CN101915852B - Velocity measurement method based on stereoscopic vision - Google Patents
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Abstract
The invention discloses a velocity measurement method based on stereoscopic vision, which comprises the following steps of: S1. acquiring the image of a static marker ahead when a carrier runs and processing the image in space dimension and time dimension according to the correlation of the image in space and time to obtain the measured value of running velocity of the carrier; and S2. measuring change rate omega of gradient Phi of the orbit of the carrier and acceleration speed ax of the carrier in running direction, establishing a process model and a measurement model which are used for estimating running velocity on the basis of the measured value of running velocity, the change rate omega and the acceleration speed ax, and estimating the running velocity of the carrier as the final result by Kalman filter algorithm according to the process model and the measurement model. The method can eliminate influence of wheel idling and slipping on the velocity measurement accuracy and realize low noise and real-time velocity measurement.
Description
Technical field
The invention belongs to the track traffic technical field, particularly a kind of speed measurement method based on stereoscopic vision.
Background technology
Speed is a significant data of train driving and security control, the running quality of its accuracy and reliability decision train.At present, the travelling speed of train mainly is through directly or indirectly wheel count being measured, then according to wheel circumference and in the unit interval revolution indicator of wheel calculate the speed of train.The influence of sliding relatively between footpath abrasion and wheel track is taken turns in the accuracy of this method.Though more existing compound speed-measuring methods have merged the data of some sensors, the measurement data that is based on wheel count still is the main foundation that it carries out velocity estimation, so the influence of sliding relatively between footpath abrasion and wheel track is still taken turns in its accuracy.
Therefore, though, easily realization simple based on the speed-measuring method principle of wheel count, its measuring accuracy receives wheel footpath variation, wheel spin, the influence of skidding easily.And opportunity of dallying and skidding and the error that causes thus are difficult to describe with the method for mathematics, thereby its influence also can't be eliminated through the mode of complex mathematical processing and fused filtering.
Summary of the invention
The technical matters that (one) will solve
How the technical matters first that the present invention will solve is eliminated wheel spin, is skidded to the influence of velocity survey precision; It two is how to realize low noise, real-time velocity survey.
(2) technical scheme
For solving the problems of the technologies described above, the invention provides a kind of speed measurement method based on stereoscopic vision, may further comprise the steps:
S1; Enforcement is based on the speed calculation method of video: the image of the static marker in the place ahead when gathering the carrier operation; According to the correlativity of image information on room and time; Through image being handled, obtain the measured value
of the travelling speed of carrier at Spatial Dimension and time dimension
S2 implements blending algorithm: measure the rate of change ω of the gradient φ of carrier orbit, and measure the acceleration a of carrier traffic direction
x, utilize the measured value of travelling speed
Rate of change ω and acceleration a
xProcess model and the measurement model that the carrier travelling speed is estimated carried out in foundation, utilizes travelling speed that Kalman filtering algorithm estimates carrier as net result according to this process model and measurement model then.
Wherein, in step S1, utilize the image of two camera collection markers; These two parallel placements of camera constitute the binocular tri-dimensional vision system, and two cameras are installed in headstock; Look setting height(from bottom), adjustment makes its horizontal relatively angle spend between 85 degree 5.
