CN108426581A - Vehicle pose determines method, apparatus and computer readable storage medium - Google Patents
Vehicle pose determines method, apparatus and computer readable storage medium Download PDFInfo
- Publication number
- CN108426581A CN108426581A CN201810017856.8A CN201810017856A CN108426581A CN 108426581 A CN108426581 A CN 108426581A CN 201810017856 A CN201810017856 A CN 201810017856A CN 108426581 A CN108426581 A CN 108426581A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- motion
- state
- data
- current time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C23/00—Combined instruments indicating more than one navigational value, e.g. for aircraft; Combined measuring devices for measuring two or more variables of movement, e.g. distance, speed or acceleration
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a kind of vehicle poses to determine that method, this method include：The state of motion of vehicle that current time is obtained by presetting sensor observes data；Based on default first model, the state of motion of vehicle prediction data at current time is determined；The state of motion of vehicle at the current time is observed into data and prediction data, inputs and presets the second model, obtains the state of motion of vehicle optimization data at current time；Optimize data according to the state of motion of vehicle at the current time, determines the current pose of the vehicle.The invention also discloses a kind of vehicle pose determining device and computer readable storage mediums.The present invention realizes the optimization for observing state of motion of vehicle data, makes it closer to the actual value of state of motion of vehicle, and then determines vehicle pose based on state of motion of vehicle optimization data, improves the accuracy of vehicle pose.
Description
Technical field
The present invention relates to automatic driving vehicle technical fields more particularly to a kind of vehicle pose to determine method, apparatus and meter
Calculation machine readable storage medium storing program for executing.
Background technology
The crowds such as automatic driving vehicle is a kind of intelligent automobile, and collection automatically controls, architecture, artificial intelligence, vision calculate
More technologies in one, be computer science, pattern-recognition and intelligent control technology high development product, and weigh a state
One important symbol of family research strength and industrial level, has broad application prospects in national defence and national economy field.
The automated driving system of automatic driving vehicle needs the angle information provided according to IMU sensors to determine vehicle position
Appearance, for assessing automatic driving vehicle actual travel route and it is expected the difference between travel route.Automatic driving vehicle position at present
The angular observation that appearance is directly provided by IMU sensors is calculated, certain poor due to existing between observation and actual value
Different, this can cause result of calculation inaccurate.
Invention content
The main purpose of the present invention is to provide a kind of vehicle poses to determine method, apparatus and computer-readable storage medium
Matter, it is intended to by way of the existing angular observation calculating automatic driving vehicle pose provided IMU sensors is provided, calculate knot
The inaccurate technical problem of fruit.
To achieve the above object, a kind of vehicle pose of present invention offer determines that method, this method include：
The state of motion of vehicle that current time is obtained by presetting sensor observes data；
Based on default first model, the state of motion of vehicle prediction data at current time is determined；
The state of motion of vehicle at the current time is observed into data and prediction data, inputs and presets the second model, obtain
The state of motion of vehicle at current time optimizes data；
Optimize data according to the state of motion of vehicle at the current time, determines the current pose of the vehicle.
Optionally, it is described by preset sensor obtain current time state of motion of vehicle observe data the step of it
Afterwards, including：
The first model is established, first model is used to calculate the state of motion of vehicle prediction data at current time.
Optionally, described the step of being based on default first model, determining the state of motion of vehicle prediction data at current time
Including：
The state of motion of vehicle for obtaining current time previous moment optimizes data；
The state of motion of vehicle of the current time previous moment is optimized into data, inputs and presets the first model, worked as
The state of motion of vehicle prediction data at preceding moment.
Optionally, the state of motion of vehicle observation data at the current time include that the course heading of current time vehicle is seen
Measured value, course angular speed observation and course angular acceleration observation.
Optionally, described that data are optimized according to the state of motion of vehicle at the current time, determine the current of the vehicle
The step of pose includes：
Based on default transformation model, the state of motion of vehicle at the current time is optimized into data, is converted to the vehicle
Current pose.
