corresponding to a first variable node corresponding to the current moment are calculated through historical data (i.e. the first state quantity of the vehicle at the previous moment) and current measurement information (i.e. the first position information), the mean value and the variance corresponding to the first variable node corresponding to the current moment can represent optimal estimation of the state of the vehicle at the current moment based on the measurement of the inertia measurement unit, and the first state pre-measurement of the vehicle at the current moment about the inertia measurement unit is obtained, so that the state of the vehicle is predicted and updated. When the first position information of the vehicle at each time is known, the prediction of the vehicle first state prediction amount of the vehicle at each time with respect to the inertial measurement unit may be performed by the following formula:
Wherein X 1,X2,…,Xn is the vehicle first state prediction amount of the inertial measurement unit at each moment, Z 1,Z2,…,Zn is the first position information of the vehicle at each moment, f (X i|Xi-1) is a first state transition factor node, f (Z i|Xi) is a first measurement factor node, f (X i|Xi-1) obeys Gaussian distribution, the mean and variance of which are M i-1Xi-1 respectively, F (Z i|Xi) obeys Gaussian distribution with mean and variance of H iXi and/>, respectivelyM is a matrix, H is a measurement matrix, and K is a noise matrix based on an inertial measurement unit.
The first position information corresponding to the current moment is calculated through the first measurement factor node, the first state transition factor node and the first state quantity of the vehicle at the previous moment, so as to obtain the first state prediction quantity of the vehicle corresponding to the current moment and related to the inertia measurement unit, and the first state prediction quantity can be represented by a formula:
Wherein the method comprises the steps of
P i|i(Xi) is a vehicle first state prediction amount related to the inertial measurement unit corresponding to the current time, P i|i-1(Xi) is a vehicle first state amount at the previous time, and f (Z i|Yi) is a first measurement factor node.
Similarly, the first position information corresponding to the next moment is calculated through the first measurement factor node, the first state transfer factor node and the first state quantity of the vehicle at the current moment to obtain the first state prediction quantity of the vehicle corresponding to the next moment and related to the inertial measurement unit, and the first state prediction quantity can be characterized by the following formula:
In some embodiments, the second position information corresponding to the current moment is calculated through a second measurement factor node, a second state transition factor node and a vehicle second state quantity at the previous moment in the factor graph model, the second measurement factor node reflects a constraint relation between the second position information obtained through ultra-wideband measurement calculation and the vehicle second state quantity, the second state transition factor node describes a change rule of a vehicle state along with time, a mean value and a variance corresponding to a second variable node corresponding to the current moment are calculated through historical data (namely the vehicle second state quantity at the previous moment) and current measurement information (namely the second position information), and the mean value and the variance corresponding to the second variable node corresponding to the current moment can represent optimal estimation of the state of the vehicle at the current moment based on ultra-wideband measurement, so that the vehicle second state prediction of the current moment about ultra-wideband is obtained, and the vehicle state is predicted and updated.
In some embodiments, a first state pre-measurement (mean and variance corresponding to a first variable node at a current time) of a vehicle corresponding to an inertial measurement unit at the current time and a second state pre-measurement (mean and variance corresponding to a second variable node at the current time) of the vehicle corresponding to an ultra-wideband are fused by using a factor graph model, a posterior probability or other fusion algorithm can be maximized in consideration of correlation and uncertainty among different information sources, the first state pre-measurement and the second state pre-measurement are integrated, different weights can be respectively given to the first state pre-measurement and the second state pre-measurement according to factors such as reliability of the first state pre-measurement and the second state pre-measurement, and an optimal position of the vehicle is estimated, wherein the optimal position is a target predicted position of the vehicle. Specifically, the first state quantity of the vehicle at the initial time may be X0, and the second state quantity of the vehicle at the initial time may be Y0. The first state quantity of the vehicle at the initial time and the second state quantity of the vehicle at the initial time are positioning data of the GPS which just enters the GNSS refusal environment at the initial time. Calculating first position information corresponding to the first moment through a first measurement factor node, a first state transition factor node and a first state quantity X0 of the vehicle at the initial moment to obtain a first state pre-measurement X1 of the vehicle corresponding to the first moment and related to an inertia measurement unit; calculating second position information corresponding to the first moment through a second measurement factor node, a second state transfer factor node and a second state quantity Y0 of the vehicle at the initial moment to obtain a second state predicted quantity Y1 of the vehicle corresponding to the first moment and related to the inertia measurement unit; and carrying out fusion processing on the basis of the vehicle first state predicted quantity X1 corresponding to the first moment and the vehicle second state predicted quantity Y1 corresponding to the first moment and the inertia measuring unit to obtain target position information XY1 of the vehicle at the first moment. In the vehicle positioning method based on the factor graph model, the continuity and stability of long-term tracking of the inertial measurement unit are considered, ultra-wideband real-time and relatively accurate position information is integrated, and the accuracy and the robustness of real-time vehicle positioning are improved.
