CN106840179A - A kind of intelligent vehicle localization method based on multi-sensor information fusion - Google Patents

A kind of intelligent vehicle localization method based on multi-sensor information fusion Download PDF

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CN106840179A
CN106840179A CN201710131878.2A CN201710131878A CN106840179A CN 106840179 A CN106840179 A CN 106840179A CN 201710131878 A CN201710131878 A CN 201710131878A CN 106840179 A CN106840179 A CN 106840179A
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intelligent vehicle
moment
pose
information
coordinate
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CN106840179B (en
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祝辉
邓炯
余彪
梁华为
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Hefei Institutes of Physical Science of CAS
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Hefei Institutes of Physical Science of CAS
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    • 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
    • G01C21/30—Map- or contour-matching
    • G01C21/32—Structuring or formatting of map data
    • 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
    • G01C21/34—Route searching; Route guidance
    • G01C21/3407—Route searching; Route guidance specially adapted for specific applications
    • G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
    • 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
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement

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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)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The invention discloses a kind of intelligent vehicle localization method based on multi-sensor information fusion, the simple drift phenomenon for relying on GPS location generation can be prevented effectively from, so as to improve the positioning precision of vehicle.Processed by radar data and encoder data, position of the vehicle in local map is obtained using synchronous superposition method (SLAM), position of the vehicle in longitude and latitude system is obtained by GPS location, and by the two by under Coordinate Conversion unification to earth coordinates, reuse Kalman filter and fusion treatment is done to above two positional information, obtain the accurate estimation of vehicle location.The present invention realizes vehicle accurately Global localization based on vehicle-mounted encoder, laser radar, GPS.

Description

A kind of intelligent vehicle localization method based on multi-sensor information fusion
Technical field
The present invention relates to intelligent vehicle localization method field, specifically a kind of intelligent vehicle based on multi-sensor information fusion is determined Position method.
Background technology
One of premise that intelligent vehicle autonomous driving or auxiliary drive seeks to accurately know the position of oneself.Current intelligent vehicle Autonomous driving or auxiliary driving field, using most localization methods are integrated into odometer based on GPS location, or GPS Row positioning.GPS device is used alone to position, due to equipment error in itself, positioning often occurs drift phenomenon, while Some occasions, such as tunnel, built-up urban district, gps signal can weaken rapidly even to be lost.GPS merges fixed with odometer Position, can be more prone to carry out reckoning positioning using odometer when gps signal is weak, and then reduce the pure error triggered by GPS location, but It is to have accumulated error and drift in vehicle travel process due to odometer, odometer reckoning positioning is not equally accurate.
The present invention uses synchronous superposition technology, mileage is counted using radar data is corrected, and obtains To the local location estimation of intelligent vehicle, in conjunction with GPS location, by Coordinate Conversion and Kalman filtering, the two is merged, Obtain accurately intelligent vehicle location estimation.Positioning is merged with odometer relative to GPS, the present invention can preferably complete intelligent vehicle Positioning, be intelligent vehicle automatic Pilot or auxiliary driving provide safeguard.
The content of the invention
It is existing to solve it is an object of the invention to provide a kind of intelligent vehicle localization method based on multi-sensor information fusion Technical intelligence car autonomous driving or the inaccurate problem of auxiliary driving field GPS location.
In order to achieve the above object, the technical solution adopted in the present invention is：
A kind of intelligent vehicle localization method based on multi-sensor information fusion, it is characterised in that：Comprise the following steps：
（1）, photoelectric encoder based on intelligent vehicle and laser radar collection data, and utilize synchronous superposition side Method（SLAM）Obtain positional information of the intelligent vehicle in the local map that SLAM methods build；
（2）, positional information of the intelligent vehicle in geographic coordinate system is obtained by the GPS location of intelligent vehicle；
（3）, by step（1）And step（2）Position letter of the intelligent vehicle for obtaining in local map coordinate system, in geographic coordinate system Breath is unified in earth coordinates by Coordinate Conversion；
（4）, based on step（3）Coordinate transform process, using Kalman Filter Technology by step（1）The position of the intelligent vehicle for obtaining Confidence breath, step（2）The positional information of the intelligent vehicle for obtaining is merged, and obtains intelligent vehicle accurately location estimation.