In addition, to achieve the above object, the present invention also provides a kind of vehicle pose determining device, the vehicle pose determines
Device includes：Memory, processor and to be stored in the vehicle pose that can be run on the memory and on the processor true
Determine program, the vehicle pose determines realizes following steps when program is executed by the processor：
The state of motion of vehicle that current time is obtained by presetting sensor observes data；
Based on default first model, the state of motion of vehicle prediction data at current time is determined；
The state of motion of vehicle at the current time is observed into data and prediction data, inputs and presets the second model, obtain
The state of motion of vehicle at current time optimizes data；
Optimize data according to the state of motion of vehicle at the current time, determines the current pose of the vehicle.
Optionally, the vehicle pose determines also realizes following steps when program is executed by the processor：
The first model is established, first model is used to calculate the state of motion of vehicle prediction data at current time.
Optionally, the vehicle pose determines also realizes following steps when program is executed by the processor：
The state of motion of vehicle for obtaining current time previous moment optimizes data；
The state of motion of vehicle of the current time previous moment is optimized into data, inputs and presets the first model, worked as
The state of motion of vehicle prediction data at preceding moment.
Optionally, the vehicle pose determines also realizes following steps when program is executed by the processor：
Based on default transformation model, the state of motion of vehicle at the current time is optimized into data, is converted to the vehicle
Current pose.
In addition, to achieve the above object, it is described computer-readable the present invention also provides a kind of computer readable storage medium
It is stored with vehicle pose on storage medium and determines program, the vehicle pose, which determines, realizes following step when program is executed by processor
Suddenly：
The state of motion of vehicle that current time is obtained by presetting sensor observes data；
Based on default first model, the state of motion of vehicle prediction data at current time is determined；
The state of motion of vehicle at the current time is observed into data and prediction data, inputs and presets the second model, obtain
The state of motion of vehicle at current time optimizes data；
Optimize data according to the state of motion of vehicle at the current time, determines the current pose of the vehicle.
The state of motion of vehicle that the present invention obtains current time by presetting sensor observes data；Based on default first mould
Type determines the state of motion of vehicle prediction data at current time；By the state of motion of vehicle at current time observation data and
Prediction data inputs and presets the second model, obtains the state of motion of vehicle optimization data at current time；According to the current time
State of motion of vehicle optimize data, determine the current pose of the vehicle.By the above-mentioned means, the present invention is by transporting vehicle
Dynamic state observation data optimize, and make it closer to the actual value of state of motion of vehicle, and then are based on state of motion of vehicle
Optimization data determine vehicle pose, improve the accuracy of vehicle pose.
Description of the drawings
Fig. 1 is the terminal structure schematic diagram for the hardware running environment that the embodiment of the present invention is related to；
Fig. 2 is the flow diagram that vehicle pose of the present invention determines method first embodiment；
Fig. 3 is the flow diagram that vehicle pose of the present invention determines method 3rd embodiment；
Fig. 4 is the flow diagram that vehicle pose of the present invention determines method fourth embodiment.
The embodiments will be further described with reference to the accompanying drawings for the realization, the function and the advantages of the object of the present invention.
Specific implementation mode
It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not intended to limit the present invention.
The primary solutions of the embodiment of the present invention are：The state of motion of vehicle at current time is obtained by presetting sensor
Observe data；Based on default first model, the state of motion of vehicle prediction data at current time is determined；By the current time
State of motion of vehicle observes data and prediction data, inputs and presets the second model, and the state of motion of vehicle for obtaining current time is excellent
Change data；Optimize data according to the state of motion of vehicle at the current time, determines the current pose of the vehicle.
As shown in Figure 1, the affiliated terminal structure of device for the hardware running environment that Fig. 1, which is the embodiment of the present invention, to be related to shows
It is intended to.
Terminal of the embodiment of the present invention carries the automated driving system of automatic driving vehicle.
As shown in Figure 1, the terminal may include：Processor 1001, such as CPU, communication bus 1002, user interface
1003, network interface 1004, memory 1005.Wherein, communication bus 1002 is for realizing the connection communication between these components.
User interface 1003 may include display screen (Display), input unit such as keyboard (Keyboard), optional user interface
1003 can also include standard wireline interface and wireless interface.Network interface 1004 may include optionally that the wired of standard connects
Mouth, wireless interface (such as WI-FI interfaces).Memory 1005 can be high-speed RAM memory, can also be stable memory
(non-volatile memory), such as magnetic disk storage.Memory 1005 optionally can also be independently of aforementioned processor
1001 storage device.