In some embodiments, a plurality of measured position information from a plurality of sensors at a current time of the vehicle may also be obtained. The measured plurality of position information of the vehicle from a plurality of sensors (vehicle positioning device, such as inertial measurement unit, ultra wideband, laser radar, ultrasonic wave, etc.) at the current moment is Y 1,Y2,…,Yn, and according to gaussian distribution, the measurement model is: The measured position information of the sensors can be fused through the factor graph model, and the position information of the vehicle at the current moment can be predicted. The formula of the vehicle position information predicted based on the factor graph model from the i-1 time to the i time can be:
P(Xij|Yij)＝P(Xij|Xi-1|j)P(Yij|Xi)
Fusing a plurality of pieces of measured position information from a plurality of sensors at a current time based on a factor graph model, the predicted position information at the current time of the vehicle may be expressed as: above/> And predicting the obtained position information of the vehicle at the current moment. The vehicle is positioned in real time by integrating the position information obtained by measurement and calculation of a plurality of sensors (vehicle real-time positioning devices), so that the overall positioning accuracy and robustness are improved.
In some embodiments, calculating the first position information corresponding to the current time through the first measurement factor node, the first state transition factor node and the first state quantity of the vehicle at the previous time to obtain the first state prediction value of the vehicle corresponding to the current time and related to the inertia measurement unit, including: predicting the first state quantity of the vehicle at the current moment according to the first state quantity of the vehicle at the previous moment and the first state transfer factor node to obtain a first initial state prediction quantity of the vehicle at the current moment; performing error calculation based on the vehicle first initial state predicted value of the vehicle at the current moment and first position information corresponding to the current moment to obtain first loss; updating the first measurement factor node according to the first loss to obtain an updated first measurement factor node; updating the first state transfer factor node based on the first state quantity of the vehicle at the previous moment and the first position information corresponding to the current moment to obtain an updated first state transfer factor node; and calculating the first position information corresponding to the current moment through the updated first measurement factor node to obtain the vehicle first state prediction quantity corresponding to the current moment and related to the inertia measurement unit.
In some embodiments, the first state quantity of the vehicle at the current time is predicted according to the first state quantity of the vehicle at the previous time and the first state transfer factor node, and the first state quantity of the vehicle at the current time is predicted by using the known first state transfer factor node and the first state quantity of the vehicle at the previous time, so as to obtain a preliminary uncorrected first initial state prediction quantity of the vehicle. And carrying out error calculation on the first initial state predicted value of the current vehicle obtained based on state transition prediction and first position information (namely the vehicle position measured by the actual IMU) corresponding to the current moment, and calculating the difference between the first initial state predicted value and the first position information to obtain first loss. The first penalty may then be used for updating the first metrology factor node to improve accuracy of factor graph model predictions. And updating a first measurement factor node according to the calculated first loss, wherein the first measurement factor node can represent the influence degree of IMU measurement data (first position information) on the vehicle state. The updated first metrology factor node will more accurately describe the relationship between the IMU measurement data (first position information) and the vehicle state. Updating the first state transfer factor node based on the first state quantity of the vehicle at the previous moment and the first position information corresponding to the current moment, and optimizing the first state transfer factor node to enable the first state transfer factor node to reflect the real situation that the first state quantity of the vehicle changes along with time. After the updated first measurement factor node and the updated first state transfer factor node are obtained, calculating and predicting the first position information corresponding to the current moment based on the updated first measurement factor node to obtain more accurate vehicle first state prediction quantity corresponding to the current moment and related to the inertia measurement unit. By continuously cycling through the steps, the factor graph model is continuously updated according to the new first position information in the real-time positioning process, and the first state prediction quantity of the vehicle at each moment can be accurately tracked and estimated in real time. Through continuous iteration and continuous updating of the factor graph model in the process, the first position information is combined with the first state transfer factor node, so that positioning errors are gradually reduced, and positioning accuracy is improved.
In some embodiments, calculating the second position information corresponding to the current time through the second measurement factor node, the second state transition factor node and the second state quantity of the vehicle at the previous time to obtain the second state prediction value of the vehicle corresponding to the current time and related to the inertia measurement unit, including: predicting the second state quantity of the vehicle at the current moment according to the second state quantity of the vehicle at the previous moment and the second state transfer factor node to obtain a second initial state prediction quantity of the vehicle at the current moment; performing error calculation based on the vehicle second initial state predicted value of the vehicle at the current moment and second position information corresponding to the current moment to obtain second loss; updating the second measurement factor node according to the second loss to obtain an updated second measurement factor node; updating the second state transfer factor node based on the second state quantity of the vehicle at the previous moment and the second position information corresponding to the current moment to obtain an updated second state transfer factor node; and calculating the second position information corresponding to the current moment through the updated second measurement factor node to obtain the second state prediction quantity of the vehicle corresponding to the current moment and related to the inertia measurement unit.