A kind of described intelligent vehicle localization method based on multi-sensor information fusion, it is characterised in that：Step（1）Tool Body process is as follows：
（1.1）, with intelligent vehicle start when car body center be origin Oo, it is X along car direction of advanceoDirection of principal axis, vertical XoAxle points to car Body left side is YoDirection of principal axis, ZoAxle sets up odometer coordinate system vertically upward.Due to only consider intelligent vehicle move in the horizontal plane and There is no pitching and tumbling action, therefore Z axis coordinate is always 0 and the only change of yaw angle, therefore intelligent vehicle can be obtained Pose model (x, y, θ).By the photoelectric encoder of the left and right wheel of intelligent back wheels of vehicle, intelligent vehicle odometer information is calculated, specifically Process is as follows：
（1.1.1）, 0 moment, pose (x of the intelligent vehicle in odometer coordinate system0, y0, θ0) it is (0,0,0)
（1.1.1）, in the unit sampling time, under odometer coordinate system, the increment Delta S of wheel position can be by below equation Obtain：
Wherein Δ Q is the pulse increment of photoelectric code disk output, and D is wheel diameter, TsIt it is the sampling time, N is on photoelectric code disk Grating sum, K is the photoelectric encoder rate of deceleration；
（1.1.2）, the distance between left and right wheels be ω, the increment of left and right wheel position is respectively Δ S in the unit sampling timeLWith ΔSR, then automobile is from k -1 moment Sk-1 = (xk-1,yk-1,θk-1) arrive k moment Sk = (xk,yk,θk) pose become turn to
（1.1.3）, the odometer information for obtaining moment k intelligent vehicle can be accumulated using the above method；
（1.2）, the observation information that is obtained by mobile lidar and record moment k intelligent vehicle surrounding enviroment；
（1.3）, set up with odometer coordinate system with the coaxial map coordinates system of origin, and under map coordinates system by SLAM calculation Method is patterned and positions.By step（1.1）The odometer information for obtaining is led to as the control input information of SLAM algorithms Cross step（1.2）The radar scanning information for obtaining as SLAM algorithm observation informations, using a kind of based on particle filter SLAM algorithms obtain optimization pose of the intelligent vehicle under map coordinates system, while according to the pose after optimization by k moment intelligent vehicles The obstacle information on periphery carries out building figure.
A kind of described intelligent vehicle localization method based on multi-sensor information fusion, it is characterised in that：Step（1.3） In, when the more excellent pose for obtaining intelligent vehicle is optimized using particle filter with map, the pose at intelligent vehicle current time Pose according to previous moment calculates what is obtained.Know the pose x of k-1 moment intelligent vehiclesk-1, in the control information at k moment is Journey meter information uk, the observation information z at k momentk, estimate the x of the current pose of intelligent vehiclekPosterior probability；
Posterior probability is obtained using sequential importance sampling algorithm and sampling importance resampling methods in particle filter Distribution, is weighted approximately to obtain Posterior probability distribution by the sampling particle to reference distribution,
Specifically include following steps：
（1.3.1）, initialization system, N number of particle is set, each particle represents the current pose of intelligent vehicle, and sample QUOTE , calculate weight QUOTE ；
（1.3.2）, importance sampling, in moment k, according to the control information u of k moment intelligent vehicleskCalculate k moment intelligent vehicle poses Distribution, sampling QUOTE is carried out to each particle ；
（1.3.3）, according to the observation information z at k momentkWith the weights of particle previous moment, k moment particle importance weights are calculated QUOTE , and it is normalized weights;
（1.3.4）, resampling, increase importance weight particle high, delete the low particle of importance weight, calculate posteriority general Rate.
A kind of described intelligent vehicle localization method based on multi-sensor information fusion, it is characterised in that：Step（2）In, Original latitude, longitude, height coordinate of the intelligent vehicle in WGS-84 geographic coordinate systems are obtained by the GPS location of intelligent vehicle.
A kind of described intelligent vehicle localization method based on multi-sensor information fusion, it is characterised in that：Step（3）Middle seat Mark conversion RUP is as follows：
（3.1）, original latitude by intelligent vehicle in WGS-84 coordinate systems, longitude use general gauss projection, be transformed into flat Areal coordinate system, and " northeast day " earth coordinates with intelligent vehicle original position car body center as origin are transformed into, and sat with this Mark system is master coordinate system;
（3.2）, the map coordinates system in intelligent vehicle SLAM algorithms is transformed into earth coordinates, specific conversion method is as follows：Intelligence Warp, latitude and the course angle φ of Startup time intelligent vehicle are obtained when energy car starts by GPS device, by step（3.1）Side Method obtains now coordinate (x of the intelligent vehicle in earth coordinates0,y0), then point (the x in map coordinates systemm,ym) in the earth Coordinate (x in coordinate systemp,yp) be：
A kind of described intelligent vehicle localization method based on multi-sensor information fusion, it is characterised in that：Step（4）Kalman In Filtering Model, step（1）Pose of the intelligent vehicle obtained by SLAM algorithms under earth coordinates is used as predicted value；Step （2）Pose of the intelligent vehicle obtained by GPS under earth coordinates is used as observation.