Optionally, terminal can also include camera, RF (Radio Frequency, radio frequency) circuit, sensor, audio
Circuit, Wi-Fi module etc..Wherein, sensor such as optical sensor, motion sensor and other sensors.Specifically, light
Sensor may include ambient light sensor and proximity sensor, wherein ambient light sensor can according to the light and shade of ambient light come
The brightness of display screen is adjusted, proximity sensor can close display screen and/or backlight when mobile terminal is moved in one's ear.As
One kind of motion sensor, gravity accelerometer can detect in all directions the size of (generally three axis) acceleration, quiet
Size and the direction that can detect that gravity when only, the application that can be used to identify mobile terminal posture are (such as horizontal/vertical screen switching, related
Game, magnetometer pose calibrating), Vibration identification correlation function (such as pedometer, tap) etc.；Certainly, mobile terminal can also match
The other sensors such as gyroscope, barometer, hygrometer, thermometer, infrared sensor are set, details are not described herein.
It will be understood by those skilled in the art that the restriction of the not structure paired terminal of terminal structure shown in Fig. 1, can wrap
It includes than illustrating more or fewer components, either combines certain components or different components arrangement.
As shown in Figure 1, as may include that operating system, network are logical in a kind of memory 1005 of computer storage media
Letter module, Subscriber Interface Module SIM and vehicle pose determine program.
In terminal shown in Fig. 1, network interface 1004 is mainly used for connecting background server, is carried out with background server
Data communicate；User interface 1003 is mainly used for connecting client (user terminal), with client into row data communication；And processor
1001 can be used for calling the vehicle pose stored in memory 1005 to determine program, and execute following operation：
The state of motion of vehicle that current time is obtained by presetting sensor observes data；
Based on default first model, the state of motion of vehicle prediction data at current time is determined；
The state of motion of vehicle at the current time is observed into data and prediction data, inputs and presets the second model, obtain
The state of motion of vehicle at current time optimizes data；
Optimize data according to the state of motion of vehicle at the current time, determines the current pose of the vehicle.
Further, processor 1001 can call the vehicle pose stored in memory 1005 to determine program, also execute
It operates below：
The first model is established, first model is used to calculate the state of motion of vehicle prediction data at current time.
Further, processor 1001 can call the vehicle pose stored in memory 1005 to determine program, also execute
It operates below：
The state of motion of vehicle for obtaining current time previous moment optimizes data；
The state of motion of vehicle of the current time previous moment is optimized into data, inputs and presets the first model, worked as
The state of motion of vehicle prediction data at preceding moment.
Further, processor 1001 can call the vehicle pose stored in memory 1005 to determine program, also execute
It operates below：
Based on default transformation model, the state of motion of vehicle at the current time is optimized into data, is converted to the vehicle
Current pose.
Based on above-mentioned hardware configuration, propose that vehicle pose of the present invention determines each embodiment of method.
With reference to Fig. 2, vehicle pose of the present invention determines that a kind of vehicle pose of method first embodiment offer determines method, described
Method includes：
Step S10, the state of motion of vehicle that current time is obtained by presetting sensor observe data；
The present embodiment is applied to the automated driving system of automatic driving vehicle.It is pre- on automatic driving vehicle in the present embodiment
Angle, angular speed and angle of the automatic driving vehicle in three longitude, latitude, height orientation can be measured by, which being first equipped with, accelerates
The installation site of the sensor of degree, sensor can be flexibly arranged, and be not construed as limiting herein.The default sensor can be IMU
(Inertial Measurement Unit, Inertial Measurement Unit) sensor, IMU sensors include three uniaxial acceleration
Meter and three uniaxial gyros can measure course heading, course angular speed and the course angle of object in three dimensions and accelerate
Degree.In the present embodiment, for the course heading of the automatic driving vehicle measured by IMU sensors, course angular speed and course
Angular acceleration is defined as course heading observation, course angular speed observation and course angular acceleration observation, is referred to as vehicle
Motion state observes data.In addition, the automated driving system foundation of default sensor and automatic driving vehicle has communication connection, it can
Connection is established by wired mode or wireless mode, wireless mode can be the connection types such as Wi-Fi, ZigBee, bluetooth.