Wherein, in step S2, utilize the rate of change ω of the gradient φ of gyroscope survey carrier orbit, utilize the acceleration a of accelerometer measures carrier traffic direction
x
Wherein, step S1 further may further comprise the steps:
S11 is at moment t
1The static marker in the place ahead carried out video sampling when carrier was moved, and obtained two cameras at the image of synchronization to the like-identified thing;
S12 selects unique point P therein in the piece image
i, said unique point is meant the point in the pixel region that the border obviously comparatively speaking, contrast is clear and be easy to follow the tracks of and discern;
S13 utilizes the unique point P that selects among the step S12
i, seek the unique point P ' that is complementary at another width of cloth image
i, form the coupling pair set { P of same unique point in two width of cloth images
i, i=1,2 ..., N} with P '
i, i=1,2 ..., N};
S14 is according to the location gap b and the resulting coupling pair set of step S13 of two cameras, calculated characteristics point P
iPosition x in the camera coordinate system
i, and recording feature point P
iAnd coordinate position { P
i, x
i;
S15 is at moment t
2Carry out video sampling;
S16 utilizes the set of the unique point that obtains among the step S12 equally, at same camera at moment t
2Institute takes the photograph and carries out matched and searched in the image, the unique point set that obtains mating
S17, the result who utilizes step S16 gained is at t
2Another camera is taken the photograph and is mated in the image constantly, obtains the set of corresponding matched point
Method among the S14 calculates the unique point P that step S12 confirms set by step
iAt moment t
2Position { P under the camera coordinate system
I,x
i';
S18; According to the result of step S14 and S17 gained and the time interval of adjacent twice video sampling, calculate the speed
of unique point under the camera coordinate system
S19 carries out coordinate transform according to the geometric position that two cameras are installed, and obtains the speed of unique point under carrier coordinate system, and the speed that calculates is the travelling speed of carrier;
S10; Calculate the speed of all unique points of synchronization; Get the travelling speed v of its mean value, get travelling speed v then at the component
of carrier traffic direction measured value as the carrier travelling speed as this moment carrier.
Wherein, step S2 further may further comprise the steps:
S21 utilizes single axis gyroscope to measure the rate of change ω of the gradient φ of track, and utilizes single-axis accelerometer to measure the acceleration a of carrier traffic direction
x, the measurement model of single axis gyroscope and single-axis accelerometer is described with following formula respectively:
Wherein, b is the measured deviation of single axis gyroscope, and υ is the measurement noise of single axis gyroscope, and g is an acceleration of gravity; η is the measurement noise of single-axis accelerometer, and (0, Q), η～N (0 to adopt two kinds of white Gaussian noise model representations to measure noise: υ～N; R), E [υ ' υ]=Q wherein, E [η ' η]=R; E [*] representes expectation value, and it obtains according to number of actual measurements according to statistics, subscript " ' " computing of expression transposition;
S22, the measured value that utilizes step S1 to obtain is expressed as bearer rate
Wherein ε is a measuring error, and ε～N (0, H), E [ε ' ε]=H wherein, H obtains according to number of actual measurements according to statistics, v
xActual value for bearer rate;
S23 turns to formula (1) linearity:
Formula (3) and formula (4) are respectively to utilize Kalman filtering algorithm to carry out the process model and the measurement model of velocity estimation, utilize Kalman filtering algorithm to estimate the travelling speed of carrier according to this process model and measurement model.
Wherein, it is following to utilize Kalman filtering algorithm to estimate the process of travelling speed of carrier according to this process model and measurement model:
(1) utilizes selected SF f
sWith formula (3), (4) discretize, obtain discretization model, wherein SF f respectively
sGreater than 10Hz;
The noise variance of the single axis gyroscope that discretization model that (2) obtains in the basis (1) and statistics obtain and the measurement noise of single-axis accelerometer utilizes the stable state Kalman filtering algorithm of standard to calculate Kalman filter;
(3) at each sampling instant execution in step a: read the measured value that individual axis acceleration is taken into account single axis gyroscope; Be input to Kalman filter to the measured value of same sampling instant step S1 and step a then; Through Filtering Processing, the output that obtains is as said net result.
Wherein, utilize the Kalman in Matlab software control tool box to order the stable state Kalman filtering algorithm of realizing said standard.
Wherein, said SF f
sGreater than 10Hz.
Wherein, said SF f
sPreferred 30Hz.
Wherein, said carrier is preferably train.
(3) beneficial effect
The present invention can produce following beneficial effect: utilize dual camera to constitute the stereoscopic vision configuration, be installed on the carrier front portion, realize the reckoning to speed through taking carrier the place ahead static scene of advancing; Utilize the space correlation of image that unique point is positioned based on the speed estimation algorithms of video, utilize property time correlation of unique point to follow the tracks of.Through processing, calculate the speed of carrier to the adjacent continuous frame.Above measuring method does not rely on the counting to wheel shaft, thereby has eliminated wheel spin, skid to the influence of measuring accuracy.Further, utilize the blending algorithm of Kalman (Kalman) filtering to realize the fusion of bearer rate measured value and accelerometer, gyroscope survey value, realized low noise, real-time velocity survey.In addition, measurement mechanism is installed simple, and is easy to maintenance.