The observational equation of definition status space transfer equation and state space is as follows：
,
,
Wherein X (k) is the position and posture vector at k moment, and A (k) is process matrix, and H (k) is calculation matrix, and W (k) makes an uproar for process Sound matrix, its covariance is Q, and V (k) is measurement noise, and its covariance is R.The process of Kalman filtering is as follows：
Predictive equation group：
,
,
Renewal equation group：
,
,
,
Wherein：
X (k | k-1) is to estimate position and posture vector at the k moment；
X (k-1 | k-1) is the optimal position and posture at k-1 moment；
Kg (k) is the kalman gain at k moment；
X (k | k) is that the optimal position and posture at k moment is estimated；
Q is the covariance of systematic procedure noise W (k)；
R is the covariance of systematic survey noise V (k).
A kind of described intelligent vehicle localization method based on multi-sensor information fusion, it is characterised in that：State-noise square The value of battle array W (k) and measurement noise matrix V (k) is white Gaussian noise, and wherein measurement noise matrix V (k) is fixed according to GPS device Position quality adaptation regulation.
The present invention uses synchronous superposition technology, mileage is counted using radar data is corrected, and obtains To the location estimation of the local optimization of intelligent vehicle, in conjunction with GPS location, by Coordinate Conversion and Kalman filtering, the two is entered Row fusion, obtains accurately intelligent vehicle location estimation.Positioning is merged with odometer relative to GPS, the present invention can be preferably complete It is that the automatic Pilot of intelligent vehicle or auxiliary driving provide safeguard into the positioning of intelligent vehicle.
Compared with the prior art, beneficial effects of the present invention are embodied in：
It is of the invention it is a kind of based on laser radar, encoder, GPS Multi-information acquisitions intelligent vehicle localization method, with existing intelligence The localization method of energy car is compared, and in the case of more using a sensor laser radar, uses advanced synchronous positioning and structure Construction method, and using the method for information fusion, the positioning precision of intelligent vehicle is effectively improved, it is intelligent vehicle automatic Pilot or auxiliary Driving provides safeguard.
Brief description of the drawings
Fig. 1 is the flow chart of the intelligent vehicle localization method based on multi-sensor information fusion of the invention.
Fig. 2 is the structure chart of the synchronous superposition method that the present invention is used.
Fig. 3 is intelligent vehicle sensor structure figure of the invention.
Specific embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings.
As shown in figure 1, a kind of intelligent vehicle localization method based on multi-sensor information fusion, comprises the following steps：
1st, a kind of intelligent vehicle localization method based on multi-sensor information fusion, it is characterised in that：Comprise the following steps：
（1）, photoelectric encoder based on intelligent vehicle and laser radar collection data, and utilize synchronous superposition side Method（SLAM）Obtain positional information of the intelligent vehicle in the local map that SLAM methods build；
（2）, positional information of the intelligent vehicle in geographic coordinate system is obtained by the GPS location of intelligent vehicle；
（3）, by step（1）And step（2）Position letter of the intelligent vehicle for obtaining in local map coordinate system, in geographic coordinate system Breath is unified in earth coordinates by Coordinate Conversion；
（4）, based on step（3）Coordinate transform process, using Kalman Filter Technology by step（1）The position of the intelligent vehicle for obtaining Confidence breath, step（2）The positional information of the intelligent vehicle for obtaining is merged, and obtains intelligent vehicle accurately location estimation.
For the synchronous superposition method described in step 1, idiographic flow such as Fig. 2, while according to shown in Fig. 3, Laser radar is mounted in intelligent vehicle headstock centre, on the axle of the left and right wheels of trailing wheel, algorithm specifically includes following encoder Step：
（1.1）, with intelligent vehicle start when car body center be origin Oo, it is X along car direction of advanceoDirection of principal axis, vertical XoAxle points to car Body left side is YoDirection of principal axis, ZoAxle sets up odometer coordinate system vertically upward.Due to only consider intelligent vehicle move in the horizontal plane and There is no pitching and tumbling action, therefore Z axis coordinate is always 0 and the only change of yaw angle, therefore intelligent vehicle can be obtained Pose model (x, y, θ).By the photoelectric encoder of the left and right wheel of intelligent back wheels of vehicle, intelligent vehicle odometer information is calculated, specifically Process is as follows：
（1.1.1）, 0 moment, pose (x of the intelligent vehicle in odometer coordinate system0, y0, θ0) it is (0,0,0)
（1.1.1）, in the unit sampling time, under odometer coordinate system, the increment Delta S of wheel position can be by below equation Obtain：
Wherein Δ Q is the pulse increment of photoelectric code disk output, and D is wheel diameter, TsIt it is the sampling time, N is on photoelectric code disk Grating sum, K is the photoelectric encoder rate of deceleration；
（1.1.2）, the distance between left and right wheels be ω, the increment of left and right wheel position is respectively Δ S in the unit sampling timeLWith ΔSR, then automobile is from k -1 moment Sk-1 = (xk-1,yk-1,θk-1) arrive k moment Sk = (xk,yk,θk) pose become turn to
（1.1.3）, the odometer information for obtaining moment k intelligent vehicle can be accumulated using the above method；
（1.2）, the observation information that is obtained by mobile lidar and record moment k intelligent vehicle surrounding enviroment；
（1.3）, set up with odometer coordinate system with the coaxial map coordinates system of origin, and under map coordinates system by SLAM calculation Method is patterned and positions.By step（1.1）The odometer information for obtaining is led to as the control input information of SLAM algorithms Cross step（1.2）The radar scanning information for obtaining as SLAM algorithm observation informations, using a kind of based on particle filter SLAM algorithms obtain optimization pose of the intelligent vehicle under map coordinates system, while according to the pose after optimization by k moment intelligent vehicles The obstacle information on periphery carries out building figure.