First, the state of motion of vehicle for current time being obtained by presetting sensor observes data.Specifically, unmanned
Vehicle obtains its state of motion of vehicle measured by current time t during traveling, from IMU sensors and observes data.
Step S20 determines the state of motion of vehicle prediction data at current time based on default first model；
Since course heading observation, course angular speed observation and the course angular acceleration measured by IMU sensors are seen
It is had differences between measured value, with actual value, then also there is by the vehicle pose that course heading observation calculates larger
Error, vehicle pose refer to that position and the posture of vehicle are also wanted for this purpose, the present embodiment optimizes course heading observation
Using the course angular speed observation and course angular acceleration observation measured by IMU sensors, measured by IMU sensors
Course angular speed observation and course angular acceleration observation also optimize, and make full use of the data measured by IMU sensors,
To promote the accuracy of vehicle pose.The present embodiment will add course heading observation, course angular speed observation and course angle
Data definition obtained by speed observation optimizes is that state of motion of vehicle optimizes data, i.e. state of motion of vehicle optimizes data
Including course heading optimal value, course angle speed-optimization value and course angular acceleration optimal value.
Course heading observation, course angular speed observation and course angular acceleration observation are optimized, first had to
The course heading, course angular speed and course angular acceleration of automatic driving vehicle are predicted, it will be to automatic driving vehicle
Data definition obtained by course heading, course angular speed and course angular acceleration are predicted is that state of motion of vehicle predicts number
According to that is, state of motion of vehicle optimization data include that course heading predicted value, course angle rate predictions and course angular acceleration are pre-
Measured value.State of motion of vehicle prediction data can be obtained by the model for calculating state of motion of vehicle prediction data, the model
For state space and observation space model, it is defined as the first model.
Specifically, automated driving system obtains the vehicle movement optimization data at t-1 moment, by the vehicle movement at t-1 moment
Optimize data input state space and observation space model, you can obtain the state of motion of vehicle prediction data of current time t.
The state of motion of vehicle at the current time is observed data and prediction data, inputs and preset the second mould by step S30
Type obtains the state of motion of vehicle optimization data at current time；
And current time t motion state optimize data, then based on current time t state of motion of vehicle observation data and
Prediction data is obtained in conjunction with the lossless Kalman filter model (being defined as the second model) pre-established in automated driving system.
Lossless Kalman filter model is so that nonlinear system equation is suitable for the standard Kalman under linear hypothesis by non-loss transformation
Filtering system.The foundation of lossless Kalman filter model can refer to the prior art, and details are not described herein again.Specifically, automatic Pilot
The state of motion of vehicle of obtained current time t is observed data and prediction data by system, input nondestructive Kalman filter model,
It can be obtained the state of motion of vehicle optimization data of current time t.
Step 40, data are optimized according to the state of motion of vehicle at the current time, determines the current pose of the vehicle.
Later, automated driving system can optimize data according to the state of motion of vehicle of current time t, determine vehicle in t
The pose at moment.
So, at the t+1 moment, when automated driving system obtains the state of motion of vehicle observation data and t at t+1 moment
The state of motion of vehicle at quarter optimizes data, then by the state of motion of vehicle of t moment optimization data input state space and observation
Spatial model obtains the state of motion of vehicle prediction data at t+1 moment, later, the state of motion of vehicle at t+1 moment is observed number
According to prediction data input nondestructive Kalman filter model, you can obtain the t+1 moment state of motion of vehicle optimization data, by
This, calculates the vehicle pose at t+1 moment, and so on, automated driving system can get the vehicle pose of any time, to assess
Difference between automatic driving vehicle vehicle actual travel route and expectation travel route.
In the present embodiment, the state of motion of vehicle for current time being obtained by presetting sensor observes data；Based on pre-
If the first model, the state of motion of vehicle prediction data at current time is determined；The state of motion of vehicle at the current time is seen
Measured data and prediction data input and preset the second model, obtain the state of motion of vehicle optimization data at current time；According to described
The state of motion of vehicle at current time optimizes data, determines the current pose of the vehicle.By the above-mentioned means, the present embodiment is logical
It crosses and state of motion of vehicle observation data is optimized, make it closer to the actual value of state of motion of vehicle, and then be based on vehicle
Motion state optimization data determine vehicle pose, improve the accuracy of vehicle pose.