In some embodiments, the second state quantity of the vehicle at the current time is predicted according to the second state quantity of the vehicle at the previous time and the second state transfer factor node, and the vehicle state at the current time is predicted by using the known second state transfer factor node and the second state quantity of the vehicle at the previous time, so as to obtain a preliminary uncorrected second initial state prediction quantity of the vehicle. And (3) carrying out error calculation on the second initial state predicted value of the current vehicle obtained based on the state transition prediction and the second position information (namely the actual UWB measured vehicle position) corresponding to the current moment, and calculating the difference between the second initial state predicted value and the second position information to obtain a second loss. The second penalty may then be used for updating the second metrology factor node to improve accuracy of the factor graph model prediction. And updating a second measurement factor node according to the calculated second loss, wherein the second measurement factor node can represent the influence degree of UWB measurement data (second position information) on the vehicle state. The updated second metrology factor node will more accurately describe the relationship between UWB measurement data (second position information) and vehicle state. Updating the second state transfer factor node based on the second state quantity of the vehicle at the previous moment and the second position information corresponding to the current moment, and optimizing the second state transfer factor node to enable the second state transfer factor node to reflect the real situation that the second state quantity of the vehicle changes along with time. After the updated second measurement factor node and the updated second state transfer factor node are obtained, calculating and predicting second position information corresponding to the current moment based on the updated second measurement factor node to obtain more accurate vehicle second state prediction quantity corresponding to the current moment and related to the inertia measurement unit. By continuously cycling through the steps, the factor graph model is continuously updated according to the new second position information in the real-time positioning process, and the second state predicted quantity of the vehicle at each moment can be accurately tracked and estimated in real time. Through continuous iteration and continuous updating of the factor graph model in the process, the second position information is combined with the second state transfer factor node, so that positioning errors are gradually reduced, and positioning accuracy is improved.
In some embodiments, the vehicle speed information includes an acceleration at a current time and an angular speed at the current time, and determining first position information corresponding to the vehicle at the current time according to the vehicle speed information measured by the on-vehicle inertia measurement unit at the current time includes: integrating the acceleration at the current moment to obtain the displacement of the vehicle; integrating the angular speed at the current moment to obtain the steering information of the vehicle; and obtaining first position information corresponding to the vehicle at the current moment based on the displacement of the vehicle and the steering information of the vehicle.
In some embodiments, the vehicle speed information includes an acceleration at the current time and an angular velocity at the current time, both of which are key parameters describing the state of motion of the vehicle. The acceleration may reflect the speed of the vehicle speed change and the angular speed may reflect the speed of the vehicle rotation. The acceleration and the angular velocity of the vehicle at the current moment can be measured by the inertial measurement unit. When the inertial measurement unit on the vehicle measures the acceleration at the current moment, the linear displacement of the vehicle in the period can be obtained by performing the integration operation on the acceleration at the current moment twice. Acceleration is the rate of change of velocity per unit time, and two successive integrations can convert acceleration into velocity, and then into displacement. By performing the integration operation twice on the acceleration at the present time, the displacement of the vehicle in a certain direction for a certain period of time can be estimated. When the inertial measurement unit on the vehicle measures the angular velocity at the current moment, the angular velocity integration at the current moment can obtain the turning angle variation of the vehicle in the time period, namely the steering information of the vehicle, and the turning angle information is used for determining the direction variation of the vehicle on the basis of straight running. After the linear displacement and steering information of the vehicle are obtained, the displacement can be added to the position of the vehicle at the previous moment in a superposition or vector synthesis mode, the direction of the vehicle is adjusted according to the steering information, and new position information of the vehicle at the current moment relative to the position of the vehicle at the previous moment, namely, the first position information corresponding to the vehicle at the current moment, can be calculated.
In some embodiments, constructing the first state transfer factor node and the second state transfer factor node based on the state equation includes: carrying out Gaussian distribution on first state quantities of adjacent moments of the vehicle to obtain a first Gaussian distribution result; constructing a first state transition factor node based on a first Gaussian distribution result and an inertial measurement unit state equation, wherein the first state transition factor node represents probability distribution of state transition from one moment to the next moment; carrying out Gaussian distribution on second state quantities of adjacent moments of the vehicle to obtain a second Gaussian distribution result; and constructing a second state transfer factor node based on the second Gaussian distribution result and the ultra-wide band state equation, wherein the second state transfer factor node represents the probability distribution of state transfer from one moment to the next moment.