Step（1.3）In, when the more excellent pose for obtaining intelligent vehicle is optimized using particle filter with map, intelligence The pose at car current time calculates what is obtained according to the pose of previous moment.Know the pose x of k-1 moment intelligent vehiclesk-1, during k The control information at quarter is odometer information uk, the observation information z at k momentk, estimate the x of the current pose of intelligent vehiclekPosteriority it is general Rate；
Posterior probability is obtained using sequential importance sampling algorithm and sampling importance resampling methods in particle filter Distribution, is weighted approximately to obtain Posterior probability distribution by the sampling particle to reference distribution,
Specifically include following steps：
（1.3.1）, initialization system, N number of particle is set, each particle represents the current pose of intelligent vehicle, and sample QUOTE , calculate weight QUOTE ；
（1.3.2）, importance sampling, in moment k, according to the control information u of k moment intelligent vehicleskCalculate k moment intelligent vehicle poses Distribution, sampling QUOTE is carried out to each particle ；
（1.3.3）, according to the observation information z at k momentkWith the weights of particle previous moment, k moment particle importance weights are calculated QUOTE , and it is normalized weights;
（1.3.4）, resampling, increase importance weight particle high, delete the low particle of importance weight, calculate posteriority general Rate.
Step（2）In, original latitude of the intelligent vehicle in WGS-84 geographic coordinate systems is obtained by the GPS location of intelligent vehicle Degree, longitude, height coordinate.
Step（3）Middle Coordinate Conversion RUP is as follows：
（3.1）, original latitude by intelligent vehicle in WGS-84 coordinate systems, longitude use general gauss projection, be transformed into flat Areal coordinate system, and " northeast day " earth coordinates with intelligent vehicle original position car body center as origin are transformed into, and sat with this Mark system is master coordinate system;
（3.2）, the map coordinates system in intelligent vehicle SLAM algorithms is transformed into earth coordinates, specific conversion method is as follows：Intelligence Warp, latitude and the course angle φ of Startup time intelligent vehicle are obtained when energy car starts by GPS device, by step（3.1）Side Method obtains now coordinate (x of the intelligent vehicle in earth coordinates0,y0), then point (the x in map coordinates systemm,ym) in the earth Coordinate (x in coordinate systemp,yp) be：
Step（4）Kalman filter model in, step（1）The intelligent vehicle obtained by SLAM algorithms is under earth coordinates Pose is used as predicted value；Step（2）Pose of the intelligent vehicle obtained by GPS under earth coordinates is used as observation.
The observational equation of definition status space transfer equation and state space is as follows：
,
,
Wherein X (k) is the position and posture vector at k moment, and A (k) is process matrix, and H (k) is calculation matrix, and W (k) makes an uproar for process Sound matrix, its covariance is Q, and V (k) is measurement noise, and its covariance is R.The process of Kalman filtering is as follows：
Predictive equation group：
,
,
Renewal equation group：
,
,
,
Wherein：
X (k | k-1) is to estimate position and posture vector at the k moment；
X (k-1 | k-1) is the optimal position and posture at k-1 moment；
Kg (k) is the kalman gain at k moment；
X (k | k) is that the optimal position and posture at k moment is estimated；
Q is the covariance of systematic procedure noise W (k)；
R is the covariance of systematic survey noise V (k).
The value of state-noise matrix W (k) and measurement noise matrix V (k) is white Gaussian noise, wherein measurement noise matrix V (k) is according to GPS device alignment quality Automatic adjusument.