Vehicle pose of the present invention determines that a kind of vehicle pose of method second embodiment offer determines method, is based on above-mentioned Fig. 2
Shown in embodiment, after step S10, may include：
Step S50, establishes the first model, and first model is used to calculate the state of motion of vehicle prediction number at current time
According to.
First model (state space with observation space model) can be by initial time that automated driving system obtains and first
The state of motion of vehicle observation data after the moment that begin are established, based on these motion states observation data build constant rate of rotation and
Rate pattern, constant rate of rotation and Fast track surgery, to establish the first model.Specific modeling process can refer to the prior art, this
Place repeats no more.
With reference to Fig. 3, vehicle pose of the present invention determines that a kind of vehicle pose of method 3rd embodiment offer determines method, is based on
Above-mentioned embodiment shown in Fig. 2, step S20 may include：
Step S21, the state of motion of vehicle for obtaining current time previous moment optimize data；
The state of motion of vehicle of the current time previous moment is optimized data, inputs and preset the first mould by step S22
Type obtains the state of motion of vehicle prediction data at current time.
From the foregoing it will be appreciated that obtain the state of motion of vehicle optimization data at current time, need to be transported in conjunction with current time vehicle
Dynamic state observation data and prediction data.State of motion of vehicle prediction data will then be transported according to the vehicle of current time previous moment
Dynamic state optimization data obtain, and the state of motion of vehicle of the automated driving system acquisition current time previous moment of the present embodiment is excellent
Change data, is inputted state space and observation space model, you can obtain the state of motion of vehicle prediction data at current time.
With reference to Fig. 4, vehicle pose of the present invention determines that a kind of vehicle pose of method 3rd embodiment offer determines method, is based on
Above-mentioned Fig. 2 and embodiment shown in Fig. 3, step S40 may include：
The state of motion of vehicle at the current time is optimized data, is converted to by step S41 based on default transformation model
The current pose of the vehicle.
In the present embodiment, the model for resolving vehicle pose is previously provided in automated driving system, which is to be based on
The transformation model of geodetic rectangular coordinates in space system, can be used for resolving the pose of automatic driving vehicle, and specific solution process can refer to
The prior art, details are not described herein again.Specifically, automated driving system obtains the vehicle of t moment by lossless Kalman filter model
After motion state optimization data, state of motion of vehicle optimization data are inputted into the modulus of conversion based on geodetic rectangular coordinates in space system
Type, you can obtain the pose of automatic driving vehicle.Optimize the vehicle pose that data calculation goes out by state of motion of vehicle, compared to
The vehicle pose directly calculated using the state of motion of vehicle observation data measured by IMU sensors, accuracy are carried
It rises.
In addition, the embodiment of the present invention also proposes a kind of computer readable storage medium.
It is stored with vehicle pose on computer readable storage medium of the present invention and determines that program, the vehicle pose determine program
Following operation is realized when being executed by processor：
The state of motion of vehicle that current time is obtained by presetting sensor observes data；
Based on default first model, the state of motion of vehicle prediction data at current time is determined；
The state of motion of vehicle at the current time is observed into data and prediction data, inputs and presets the second model, obtain
The state of motion of vehicle at current time optimizes data；
Optimize data according to the state of motion of vehicle at the current time, determines the current pose of the vehicle.
Further, the vehicle pose, which determines, also realizes following operation when program is executed by processor：
The first model is established, first model is used to calculate the state of motion of vehicle prediction data at current time.
Further, the vehicle pose, which determines, also realizes following operation when program is executed by processor：
The state of motion of vehicle for obtaining current time previous moment optimizes data；
The state of motion of vehicle of the current time previous moment is optimized into data, inputs and presets the first model, worked as
The state of motion of vehicle prediction data at preceding moment.
Further, the vehicle pose, which determines, also realizes following operation when program is executed by processor：
Based on default transformation model, the state of motion of vehicle at the current time is optimized into data, is converted to the vehicle
Current pose.
Wherein, the vehicle pose stored on computer readable storage medium of the present invention determines when program is executed by processor
Specific embodiment determines that each embodiment of method is essentially identical with above-mentioned vehicle pose, and therefore not to repeat here.