In some embodiments, the state equations include inertial measurement unit state equations and ultra-wideband state equations. The state equation of the inertial measurement unit is xi=m IMUX(i-1)+NIMUWIMU, where Xi is the first state quantity of the vehicle at the current time, is the optimal estimation of the first state quantity of the vehicle at the ith time (the current time), X (i-1) is the first state quantity of the vehicle at the previous time, that is, the first state quantity of the vehicle at the ith-1 time, M IMU is a matrix, reflects the evolution rule of the first state of the vehicle over time, N IMU is an IMU noise matrix, and is used to represent the influence of IMU noise on the first state transition of the vehicle, and W IMU is IMU noise, which may represent the uncertainty in the transition process of the first state of the vehicle. The method comprises the steps of modeling each item of data in the running process of the vehicle based on a factor graph model, defining a first state quantity of the vehicle at each moment as each first variable node, enabling first state transitions from one moment to the next moment to follow Gaussian distribution, solving the first variable nodes at each moment to obtain the mean value and variance of the first state quantity at each moment, wherein the first state transitions have randomness, and therefore the change of the first state of the vehicle can be used as a probability event. In the factor graph model, corresponding to the process of the first state transition of the vehicle, a first state transition factor node can be constructed, and the first state transition factor node reflects the probability dependence relationship between two first state quantities of adjacent time steps of the vehicle, which evolves along with time. Gaussian distribution is a commonly used probability distribution model that can be used to describe the distribution of random variables. By gaussian-distributing the first state quantity at adjacent times (i.e., the last time and the next time) of the vehicle, a probability distribution of the first state quantity between different times, i.e., a first gaussian distribution result, can be obtained. And constructing a first state transfer factor node based on the first Gaussian distribution result and an inertial measurement unit state equation.
In some embodiments, the ultra-wide band state equation is yi=m UWBY(i-1)+NUWBWUWB, where Yi is the second state quantity of the vehicle at the current time, is the optimal estimation of the second state quantity of the vehicle at the i-th time (the current time), Y (i-1) is the second state quantity of the vehicle at the previous time, that is, the second state quantity of the vehicle at the i-1-th time, M UWB is a matrix reflecting the evolution rule of the second state of the vehicle over time, N UWB is a UWB noise matrix, and is used to represent the influence of UWB noise on the second state transition of the vehicle, and W UWB is UWB noise, which may represent uncertainty in the transition process of the second state of the vehicle. And modeling each item of data in the running process of the vehicle based on the factor graph model, defining a second state quantity of the vehicle at each moment as each second variable node, solving the second variable nodes at each moment according to Gaussian distribution from the second state transition from one moment to the next moment, and obtaining the mean value and variance of the second state quantity at each moment, wherein the second state transition has randomness, so that the change of the second state of the vehicle can be used as a probability event. In the factor graph model, a second state transition factor node may be constructed corresponding to a process of a second state transition of the vehicle, the second state transition factor node reflecting a probability dependence of evolution over time between two second state quantities of adjacent time steps of the vehicle. By gaussian-distributing the second state quantity at adjacent times (i.e., the last time and the next time) of the vehicle, a probability distribution of the second state quantity between different times, i.e., a second gaussian distribution result, can be obtained. And constructing a second state transfer factor node based on the second Gaussian distribution result and the ultra-wide band state equation.
Referring to fig. 4, a flowchart of a vehicle positioning method is shown in fig. 4, and the steps include:
step 401, initializing measurement information of an inertial measurement unit and an ultra-wideband;
step 402, establishing a factor graph model;
step 403, inputting the first position information and the second position information, and if not, executing step 406;
step 404, calculating the mean and variance of the first variable node and the mean and variance of the second variable node;
Step 405, fusing the first variable node and the first variable node;
and step 406, stopping data updating and maintaining the original data.
In the above step, IMU data and UWB data required for vehicle positioning are initialized in step 401, and the positioning calculation is ready to be started. The IMU provides information on acceleration, angular velocity, etc. of the vehicle, and the UWB provides information on the exact distance between the vehicle and the UWB base stations and information on the location of the respective UWB base stations. In step 402, a factor graph model framework for data fusion and positioning optimization is constructed, the factor graph model can integrate IMU data and UWB data, and express relevance and uncertainty between the IMU data and the UWB data through a Bayesian network structure, so that efficient probabilistic reasoning and optimal state estimation of a vehicle are facilitated. In step 404, the first position information (vehicle position information calculated based on IMU measurement data) and the second position information (vehicle position information calculated based on UWB measurement data) are input into the factor graph model, and if there is no new data, step 406 is performed. The confidence and central trend of the vehicle position estimate based on both sensor data is quantified by calculating the mean and variance of the first variable node and the second variable node in step 404, which is an important step for probability fusion and optimization, providing the underlying data for the subsequent fusion process. In step 405, the first variable node and the second variable node are fused by the factor graph model, and the optimal state estimation is performed by the probability theory method (such as bayesian filtering or particle filtering), so as to obtain more accurate target position information of the vehicle.