Claims (7)

1. a kind of intelligent vehicle localization method based on multi-sensor information fusion, it is characterised in that：Comprise the following steps：
（1）, photoelectric encoder based on intelligent vehicle and laser radar collection data, and utilize synchronous superposition side Method（SLAM）Obtain positional information of the intelligent vehicle in the local map that SLAM methods build；
（2）, positional information of the intelligent vehicle in WGS-84 coordinate systems is obtained by the GPS location of intelligent vehicle；
（3）, by step（1）And step（2）The intelligent vehicle for obtaining is in local map coordinate system, the position in WGS-84 coordinate systems Information is unified in earth coordinates by Coordinate Conversion；
（4）, based on step（3）Coordinate transform process, using Kalman Filter Technology by step（1）The position of the intelligent vehicle for obtaining Confidence breath, step（2）The positional information of the intelligent vehicle for obtaining is merged, and obtains intelligent vehicle accurately location estimation.
2. a kind of intelligent vehicle localization method based on multi-sensor information fusion according to claim 1, it is characterised in that： Step（1）Detailed process it is as follows：
（1.1）, with intelligent vehicle start when car body center be origin Oo, it is X along car direction of advanceoDirection of principal axis, vertical XoAxle points to car Body left side is YoDirection of principal axis, ZoAxle sets up odometer coordinate system vertically upward；Due to only consider intelligent vehicle move in the horizontal plane and There is no pitching and tumbling action, therefore Z axis coordinate is always 0 and the only change of yaw angle, therefore intelligent vehicle can be obtained Pose model (x, y, θ)；By the photoelectric encoder of the left and right wheel of intelligent back wheels of vehicle, intelligent vehicle odometer information is calculated, specifically Process is as follows：
（1.1.1）, 0 moment, pose (x of the intelligent vehicle in odometer coordinate system0, y0, θ0) it is (0,0,0)；
（1.1.1）, in the unit sampling time, under odometer coordinate system, the increment Delta S of wheel position can be by below equation Obtain：
Wherein Δ Q is the pulse increment of photoelectric code disk output, and D is wheel diameter, TsIt it is the sampling time, N is the light on photoelectric code disk Grid sum, K is the photoelectric encoder rate of deceleration；
（1.1.2）, the distance between left and right wheels be ω, the increment of left and right wheel position is respectively Δ S in the unit sampling timeLAnd Δ SR, then automobile is from k -1 moment Sk-1 = (xk-1,yk-1,θk-1) arrive k moment Sk = (xk,yk,θk) pose become turn to
（1.1.3）, the odometer information for obtaining moment k intelligent vehicle can be accumulated using the above method；
（1.2）, the observation information that is obtained by mobile lidar and record moment k intelligent vehicle surrounding enviroment；
（1.3）, set up with odometer coordinate system with the coaxial local map coordinate system of origin, it is and logical under local map coordinate system SLAM algorithms are crossed to be patterned and position；By step（1.1）The odometer information for obtaining is defeated as the control of SLAM algorithms Enter information, by step（1.2）The radar scanning information for obtaining is filtered using one kind as SLAM algorithm observation informations based on particle The SLAM algorithms of ripple device obtain optimization pose of the intelligent vehicle under map coordinates system, while according to the pose after optimization by the k moment The obstacle information on intelligent vehicle periphery is added in local map.
3. a kind of intelligent vehicle localization method based on multi-sensor information fusion according to claim 2, it is characterised in that： Step（1.3）In, when the more excellent pose for obtaining intelligent vehicle is optimized using particle filter with map, when intelligent vehicle is current The pose at quarter calculates what is obtained according to the pose of previous moment；Know the pose x of k-1 moment intelligent vehiclesk-1, the control at k moment Information is odometer information uk, the observation information z at k momentk, estimate the x of the current pose of intelligent vehiclekPosterior probability；
Posterior probability is obtained using sequential importance sampling algorithm and sampling importance resampling methods in particle filter Distribution, is weighted approximately to obtain Posterior probability distribution by the sampling particle to reference distribution,
Specifically include following steps：
（1.3.1）, initialization system, N number of particle is set, each particle represents the current pose of intelligent vehicle, and sample QUOTE , calculate weight QUOTE ；
（1.3.2）, importance sampling, in moment k, according to the control information u of k moment intelligent vehicleskCalculate k moment intelligent vehicle poses Distribution, sampling QUOTE is carried out to each particle ；
（1.3.3）, according to the observation information z at k momentkWith the weights of particle previous moment, k moment particle importance weights are calculated QUOTE , and it is normalized weights;
（1.3.4）, resampling, increase importance weight particle high, delete the low particle of importance weight, calculate posteriority general Rate.