It should be noted that herein, the terms "include", "comprise" or its any other variant are intended to non-row
His property includes, so that process, method, article or system including a series of elements include not only those elements, and
And further include other elements that are not explicitly listed, or further include for this process, method, article or system institute it is intrinsic
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including this
There is also other identical elements in the process of element, method, article or system.
The embodiments of the present invention are for illustration only, can not represent the quality of embodiment.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can add the mode of required general hardware platform to realize by software, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on this understanding, technical scheme of the present invention substantially in other words does the prior art
Going out the part of contribution can be expressed in the form of software products, which is stored in a storage medium
In (such as ROM/RAM, magnetic disc, CD), including some instructions are used so that a station terminal equipment (can be mobile phone, computer, clothes
Be engaged in device, air conditioner or the network equipment etc.) execute method described in each embodiment of the present invention.
It these are only the preferred embodiment of the present invention, be not intended to limit the scope of the invention, it is every to utilize this hair
Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skills
Art field, is included within the scope of the present invention.
Claims (10)
1. a kind of vehicle pose determines method, which is characterized in that the method includes：
The state of motion of vehicle that current time is obtained by presetting sensor observes data；
Based on default first model, the state of motion of vehicle prediction data at current time is determined；
The state of motion of vehicle at the current time is observed into data and prediction data, inputs and presets the second model, is obtained current
The state of motion of vehicle at moment optimizes data；
Optimize data according to the state of motion of vehicle at the current time, determines the current pose of the vehicle.
2. vehicle pose as described in claim 1 determines method, which is characterized in that described to be obtained currently by default sensor
After the step of state of motion of vehicle observation data at moment, including：
The first model is established, first model is used to calculate the state of motion of vehicle prediction data at current time.
3. vehicle pose as claimed in claim 2 determines method, which is characterized in that it is described based on default first model, it determines
The step of state of motion of vehicle prediction data at current time includes：
The state of motion of vehicle for obtaining current time previous moment optimizes data；
The state of motion of vehicle of the current time previous moment is optimized into data, inputs and presets the first model, when obtaining current
The state of motion of vehicle prediction data at quarter.
4. vehicle pose as described in claim 1 determines method, which is characterized in that the state of motion of vehicle at the current time
Observation data include the course heading observation of current time vehicle, course angular speed observation and the observation of course angular acceleration
Value.
5. vehicle pose as described in claim 1 determines method, which is characterized in that the vehicle according to the current time
Motion state optimizes data, and the step of current pose for determining the vehicle includes：
Based on default transformation model, the state of motion of vehicle at the current time is optimized into data, is converted to working as the vehicle
Preceding pose.
6. a kind of vehicle pose determining device, which is characterized in that the vehicle pose determining device includes：Memory, processor
And it is stored in the vehicle pose that can be run on the memory and on the processor and determines that program, the vehicle pose determine
Program realizes following steps when being executed by the processor：
The state of motion of vehicle that current time is obtained by presetting sensor observes data；
Based on default first model, the state of motion of vehicle prediction data at current time is determined；
The state of motion of vehicle at the current time is observed into data and prediction data, inputs and presets the second model, is obtained current
The state of motion of vehicle at moment optimizes data；
Optimize data according to the state of motion of vehicle at the current time, determines the current pose of the vehicle.
7. vehicle pose determining device as claimed in claim 6, which is characterized in that the vehicle pose determines that program is described
Processor also realizes following steps when executing：
The first model is established, first model is used to calculate the state of motion of vehicle prediction data at current time.
8. vehicle pose determining device as claimed in claim 7, which is characterized in that the vehicle pose determines that program is described
Processor also realizes following steps when executing：
The state of motion of vehicle for obtaining current time previous moment optimizes data；
The state of motion of vehicle of the current time previous moment is optimized into data, inputs and presets the first model, when obtaining current
The state of motion of vehicle prediction data at quarter.
9. vehicle pose determining device as claimed in claim 6, which is characterized in that the vehicle pose determines that program is described
Processor also realizes following steps when executing：
Based on default transformation model, the state of motion of vehicle at the current time is optimized into data, is converted to working as the vehicle
Preceding pose.