The simulation analysis is carried out on the vehicle positioning provided by the application, and compared with a method using only IMU positioning, the simulation software is Matlab. After the real motion trail of the vehicle is set, the motion trail of the vehicle is simulated by using two methods respectively, so that the predicted motion trail of the vehicle is obtained. And calculating the x-axis error and the y-axis error of the predicted track and the real track in the two-dimensional plane, and carrying out comparative analysis on the errors of the x-axis and the y-axis to obtain an error schematic diagram shown in figure 5. The absolute values of the x-axis and y-axis errors obtained by using only the IMU positioning method are respectively 0.98m and 1.12m, and the absolute values of the x-axis and y-axis errors after IMU/UWB data fusion (the positioning method of the application) are respectively 0.38m and 0.31m. Therefore, compared with the method which only uses the IMU for positioning, the method provided by the application has smaller result error and higher positioning precision.
Any combination of the above optional solutions may be adopted to form an optional embodiment of the present application, which is not described herein.
The following are examples of the apparatus of the present application that may be used to perform the method embodiments of the present application. For details not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the method of the present application.
Fig. 6 is a schematic diagram of a vehicle positioning device according to an embodiment of the present application. As shown in fig. 5, the vehicle positioning device includes:
The acquiring module 601 is configured to acquire first measurement data and second measurement data of a vehicle at a current moment, where the first measurement data is vehicle speed information measured by an inertial measurement unit on the vehicle, and the second measurement data is position information of a plurality of ultra wideband base stations and distance information corresponding to the vehicle by each ultra wideband base station;
The calculation module 602 is configured to determine, according to vehicle speed information measured by an inertial measurement unit on a vehicle at a current time, first position information corresponding to the vehicle at the current time, and determine, according to position information of a plurality of ultra wideband base stations at the current time and distance information corresponding to the vehicle respectively, second position information corresponding to the vehicle at the current time;
The prediction module 603 is configured to predict, based on first location information corresponding to the current time of the vehicle and second location information corresponding to the current time of the vehicle, the location information of the vehicle at the current time by using a factor graph model, to obtain target location information of the vehicle at the current time, where the factor graph model is constructed based on historical vehicle speed information, historical location information of a plurality of ultra wideband base stations, and distance information corresponding to the historical vehicle by each ultra wideband base station.
According to the technical scheme provided by the embodiment of the application, the first measurement data and the second measurement data of the vehicle at the current moment are acquired through the acquisition module 601, the first measurement data are vehicle speed information measured by the inertial measurement unit on the vehicle, the inertial measurement unit can provide acceleration and angular speed of the vehicle in real time, and the calculation module 602 can calculate a preliminary position estimation of the vehicle at the current moment, namely, the first position information corresponding to the vehicle at the current moment, based on the vehicle speed information measured by the inertial measurement unit on the vehicle at the current moment. The second measurement data is the position information of a plurality of ultra-wideband base stations at the current moment and the distance information of each ultra-wideband base station corresponding to the vehicle respectively, which are obtained through an ultra-wideband wireless positioning method, and the calculation module 602 can determine the position of the vehicle, namely the second position information of the vehicle corresponding to the current moment, by utilizing the multilateral positioning principle and combining the known coordinates of the plurality of ultra-wideband base stations and the distance between the vehicle and each base station. Based on the first position information corresponding to the current moment of the vehicle and the second position information corresponding to the current moment of the vehicle, the position information of the vehicle at the current moment of the vehicle is predicted through a prediction module 603, positioning data of an inertia measurement unit and ultra-wideband are integrated, the first position information corresponding to the current moment of the vehicle and the second position information corresponding to the current moment of the vehicle are associated through a factor graph model, fusion is conducted according to a maximum posterior probability principle, the position of the current moment of the vehicle is predicted, and target position information of the vehicle at the current moment of the vehicle is obtained. In the running process of the vehicle, first measurement data and second measurement data are collected in real time, corresponding first position information and second position information at all times are obtained through calculation, the first position information and the second position information are input into a factor graph model, iterative calculation is continuously carried out, and target position information of the vehicle at all times is updated, so that real-time positioning of the vehicle is achieved. The factor graph model is constructed and trained based on historical vehicle speed information, historical position information of a plurality of ultra-wideband base stations and distance information corresponding to the historical vehicles of the ultra-wideband base stations. Through the steps, the characteristics of continuous tracking capacity of the inertial measurement unit and high ultra-wideband positioning accuracy can be utilized, the inertial measurement unit and different positioning data sources of the ultra-wideband are effectively integrated based on the factor graph model to perform data fusion, the limitation of a single sensor is overcome by utilizing the mutual dependency relationship and uncertainty information between the inertial measurement unit and the ultra-wideband, the problem of error accumulation of the inertial measurement unit after long-term use is restrained, the overall stability and accuracy of positioning are improved, the problem of inaccurate positioning of other positioning technologies under the condition of GNSS signal failure in the prior art is solved, and the positioning accuracy of a vehicle under a satellite rejection environment is improved.