4. a kind of intelligent vehicle localization method based on multi-sensor information fusion according to claim 1, it is characterised in that： Step（2）In, original latitude, longitude, height of the intelligent vehicle in WGS-84 geographic coordinate systems are obtained by the GPS location of intelligent vehicle Degree coordinate.
5. a kind of intelligent vehicle localization method based on multi-sensor information fusion according to claim 1 or 4, its feature exists In：Step（3）Middle Coordinate Conversion RUP is as follows：
（3.1）, original latitude by intelligent vehicle in WGS-84 coordinate systems, longitude use general gauss projection, be transformed into flat Areal coordinate system, and " northeast day " earth coordinates with intelligent vehicle original position car body center as origin are transformed into, and sat with this Mark system is master coordinate system;
（3.2）, the map coordinates system in intelligent vehicle SLAM algorithms is transformed into earth coordinates, specific conversion method is as follows：Intelligence Warp, latitude and the course angle φ of Startup time intelligent vehicle are obtained when energy car starts by GPS device, by step（3.1）Side Method obtains now coordinate (x of the intelligent vehicle in earth coordinates0,y0), then point (the x in map coordinates systemm,ym) in the earth Coordinate (x in coordinate systemp,yp) be：
。
6. a kind of intelligent vehicle localization method based on multi-sensor information fusion according to claim 1 or 2 or 5, it is special Levy and be：Step（4）Kalman filter model in, step（1）The intelligent vehicle obtained by SLAM algorithms is in earth coordinates Under pose as predicted value；Step（2）Pose of the intelligent vehicle obtained by GPS under earth coordinates is used as observation；
The observational equation of definition status space transfer equation and state space is as follows：
,
,
Wherein X (k) is the position and posture vector at k moment, and A (k) is process matrix, and H (k) is calculation matrix, and W (k) makes an uproar for process Sound matrix, its covariance is Q, and V (k) is measurement noise, and its covariance is R；The process of Kalman filtering is as follows：
Predictive equation group：
,
,
Renewal equation group：
,
,
,
Wherein：
X (k | k-1) is to estimate position and posture vector at the k moment；
X (k-1 | k-1) is the optimal position and posture at k-1 moment；
Kg (k) is the kalman gain at k moment；
X (k | k) is that the optimal position and posture at k moment is estimated；
Q is the covariance of systematic procedure noise W (k)；
R is the covariance of systematic survey noise V (k).
7. a kind of intelligent vehicle localization method based on multi-sensor information fusion according to claim 6, it is characterised in that： The value of state-noise matrix W (k) and measurement noise matrix V (k) is white Gaussian noise, wherein measurement noise matrix V (k) basis GPS device alignment quality Automatic adjusument.
CN201710131878.2A 2017-03-07 2017-03-07 Intelligent vehicle positioning method based on multi-sensor information fusion Expired - Fee Related CN106840179B (en)

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CN108759833A (en) * 2018-04-25 2018-11-06 中国科学院合肥物质科学研究院 A kind of intelligent vehicle localization method based on priori map
CN108958266A (en) * 2018-08-09 2018-12-07 北京智行者科技有限公司 A kind of map datum acquisition methods
CN108960334A (en) * 2018-07-12 2018-12-07 中国人民解放军陆军炮兵防空兵学院郑州校区 A kind of multi-sensor data Weighted Fusion method
CN109031373A (en) * 2018-06-08 2018-12-18 北京航天光华电子技术有限公司 A kind of Intelligent Mobile Robot navigation system and method
CN109059906A (en) * 2018-06-26 2018-12-21 上海西井信息科技有限公司 Vehicle positioning method, device, electronic equipment, storage medium
CN109061703A (en) * 2018-06-11 2018-12-21 百度在线网络技术（北京）有限公司 Method, apparatus, equipment and computer readable storage medium used for positioning
CN109116397A (en) * 2018-07-25 2019-01-01 吉林大学 A kind of vehicle-mounted multi-phase machine vision positioning method, device, equipment and storage medium
CN109270545A (en) * 2018-10-23 2019-01-25 百度在线网络技术（北京）有限公司 A kind of positioning true value method of calibration, device, equipment and storage medium
CN109358316A (en) * 2018-11-05 2019-02-19 南开大学 Line laser global localization method based on structural unit coding and multiple hypotheis tracking
CN109671120A (en) * 2018-11-08 2019-04-23 南京华捷艾米软件科技有限公司 A kind of monocular SLAM initial method and system based on wheel type encoder