Description of drawings
Fig. 1 is the method flow diagram of the embodiment of the invention;
Fig. 2 is that the employed system of the method for the embodiment of the invention constitutes block diagram;
Camera installation site and angle synoptic diagram when Fig. 3 is the method for embodiment of the present invention embodiment;
Fig. 4 is based on the speed calculation method principle schematic of video in the method for the embodiment of the invention.
Embodiment
Below in conjunction with accompanying drawing and embodiment, specific embodiments of the invention describes in further detail.Following examples are used to explain the present invention, but are not used for limiting scope of the present invention.
The embodiment of the invention is that example describes with the train.The method flow diagram of the embodiment of the invention is as shown in Figure 1.As shown in Figure 2; The system that the following several means of method utilization of the present invention constitutes realizes: 2 CCD (Charge-coupled Device; Charge coupled cell) (or CMOS (Complementary metal-oxide-semiconductor, complementary matal-oxide semiconductor)) camera 1,2, single-axis accelerometer, single axis gyroscope.Wherein, single-axis accelerometer is measured the acceleration of current of traffic, and single axis gyroscope is used for measuring the rate of change of the gradient of train operation track.Two camera configured in parallel constitute the binocular tri-dimensional vision system, accomplish the captured in real time to vehicle operating the place ahead what comes into a driver's.System also comprises two DSP (Digital Signal Processor, digital signal processor), and first Video processing DSP is responsible for video algorithms and handles, and another is speed calculation DSP, is used for the travelling speed Measurement Algorithm.
As shown in Figure 3, two cameras are installed in headstock, look setting height(from bottom), and adjustment makes its horizontal relatively angle spend between 85 degree 5, guarantees that like this image that camera is caught does not comprise the image of car itself.
The method of the embodiment of the invention comprises two key steps, the first, according to the image information of camera collection, obtain the measured value of speed based on the speed calculation method of video; The second, carry out data fusion in conjunction with accelerometer and gyroscope, utilize the Kalman filtering algorithm to estimate train speed.
Speed estimation algorithms based on video is according to the correlativity of image information on room and time, through at Spatial Dimension and time dimension image being carried out related operation, extrapolates the travelling speed of train.The principle explanation is as shown in Figure 3.
Among Fig. 4, A, B, C, D are that same object or marker are respectively at different cameras, different reflection constantly.A and B are illustrated respectively in the reflection of 1 o'clock same marker in two cameras constantly; C and D represent respectively that then this marker is at the moment 2 o'clock reflections in two cameras.According to the space correlation of synchronization, can calculate the position of marker under the camera coordinate system, and, then can calculate average movement velocity at the moment 1 and the moment 2 carrier during this period of time according to temporal association.When the frequency acquisition (being generally 30Hz) by video carries out continuous calculation process, just can obtain the instantaneous velocity of carrier.The treatment step of algorithm is following:
1, at the moment 1 (t
1) carry out video sampling, obtain camera 1 and camera 2 at the snapshot of synchronization to same scene.
2, (take the photograph image) in the piece image therein and seek and select unique point P like 1 of camera
i, unique point is that those borders are obvious, contrast is clear, the point in the pixel region that is easy to follow the tracks of and discern.
3, utilize the unique point P that selects in the step 2
i, seek the unique point P ' that is complementary at another width of cloth image
i, form the coupling pair set { P of same unique point in two width of cloth images
i, i=1,2 ..., N} with P '
i, i=1,2 ..., N}.