In some embodiments, prior to predicting the location information of the vehicle at the current time by the factor graph model, the vehicle locating device is configured to obtain first measurement data of the vehicle at each historical time and second measurement data of the vehicle at each historical time; constructing a first measurement factor node and a second measurement factor node based on a measurement equation, wherein the first measurement factor node is connected with first measurement data of the vehicle at each historical moment and a corresponding first variable node, and the second measurement factor node is connected with second measurement data of the vehicle at each historical moment and a corresponding second variable node; the position information of the vehicle at each history moment is used as a fusion variable node, a first state transfer factor node and a second state transfer factor node are constructed based on a state equation, the first state transfer factor node is connected with the first variable node at the history moment before and after, the second state transfer factor node is connected with the second variable node at the history moment before and after, and the fusion variable node is obtained by fusion of the corresponding first state transfer factor node and the second state transfer factor node; and constructing a factor graph model according to the first measurement factor node, the second measurement factor node, the first state transfer factor node, the second state transfer factor node, the first variable node, the second variable node and the fusion variable node.
In some embodiments, the prediction module 603 is configured to calculate, through the first measurement factor node, the first state transition factor node, and the first state quantity of the vehicle at the previous time, the first position information corresponding to the current time, so as to obtain a first state prediction value of the vehicle corresponding to the current time and related to the inertial measurement unit; calculating second position information corresponding to the current moment through the second measurement factor node, the second state transfer factor node and the second state quantity of the vehicle at the previous moment to obtain a second state pre-measurement value of the vehicle corresponding to the current moment and related to the inertia measurement unit; and carrying out fusion processing on the basis of the vehicle first state prediction quantity corresponding to the current moment and the vehicle second state prediction quantity corresponding to the current moment and related to the ultra-wideband to obtain the target position information of the vehicle at the current moment.
In some embodiments, the prediction module 603 is configured to predict the first state quantity of the vehicle at the current time according to the first state quantity of the vehicle at the previous time and the first state transition factor node, so as to obtain a first initial state prediction quantity of the vehicle at the current time; performing error calculation based on the vehicle first initial state predicted value of the vehicle at the current moment and first position information corresponding to the current moment to obtain first loss; updating the first measurement factor node according to the first loss to obtain an updated first measurement factor node; updating the first state transfer factor node based on the first state quantity of the vehicle at the previous moment and the first position information corresponding to the current moment to obtain an updated first state transfer factor node; and calculating the first position information corresponding to the current moment through the updated first measurement factor node to obtain the vehicle first state prediction quantity corresponding to the current moment and related to the inertia measurement unit.
In some embodiments, the prediction module 603 is configured to predict the second state quantity of the vehicle at the current time according to the second state quantity of the vehicle at the previous time and the second state transition factor node, so as to obtain a second initial state prediction quantity of the vehicle at the current time; performing error calculation based on the vehicle second initial state predicted value of the vehicle at the current moment and second position information corresponding to the current moment to obtain second loss; updating the second measurement factor node according to the second loss to obtain an updated second measurement factor node; updating the second state transfer factor node based on the second state quantity of the vehicle at the previous moment and the second position information corresponding to the current moment to obtain an updated second state transfer factor node; and calculating the second position information corresponding to the current moment through the updated second measurement factor node to obtain the second state prediction quantity of the vehicle corresponding to the current moment and related to the inertia measurement unit.
In some embodiments, the vehicle speed information includes an acceleration at a current time and an angular velocity at the current time, and the calculation module 602 is configured to integrate the acceleration at the current time to obtain a displacement of the vehicle; integrating the angular speed at the current moment to obtain the steering information of the vehicle; and obtaining first position information corresponding to the vehicle at the current moment based on the displacement of the vehicle and the steering information of the vehicle.