CN109733384A (en) * 2018-12-25 2019-05-10 科大讯飞股份有限公司 Parking path setting method and system
CN109781120A (en) * 2019-01-25 2019-05-21 长安大学 A kind of vehicle combination localization method based on synchronous positioning composition
CN109859613A (en) * 2019-01-18 2019-06-07 驭势（上海）汽车科技有限公司 A kind of high-precision cartography method and mobile unit based on projected coordinate system
CN110160545A (en) * 2018-03-15 2019-08-23 北京航空航天大学 A kind of the enhancing positioning system and method for laser radar and GPS
CN110187375A (en) * 2019-06-27 2019-08-30 武汉中海庭数据技术有限公司 A kind of method and device improving positioning accuracy based on SLAM positioning result
CN110579781A (en) * 2018-06-11 2019-12-17 上海汽车集团股份有限公司 vehicle positioning method and device and electronic equipment
CN110687905A (en) * 2019-09-11 2020-01-14 珠海市众创芯慧科技有限公司 Unmanned intelligent vehicle based on integration of multiple sensing technologies
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CN111801598A (en) * 2017-12-29 2020-10-20 所尼托技术股份公司 Location determination using acoustic context data
CN111994169A (en) * 2020-09-08 2020-11-27 北京北特圣迪科技发展有限公司 Motion control method of independently driven and steered performance trolley based on position compensation
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CN112083726A (en) * 2020-09-04 2020-12-15 湖南大学 Park-oriented automatic driving double-filter fusion positioning system
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CN113255851A (en) * 2021-07-16 2021-08-13 北京科技大学 Data fusion method and device for robot joint optomagnetic hybrid encoder
CN113625236A (en) * 2021-06-30 2021-11-09 嘉兴聚速电子技术有限公司 Multi-radar data fusion method and device, storage medium and equipment
CN113899363A (en) * 2021-09-29 2022-01-07 北京百度网讯科技有限公司 Vehicle positioning method and device and automatic driving vehicle
CN114579679A (en) * 2020-12-01 2022-06-03 中移(成都)信息通信科技有限公司 Spatial positioning data fusion method, system, device and computer storage medium
CN114608553A (en) * 2022-02-21 2022-06-10 南京航空航天大学 Automatic driving automobile positioning system and method
CN114643579A (en) * 2022-03-29 2022-06-21 深圳优地科技有限公司 Robot positioning method and device, robot and storage medium
CN114820768A (en) * 2022-04-15 2022-07-29 中国电子科技集团公司第五十四研究所 Method for aligning geodetic coordinate system and slam coordinate system
US11568649B2 (en) 2021-01-15 2023-01-31 Continental Automotive Systems, Inc. Landmark-less simultaneous localization and mapping
CN116088014A (en) * 2023-03-03 2023-05-09 北京理工大学 Main car information coordinate system conversion method and system based on longitude and latitude information
CN117647254A (en) * 2024-01-30 2024-03-05 智道网联科技(北京)有限公司 Fusion positioning method, device, equipment and storage medium for automatic driving vehicle

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CN107688184A (en) * 2017-07-24 2018-02-13 宗晖（上海）机器人有限公司 A kind of localization method and system
CN107478214A (en) * 2017-07-24 2017-12-15 杨华军 A kind of indoor orientation method and system based on Multi-sensor Fusion
CN107577646A (en) * 2017-08-23 2018-01-12 上海莫斐信息技术有限公司 A kind of high-precision track operation method and system
CN108168560A (en) * 2017-12-27 2018-06-15 沈阳智远弘业机器人有限公司 A kind of complex navigation control method for omnidirectional AGV
CN108168560B (en) * 2017-12-27 2021-06-08 沈阳智远弘业机器人有限公司 Composite navigation control method for omnidirectional AGV
CN111801598A (en) * 2017-12-29 2020-10-20 所尼托技术股份公司 Location determination using acoustic context data
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CN108332750A (en) * 2018-01-05 2018-07-27 深圳市功夫机器人有限公司 Robot localization method and terminal device
CN108196550A (en) * 2018-02-08 2018-06-22 广东工业大学 A kind of automatic guiding carriage and its autocontrol method
CN108446324A (en) * 2018-02-11 2018-08-24 重庆邮电大学 A kind of GPS data reconstructing method based on long memory network LSTM in short-term
CN108398705A (en) * 2018-03-06 2018-08-14 广州小马智行科技有限公司 Ground drawing generating method, device and vehicle positioning method, device
CN110160545A (en) * 2018-03-15 2019-08-23 北京航空航天大学 A kind of the enhancing positioning system and method for laser radar and GPS
CN110160545B (en) * 2018-03-15 2020-05-05 北京航空航天大学 Enhanced positioning system and method for laser radar and GPS
CN108519615B (en) * 2018-04-19 2021-11-26 河南科技学院 Mobile robot autonomous navigation method based on combined navigation and feature point matching
CN108519615A (en) * 2018-04-19 2018-09-11 河南科技学院 Mobile robot autonomous navigation method based on integrated navigation and Feature Points Matching