4, according to the location gap b and the formed coupling set of step 3 of two cameras, the position x of calculated characteristics point in the camera coordinate system
iPosition x
iCalculating adopt document [1] (Milan Sonka; Vaclav Hlavac, Roger Boyle:Image Processing, Analysis and Machine Vision; Second Edition; ISBN 0-534-95393-X, Brooks/Cole, the stereoscopic vision algorithm (Stereo vision algorithm) in pp.460.) carries out.Recording feature point P
iAnd coordinate position { P
i, x
i.
5, at the moment 2 (t
2) carry out video sampling.
6; Utilize the unique point set that obtains in the step 2 equally; This camera constantly 2 take the photograph and carry out matched and searched in the image, the unique point that obtains mating set
7, utilize in 6 the result at t
2Another camera is taken the photograph and is mated in the image constantly, obtains the set of corresponding matched point
Calculate the unique point P that step 2 is confirmed with quadrat method in 4 set by step
iAt the moment 2 o'clock position { P under the camera coordinate system
i, x
i'.
8, according to the time interval between step 4 and 7 gained results and twice adjacent video sampling, can calculate unique point P
iSpeed under the camera coordinate system
9, carry out coordinate transform according to the geometric relationship that camera is installed, can obtain unique point P
iSpeed under the train coordinate system.Because camera institute visual field scape is a static scene, the velocity magnitude that then calculates is the travelling speed of train.
10, calculate the speed of all unique points, get the speed of its mean value as this moment train:
Because row orbit, we pay close attention to its speed of advancing.So get the measured value of the x durection component
(the train coordinate system is referring to Fig. 3) of v as train speed.
11, return step 1 and continue operation.
According to top step, can obtain the train speed that utilizes video image to calculate.Because Video processing is influenced by camera sampling rate and DSP processing power, can there be time-delay in the speed that calculates.In order to improve time-delay, reduce noise, speed that is drawn by Video processing and acceleration are taken into account gyroscope and are utilized the Kalman filtering algorithm further to merge, thereby reach instant, low noise, velocity estimation accurately.Process is following:
The rate of change ω of gyroscope survey track cross level gradient φ.The acceleration a of accelerometer measures current of traffic
xConsider noise and measured deviation, the measurement model of gyroscope and accelerometer can be described by following formula respectively.
Wherein, in the symbol on the equality left side, the variable top adds " " expression to this variable differential, and b is the gyroscope survey deviation, and g is an acceleration of gravity, and υ and η are respectively the measurement noise of two sensors, and we adopt the white Gaussian noise model,
υ～N(0，Q)，η～N(0，R)
E [υ ' υ]=Q wherein, E [η ' η]=R obtains according to number of actual measurements according to statistics.
Utilization is expressed as train speed based on the measured value that the speed calculation method of video calculates
Wherein ε is a measuring error, and ε～N (0, H), E [ε ' ε]=H, v
xBe the train speed actual value.
Because track grade is no more than 10% (5.7 °) usually, formula (1) can linearization.
Formula (3) and formula (4) are based on process model and the measurement model that visual velocity is estimated respectively, can utilize the steady state Kalman filtering algorithm according to this model, estimate the travelling speed of train.Detailed process is following:
(1) selected calculating sampling frequency f
s,, obtain the discretize mathematical model with modular form (3), (4) discretize.For guaranteeing measuring accuracy, f
sShould select 30Hz usually greater than 10Hz.
(2) noise variance that utilizes the discretization model that obtains in (1) and statistics to obtain, utilize that the steady state Kalman filtering algorithm (as utilizing the kalman order in Matlab software control tool box) of standard calculates to the Kalman wave filter.
(3), read acceleration transducer and gyroscope survey data at each sampling instant: b; C, measured value that calculates the speed calculation method based on video and the result of b are input to the kalman wave filter, and through Filtering Processing, the output that obtains is as the train speed measurement result.
Method of the present invention not only can be used for locomotive velocity measuring and also can be used for other carrier is tested the speed, and just because the railway operation environment is more single, therefore relatively is fit to use visible sensation method.