In some embodiments, the state equations include an inertial measurement unit state equation and an ultra-wide band state equation, and the vehicle positioning device is configured to perform gaussian distribution on first state quantities of adjacent moments of the vehicle to obtain a first gaussian distribution result; constructing a first state transition factor node based on a first Gaussian distribution result and an inertial measurement unit state equation, wherein the first state transition factor node represents probability distribution of state transition from one moment to the next moment; carrying out Gaussian distribution on second state quantities of adjacent moments of the vehicle to obtain a second Gaussian distribution result; and constructing a second state transfer factor node based on the second Gaussian distribution result and the ultra-wide band state equation, wherein the second state transfer factor node represents the probability distribution of state transfer from one moment to the next moment.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
Fig. 7 is a schematic diagram of an electronic device 7 according to an embodiment of the present application. As shown in fig. 7, the electronic device 7 of this embodiment includes: a processor 701, a memory 702 and a computer program 703 stored in the memory 702 and executable on the processor 701. The steps of the various method embodiments described above are implemented by the processor 701 when executing the computer program 703. Or the processor 701, when executing the computer program 703, performs the functions of the modules/units in the various device embodiments described above.
The electronic device 7 may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The electronic device 7 may include, but is not limited to, a processor 701 and a memory 702. It will be appreciated by those skilled in the art that fig. 7 is merely an example of the electronic device 7 and is not limiting of the electronic device 7 and may include more or fewer components than shown, or different components.
The Processor 701 may be a central processing unit (Central Processing Unit, CPU) or other general purpose Processor, digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like.
The memory 702 may be an internal storage unit of the electronic device 7, for example, a hard disk or a memory of the electronic device 7. The memory 702 may also be an external storage device of the electronic device 7, such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the electronic device 7. The memory 702 may also include both internal storage units and external storage devices of the electronic device 7. The memory 702 is used to store computer programs and other programs and data required by the electronic device.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a readable storage medium (e.g., a computer readable storage medium). Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, and the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. The computer program may comprise computer program code, which may be in source code form, object code form, executable file or in some intermediate form, etc. The computer readable storage medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims (10)

1. A vehicle positioning method, characterized by comprising:
Acquiring first measurement data and second measurement data of a vehicle at the current moment, wherein the first measurement data are vehicle speed information measured by an inertial measurement unit on the vehicle, and the second measurement data are position information of a plurality of ultra-wideband base stations and distance information corresponding to the vehicle respectively by the ultra-wideband base stations;
Determining first position information corresponding to the vehicle at the current moment according to the vehicle speed information measured by the vehicle inertia measuring unit at the current moment, and determining second position information corresponding to the vehicle at the current moment according to the position information of a plurality of ultra-wideband base stations at the current moment and each distance information corresponding to the vehicle by each ultra-wideband base station;
And predicting the position information of the vehicle at the current moment according to a factor graph model based on the first position information of the vehicle at the current moment and the second position information of the vehicle at the current moment to obtain the target position information of the vehicle at the current moment, wherein the factor graph model is constructed based on historical vehicle speed information, historical position information of a plurality of ultra-wideband base stations and distance information corresponding to the historical vehicle respectively by the ultra-wideband base stations.
2. The method of claim 1, wherein the predicting, by a factor graph model, the location information of the vehicle at the current time, further comprises:
Acquiring first measurement data of a vehicle at each historical moment and second measurement data of the vehicle at each historical moment;
constructing a first measurement factor node and a second measurement factor node based on a measurement equation, wherein the first measurement factor node is connected with first measurement data of the vehicle at each historical moment and a corresponding first variable node, and the second measurement factor node is connected with second measurement data of the vehicle at each historical moment and a corresponding second variable node;
The position information of the vehicle at each history moment is used as a fusion variable node, a first state transfer factor node and a second state transfer factor node are constructed based on a state equation, the first state transfer factor node is connected with the first variable node at the history moment before and after, the second state transfer factor node is connected with the second variable node at the history moment before and after, and the fusion variable node is obtained by fusion of the corresponding first state transfer factor node and second state transfer factor node;
And constructing the factor graph model according to the first measurement factor node, the second measurement factor node, the first state transfer factor node, the second state transfer factor node, the first variable node, the second variable node and the fusion variable node.
3. The method according to claim 2, wherein the predicting, by a factor graph model, the position information of the vehicle at the current time based on the first position information of the vehicle at the current time and the second position information of the vehicle at the current time to obtain the target position information of the vehicle at the current time includes:
Calculating first position information corresponding to the current moment through the first measurement factor node, the first state transfer factor node and a vehicle first state quantity at the previous moment to obtain a vehicle first state pre-measurement value corresponding to the current moment and related to the inertia measurement unit;
Calculating second position information corresponding to the current moment through the second measurement factor node, the second state transfer factor node and a vehicle second state quantity at the previous moment to obtain vehicle second state pre-measurement corresponding to the current moment and related to the inertia measurement unit;
and carrying out fusion processing on the basis of the vehicle first state prediction quantity corresponding to the current moment and the vehicle second state prediction quantity corresponding to the current moment and related to the ultra-wideband, so as to obtain the target position information of the vehicle at the current moment.