CN108759833A (en) * 2018-04-25 2018-11-06 中国科学院合肥物质科学研究院 A kind of intelligent vehicle localization method based on priori map
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CN109031373A (en) * 2018-06-08 2018-12-18 北京航天光华电子技术有限公司 A kind of Intelligent Mobile Robot navigation system and method
US10964054B2 (en) 2018-06-11 2021-03-30 Baidu Online Network Technology (Beijing) Co., Ltd. Method and device for positioning
CN109061703A (en) * 2018-06-11 2018-12-21 百度在线网络技术（北京）有限公司 Method, apparatus, equipment and computer readable storage medium used for positioning
CN110579781A (en) * 2018-06-11 2019-12-17 上海汽车集团股份有限公司 vehicle positioning method and device and electronic equipment
CN109059906A (en) * 2018-06-26 2018-12-21 上海西井信息科技有限公司 Vehicle positioning method, device, electronic equipment, storage medium
CN108960334A (en) * 2018-07-12 2018-12-07 中国人民解放军陆军炮兵防空兵学院郑州校区 A kind of multi-sensor data Weighted Fusion method
CN109116397B (en) * 2018-07-25 2022-12-30 吉林大学 Vehicle-mounted multi-camera visual positioning method, device, equipment and storage medium
CN109116397A (en) * 2018-07-25 2019-01-01 吉林大学 A kind of vehicle-mounted multi-phase machine vision positioning method, device, equipment and storage medium
CN108958266A (en) * 2018-08-09 2018-12-07 北京智行者科技有限公司 A kind of map datum acquisition methods
CN109270545A (en) * 2018-10-23 2019-01-25 百度在线网络技术（北京）有限公司 A kind of positioning true value method of calibration, device, equipment and storage medium
CN109358316A (en) * 2018-11-05 2019-02-19 南开大学 Line laser global localization method based on structural unit coding and multiple hypotheis tracking
CN109671120A (en) * 2018-11-08 2019-04-23 南京华捷艾米软件科技有限公司 A kind of monocular SLAM initial method and system based on wheel type encoder
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CN109781120A (en) * 2019-01-25 2019-05-21 长安大学 A kind of vehicle combination localization method based on synchronous positioning composition
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CN110779528A (en) * 2019-11-07 2020-02-11 四川长虹电器股份有限公司 Particle filter-based positioning recovery method and robot equipment
CN111708014A (en) * 2020-05-26 2020-09-25 烟台陆地智能科技有限公司 Method for positioning and measuring non-cooperative target in closed space of millimeter wave radar
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CN112639515A (en) * 2020-07-20 2021-04-09 华为技术有限公司 Signal processing method and device
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CN112083725A (en) * 2020-09-04 2020-12-15 湖南大学 Structure-shared multi-sensor fusion positioning system for automatic driving vehicle
CN112083726A (en) * 2020-09-04 2020-12-15 湖南大学 Park-oriented automatic driving double-filter fusion positioning system
CN111994169A (en) * 2020-09-08 2020-11-27 北京北特圣迪科技发展有限公司 Motion control method of independently driven and steered performance trolley based on position compensation
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CN112781893B (en) * 2021-01-04 2022-09-06 重庆长安汽车股份有限公司 Spatial synchronization method and device for vehicle-mounted sensor performance test data and storage medium
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US11568649B2 (en) 2021-01-15 2023-01-31 Continental Automotive Systems, Inc. Landmark-less simultaneous localization and mapping
CN112904396A (en) * 2021-02-03 2021-06-04 深圳亿嘉和科技研发有限公司 High-precision positioning method and system based on multi-sensor fusion
CN113625236A (en) * 2021-06-30 2021-11-09 嘉兴聚速电子技术有限公司 Multi-radar data fusion method and device, storage medium and equipment
CN113255851A (en) * 2021-07-16 2021-08-13 北京科技大学 Data fusion method and device for robot joint optomagnetic hybrid encoder
US11953609B2 (en) 2021-09-29 2024-04-09 Beijing Baidu Netcom Science Technology Co., Ltd. Vehicle positioning method, apparatus and autonomous driving vehicle
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CN114608553A (en) * 2022-02-21 2022-06-10 南京航空航天大学 Automatic driving automobile positioning system and method
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CN114643579B (en) * 2022-03-29 2024-01-16 深圳优地科技有限公司 Robot positioning method and device, robot and storage medium
CN114820768A (en) * 2022-04-15 2022-07-29 中国电子科技集团公司第五十四研究所 Method for aligning geodetic coordinate system and slam coordinate system
CN116088014B (en) * 2023-03-03 2023-07-04 北京理工大学 Main car information coordinate system conversion method and system based on longitude and latitude information
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