10. a kind of computer readable storage medium, which is characterized in that be stored with vehicle position on the computer readable storage medium
Appearance determines program, and the vehicle pose, which determines, realizes following steps when program is executed by processor：
The state of motion of vehicle that current time is obtained by presetting sensor observes data；
Based on default first model, the state of motion of vehicle prediction data at current time is determined；
The state of motion of vehicle at the current time is observed into data and prediction data, inputs and presets the second model, is obtained current
The state of motion of vehicle at moment optimizes data；
Optimize data according to the state of motion of vehicle at the current time, determines the current pose of the vehicle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810017856.8A CN108426581A (en) | 2018-01-08 | 2018-01-08 | Vehicle pose determines method, apparatus and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810017856.8A CN108426581A (en) | 2018-01-08 | 2018-01-08 | Vehicle pose determines method, apparatus and computer readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108426581A true CN108426581A (en) | 2018-08-21 |
Family
ID=63155857
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810017856.8A Pending CN108426581A (en) | 2018-01-08 | 2018-01-08 | Vehicle pose determines method, apparatus and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108426581A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109829004A (en) * | 2018-12-26 | 2019-05-31 | 北京百度网讯科技有限公司 | Data processing method, device, equipment and storage medium based on unmanned vehicle |
| CN110568847A (en) * | 2019-08-30 | 2019-12-13 | 驭势科技（北京）有限公司 | Intelligent control system and method for vehicle, vehicle-mounted equipment and storage medium |
| CN110609539A (en) * | 2018-10-31 | 2019-12-24 | 驭势科技（北京）有限公司 | Path tracking control method, device and system and storage medium |
| CN110823224A (en) * | 2019-10-18 | 2020-02-21 | 中国第一汽车股份有限公司 | Vehicle positioning method and vehicle |
| CN111098850A (en) * | 2018-10-25 | 2020-05-05 | 北京初速度科技有限公司 | Automatic parking auxiliary system and automatic parking method |
| CN111854731A (en) * | 2020-07-22 | 2020-10-30 | 中国第一汽车股份有限公司 | Pose determination method and device, vehicle and storage medium |
| CN112683281A (en) * | 2021-03-11 | 2021-04-20 | 之江实验室 | Automatic driving vehicle joint positioning method based on vehicle kinematics |
| CN114323020A (en) * | 2021-12-06 | 2022-04-12 | 纵目科技（上海）股份有限公司 | Vehicle positioning method, system, device and computer readable storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103123487A (en) * | 2011-11-21 | 2013-05-29 | 上海航天控制工程研究所 | Spacecraft attitude determination method |
| CN106123908A (en) * | 2016-09-08 | 2016-11-16 | 北京京东尚科信息技术有限公司 | Automobile navigation method and system |
| US20160379486A1 (en) * | 2015-03-24 | 2016-12-29 | Donald Warren Taylor | Apparatus and system to manage monitored vehicular flow rate |
| CN107231486A (en) * | 2017-06-28 | 2017-10-03 | 努比亚技术有限公司 | Intelligent control method, terminal and the computer-readable recording medium of a kind of vehicle |
| CN107357168A (en) * | 2017-06-01 | 2017-11-17 | 同济大学 | A kind of unmanned vehicle barrier-avoiding method based on Chance-constrained Model PREDICTIVE CONTROL |
-
2018
- 2018-01-08 CN CN201810017856.8A patent/CN108426581A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103123487A (en) * | 2011-11-21 | 2013-05-29 | 上海航天控制工程研究所 | Spacecraft attitude determination method |
| US20160379486A1 (en) * | 2015-03-24 | 2016-12-29 | Donald Warren Taylor | Apparatus and system to manage monitored vehicular flow rate |
| CN106123908A (en) * | 2016-09-08 | 2016-11-16 | 北京京东尚科信息技术有限公司 | Automobile navigation method and system |
| CN107357168A (en) * | 2017-06-01 | 2017-11-17 | 同济大学 | A kind of unmanned vehicle barrier-avoiding method based on Chance-constrained Model PREDICTIVE CONTROL |
| CN107231486A (en) * | 2017-06-28 | 2017-10-03 | 努比亚技术有限公司 | Intelligent control method, terminal and the computer-readable recording medium of a kind of vehicle |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111098850A (en) * | 2018-10-25 | 2020-05-05 | 北京初速度科技有限公司 | Automatic parking auxiliary system and automatic parking method |