Above embodiment only is used to explain the present invention; And be not limitation of the present invention; The those of ordinary skill in relevant technologies field under the situation that does not break away from the spirit and scope of the present invention, can also be made various variations and modification; Therefore all technical schemes that are equal to also belong to category of the present invention, and scope of patent protection of the present invention should be defined by the claims.
Claims (9)
1. the speed measurement method based on stereoscopic vision is characterized in that, may further comprise the steps:
S1; Enforcement is based on the speed calculation method of video: the image of the static marker in the place ahead when gathering the carrier operation; According to the correlativity of image information on room and time; Through image being handled, obtain the measured value
of the travelling speed of carrier at Spatial Dimension and time dimension
S2 implements blending algorithm: measure the rate of change ω of the gradient φ of carrier orbit, and measure the acceleration a of carrier traffic direction
x, utilize the measured value of travelling speed
Rate of change ω and acceleration a
xSet up process model and measurement model that the carrier travelling speed is estimated, utilize travelling speed that Kalman filtering algorithm estimates carrier as net result according to this process model and measurement model then.
2. the speed measurement method based on stereoscopic vision as claimed in claim 1 is characterized in that, in step S1; Utilize the image of two camera collection markers; These two parallel placements of camera constitute the binocular tri-dimensional vision system, and two cameras are installed in headstock; Look setting height(from bottom), adjustment makes its horizontal relatively angle spend between 85 degree 5.
3. according to claim 1 or claim 2 the speed measurement method based on stereoscopic vision is characterized in that, in step S2, utilizes the rate of change ω of the gradient φ of gyroscope survey carrier orbit, utilizes the acceleration a of accelerometer measures carrier traffic direction
x
4. the speed measurement method based on stereoscopic vision as claimed in claim 3 is characterized in that step S1 further may further comprise the steps:
S11 is at moment t
1The static marker in the place ahead carried out video sampling when carrier was moved, and obtained two cameras at the image of synchronization to the like-identified thing;
S12 selects unique point P therein in the piece image
i, said unique point is meant the point in the pixel region that the border obviously comparatively speaking, contrast is clear and be easy to follow the tracks of and discern;
S13 utilizes the unique point P that selects among the step S12
i, seek the unique point P ' that is complementary at another width of cloth image
i, form the coupling pair set { P of same unique point in two width of cloth images
i, i=1,2 ..., N} with P '
i, i=1,2 ..., N};
S14 is according to the location gap b and the resulting coupling pair set of step S13 of two cameras, calculated characteristics point P
iPosition x in the camera coordinate system
i, and recording feature point P
iAnd coordinate position { P
i, x
i;
S15 is at moment t
2Carry out video sampling;
S16 utilizes the set of the unique point that obtains among the step S12 equally, at same camera at moment t
2Institute takes the photograph and carries out matched and searched in the image, the unique point set that obtains mating
S17, the result who utilizes step S16 gained is at t
2Another camera is taken the photograph and is mated in the image constantly, obtains the set of corresponding matched point
Method among the S14 calculates the unique point P that step S12 confirms set by step
iAt moment t
2Position { P under the camera coordinate system
i, x
i';
S18; According to the result of step S14 and S17 gained and the time interval of adjacent twice video sampling, calculate the speed
of unique point under the camera coordinate system
S19 carries out coordinate transform according to the geometric position that two cameras are installed, and obtains the speed of unique point under carrier coordinate system, and the speed that calculates is the travelling speed of carrier;
S10; Calculate the speed of all unique points of synchronization; Get the travelling speed v of its mean value, get travelling speed v then at the component
of carrier traffic direction measured value as the carrier travelling speed as this moment carrier.