4. The method according to claim 3, wherein the calculating the first position information corresponding to the current time by the first measurement factor node, the first state transition factor node, and the vehicle first state quantity at the previous time to obtain the vehicle first state prediction value corresponding to the current time with respect to the inertial measurement unit includes:
Predicting the first state quantity of the vehicle at the current moment according to the first state quantity of the vehicle at the previous moment and the first state transfer factor node to obtain a first initial state prediction quantity of the vehicle at the current moment;
performing error calculation based on a vehicle first initial state prediction amount of a vehicle at the current moment and first position information corresponding to the current moment to obtain first loss;
Updating the first measurement factor node according to the first loss to obtain an updated first measurement factor node;
updating the first state transfer factor node based on the first state quantity of the vehicle at the previous moment and the first position information corresponding to the current moment to obtain an updated first state transfer factor node;
and calculating the first position information corresponding to the current moment through the updated first measurement factor node to obtain the vehicle first state prediction quantity corresponding to the current moment and related to the inertia measurement unit.
5. A method according to claim 3, wherein the calculating the second position information corresponding to the current time by the second measurement factor node, the second state transition factor node, and the vehicle second state quantity at the previous time to obtain the vehicle second state prediction value corresponding to the current time with respect to the inertial measurement unit includes:
Predicting the second state quantity of the vehicle at the current moment according to the second state quantity of the vehicle at the previous moment and the second state transfer factor node to obtain a second initial state prediction quantity of the vehicle at the current moment;
performing error calculation based on the predicted value of the second initial state of the vehicle at the current moment and the second position information corresponding to the current moment to obtain second loss;
Updating the second measurement factor node according to the second loss to obtain an updated second measurement factor node;
updating the second state transfer factor node based on the second state quantity of the vehicle at the previous moment and the second position information corresponding to the current moment to obtain an updated second state transfer factor node;
And calculating the second position information corresponding to the current moment through the updated second measurement factor node to obtain the vehicle second state prediction quantity corresponding to the current moment and related to the inertia measurement unit.
6. The method according to claim 1, wherein the vehicle speed information includes an acceleration at the current time and an angular speed at the current time, and the determining the first position information corresponding to the vehicle at the current time based on the vehicle speed information measured by the on-vehicle inertia measurement unit at the current time includes:
integrating the acceleration at the current moment to obtain the displacement of the vehicle;
Integrating the angular speed at the current moment to obtain steering information of the vehicle;
And obtaining first position information corresponding to the vehicle at the current moment based on the displacement of the vehicle and the steering information of the vehicle.
7. The method of claim 2, wherein the state equations comprise inertial measurement unit state equations and ultra-wideband state equations, the constructing first and second state transfer factor nodes based on the state equations comprises:
Carrying out Gaussian distribution on first state quantities of adjacent moments of the vehicle to obtain a first Gaussian distribution result;
Constructing a first state transition factor node based on the first Gaussian distribution result and the inertial measurement unit state equation, wherein the first state transition factor node represents probability distribution of state transition from one moment to the next moment;
carrying out Gaussian distribution on second state quantities of adjacent moments of the vehicle to obtain a second Gaussian distribution result;
And constructing a second state transfer factor node based on the second Gaussian distribution result and the ultra-wide band state equation, wherein the second state transfer factor node represents probability distribution of state transfer from one moment to the next moment.
8. A vehicle positioning device, characterized by comprising:
The acquisition module is used for acquiring first measurement data and second measurement data of a vehicle at the current moment, wherein the first measurement data are vehicle speed information measured by an inertial measurement unit on the vehicle, and the second measurement data are position information of a plurality of ultra-wideband base stations and distance information corresponding to the vehicle respectively;
The calculation module is used for determining first position information corresponding to the vehicle at the current moment according to the vehicle speed information measured by the vehicle-mounted inertia measurement unit at the current moment, and determining second position information corresponding to the vehicle at the current moment according to the position information of a plurality of ultra-wideband base stations at the current moment and the distance information corresponding to the vehicle of each ultra-wideband base station respectively;
The prediction module is used for predicting the position information of the vehicle at the current moment through a factor graph model based on the first position information of the vehicle at the current moment and the second position information of the vehicle at the current moment to obtain the target position information of the vehicle at the current moment, and the factor graph model is constructed based on historical vehicle speed information, historical position information of a plurality of ultra-wideband base stations and distance information corresponding to the historical vehicle respectively by the ultra-wideband base stations.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when the computer program is executed.
10. A readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 7.
CN202410362990.7A 2024-03-28 2024-03-28 Vehicle positioning method, device, electronic equipment and readable storage medium Active CN118191891B (en)

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