| CN110609539A (en) * | 2018-10-31 | 2019-12-24 | 驭势科技（北京）有限公司 | Path tracking control method, device and system and storage medium |
| CN109829004A (en) * | 2018-12-26 | 2019-05-31 | 北京百度网讯科技有限公司 | Data processing method, device, equipment and storage medium based on unmanned vehicle |
| US11496567B2 (en) | 2018-12-26 | 2022-11-08 | Apollo Intelligent Driving Technology (Beijing) Co., Ltd. | Data processing method, apparatus, device and storage medium based on unmanned vehicle |
| CN110568847B (en) * | 2019-08-30 | 2022-09-23 | 驭势科技（北京）有限公司 | Intelligent control system and method for vehicle, vehicle-mounted equipment and storage medium |
| CN110568847A (en) * | 2019-08-30 | 2019-12-13 | 驭势科技（北京）有限公司 | Intelligent control system and method for vehicle, vehicle-mounted equipment and storage medium |
| CN110823224A (en) * | 2019-10-18 | 2020-02-21 | 中国第一汽车股份有限公司 | Vehicle positioning method and vehicle |
| CN110823224B (en) * | 2019-10-18 | 2021-10-08 | 中国第一汽车股份有限公司 | Vehicle positioning method and vehicle |
| CN111854731A (en) * | 2020-07-22 | 2020-10-30 | 中国第一汽车股份有限公司 | Pose determination method and device, vehicle and storage medium |
| CN112683281A (en) * | 2021-03-11 | 2021-04-20 | 之江实验室 | Automatic driving vehicle joint positioning method based on vehicle kinematics |
| CN112683281B (en) * | 2021-03-11 | 2021-06-04 | 之江实验室 | Automatic driving vehicle joint positioning method based on vehicle kinematics |
| CN114323020A (en) * | 2021-12-06 | 2022-04-12 | 纵目科技（上海）股份有限公司 | Vehicle positioning method, system, device and computer readable storage medium |
| CN114323020B (en) * | 2021-12-06 | 2024-02-06 | 纵目科技（上海）股份有限公司 | Vehicle positioning method, system, equipment and computer readable storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108426581A (en) | Vehicle pose determines method, apparatus and computer readable storage medium | |
| CN110147705B (en) | Vehicle positioning method based on visual perception and electronic equipment | |
| CN110246182B (en) | Vision-based global map positioning method and device, storage medium and equipment | |
| CN108803604A (en) | Vehicular automatic driving method, apparatus and computer readable storage medium | |
| CN109883423B (en) | Positioning method, system, equipment and storage medium based on Kalman filtering | |
| CN103026780B (en) | For controlling the method and apparatus called of sensor | |
| CN105051492B (en) | Mobile terminal apparatus, recording medium and modification method | |
| CN107449433A (en) | The feedback cycle for being used for vehicle observation based on map | |
| CN108965609A (en) | The recognition methods of mobile terminal application scenarios and device | |
| CN107925981A (en) | Expand indoor and outdoor using auxiliary information to detect | |
| US11740321B2 (en) | Visual inertial odometry health fitting | |
| WO2013191967A1 (en) | Routability graph with predetermined number of weighted edges for estimating a trajectory of a mobile device | |
| CN104111088A (en) | System and method for determining sensor accuracy of a portable electronic device | |
| CN109993234B (en) | Unmanned driving training data classification method and device and electronic equipment | |
| CN109084775A (en) | A kind of indoor orientation method and positioning system | |
| CN111986250A (en) | Object volume measuring method, device, measuring equipment and storage medium | |
| US20220155338A1 (en) | Method for testing sensing effect, moving apparatus, electronic device, storage medium, and system for testing sensing effect | |
| Gao et al. | Glow in the dark: Smartphone inertial odometry for vehicle tracking in GPS blocked environments | |
| US20180060715A1 (en) | Industrial electronic badge | |
| CN111856499B (en) | Map construction method and device based on laser radar | |
| CN111289002A (en) | Robot path planning method and system | |
| JP2018194537A (en) | Method, program and system for position determination and tracking | |
| CN113587938B (en) | Vehicle positioning method and device and storage medium | |
| CN111385868A (en) | Vehicle positioning method, system, device and storage medium | |
| US20180364047A1 (en) | Estimation device, estimation method, and non-transitory computer-readable recording medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180821 |