5. the speed measurement method based on stereoscopic vision as claimed in claim 4 is characterized in that step S2 further may further comprise the steps:
S21 utilizes single axis gyroscope to measure the rate of change ω of the gradient φ of track, and utilizes single-axis accelerometer to measure the acceleration a of carrier traffic direction
x, the measurement model of single axis gyroscope and single-axis accelerometer is described with following formula respectively:
Wherein, b is the measured deviation of single axis gyroscope, and υ is the measurement noise of single axis gyroscope, and g is an acceleration of gravity; η is the measurement noise of single-axis accelerometer, and (0, Q), η～N (0 to adopt two kinds of white Gaussian noise model representations to measure noise: υ～N; R), E [υ ' υ]=Q wherein, E [η ' η]=R; E [*] representes expectation value, and it obtains according to number of actual measurements according to statistics, subscript " ' " computing of expression transposition;
S22, the measured value that utilizes step S1 to obtain is expressed as bearer rate
Wherein ε is a measuring error, and ε～N (0, H), E [ε ' ε]=H wherein, H obtains according to number of actual measurements according to statistics, v
xActual value for bearer rate;
S23 turns to formula (1) linearity:
Formula (3) and formula (4) are respectively to utilize Kalman filtering algorithm to carry out the process model and the measurement model of velocity estimation, utilize Kalman filtering algorithm to estimate the travelling speed of carrier according to this process model and measurement model.
6. the speed measurement method based on stereoscopic vision as claimed in claim 5 is characterized in that, it is following to utilize Kalman filtering algorithm to estimate the process of travelling speed of carrier according to this process model and measurement model:
(1) utilizes selected SF f
sWith formula (3), (4) discretize, obtain discretization model, wherein SF f respectively
sGreater than 10Hz;
The noise variance of the single axis gyroscope that discretization model that (2) obtains in the basis (1) and statistics obtain and the measurement noise of single-axis accelerometer utilizes the stable state Kalman filtering algorithm of standard to calculate Kalman filter;
(3) at each sampling instant execution in step a: read the measured value that individual axis acceleration is taken into account single axis gyroscope; Be input to Kalman filter to the measured value of same sampling instant step S1 and step a then; Through Filtering Processing, the output that obtains is as said net result.
7. the speed measurement method based on stereoscopic vision as claimed in claim 6 is characterized in that, utilizes the Kalman in Matlab software control tool box to order the stable state Kalman filtering algorithm of realizing said standard.
8. the speed measurement method based on stereoscopic vision as claimed in claim 6 is characterized in that, said SF f
sGet 30Hz.
9. according to claim 1 or claim 2 the speed measurement method based on stereoscopic vision is characterized in that said carrier is a train.
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| US9454508B2 (en) * | 2012-08-31 | 2016-09-27 | Ford Global Technologies, Llc | Kinematic road gradient estimation |
| US10042815B2 (en) | 2012-08-31 | 2018-08-07 | Ford Global Technologies, Llc | Road gradient estimation arbitration |
| CN104133076A (en) * | 2014-07-30 | 2014-11-05 | 宇龙计算机通信科技(深圳)有限公司 | Speed measurement device and method and terminal |
| CN105352495B (en) * | 2015-11-17 | 2018-03-23 | 天津大学 | Acceleration and light stream Data Fusion of Sensor unmanned plane horizontal velocity control method |
| WO2017177367A1 (en) * | 2016-04-11 | 2017-10-19 | Xiaogang Wang | Method and system for object tracking |
| CN107462242B (en) * | 2016-06-06 | 2020-09-29 | 千寻位置网络有限公司 | Vehicle speed measuring method and device |
| CN106932605B (en) * | 2017-04-27 | 2020-04-14 | 中国科学院长春光学精密机械与物理研究所 | Bullet warhead moving speed measuring system |
| CN107462912A (en) * | 2017-07-12 | 2017-12-12 | 南通赛洋电子有限公司 | A kind of filtering method of the speed of a ship or plane course-stability output of GNSS peculiar to vessel |
| DE102017212955A1 (en) * | 2017-07-27 | 2019-01-31 | Siemens Aktiengesellschaft | Determining the speed of a rail vehicle using an on-board camera |
| CN109664922B (en) * | 2018-12-29 | 2020-12-18 | 交控科技股份有限公司 | Fusion speed measurement processing method and device based on accelerometer |
| CN110047070B (en) * | 2019-04-22 | 2021-01-08 | 山东师范大学 | Method and system for identifying rail wear degree |
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| CN101915852A (en) | 2010-12-15 |
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