CN109579824A - A kind of adaptive Kano Meng Te localization method incorporating two-dimensional barcode information - Google Patents
A kind of adaptive Kano Meng Te localization method incorporating two-dimensional barcode information Download PDFInfo
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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
- G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
A kind of adaptive Kano Meng Te localization method for incorporating two-dimensional barcode information is claimed in the present invention, and the method comprising the steps of: S1, the absolute location information and odometer control amount u provided by two dimensional codetEstablish motion model；S2 carries out particle sampler according to the motion model of foundation, can estimate the initial pose of robot；S3 establishes observation model using two-dimensional laser sensor instrument distance；S4 determines the weights of importance of each particle and with new weight；S5, population needed for adaptively adjusting next iteration in the distribution situation of state space according to particle；S6 determines the position of robot in the environment according to the distribution situation of particle.The present invention can in the environment be accurately positioned robot, reduce calculation amount.
Description
Technical field
The invention belongs to Mobile Robotics Navigation field, especially a kind of Monte Carlo localization side for incorporating two-dimensional barcode information
Method.
Background technique
Under the premise of known to the environmental map, mobile robot determines it in the environment according to environment sensing and displacement
Pose problem be known as orientation problem.The Kano Meng Te positions (Monte Carlo localization, MCL) algorithm to move mould
Type sampling, and observation model is combined to assess the weights of importance of each particle, obtain the posteriority reliability distribution of system mode.Success
Applied to mobile robot field, it is suitable for two class problem of local positioning and Global localization.Odometer motion model passes through integration
Photoelectric encoder information on wheel, and then relative mistake of the robot relative to upper sampling instant pose is obtained, fixed
Time interval can carry out pose estimation.But due to the influence for the factors such as drift about or skid, lead to the precision of motion model at any time
Between increase and decline, so as to cause the Kano Meng Te location algorithm position error increase；In addition, particle can generate after resampling
Degradation effect, particle diversity reduce, and fixed large sample particle will lead to computing resource waste, therefore scholars are studying always
How to solve the problems, such as these two types of.Odometer error is just divided into systematic error in the nineties by Borenstein et al. and nonsystematic misses
Poor two parts, and propose a kind of mileage meter calibration method " UMBmark " and eliminate systematic error odometer precision is influenced, machine
Device people can calibrate differential gear model parameter by desired trajectory movement several times.Yap et al. is built using EM algorithm and combining environmental
Figure carrys out while calculating the parameter of odometer motion model and laser observation model, finally realizes online adaptive calibration.
Alhashimi et al. improves the observation model of Monte carlo algorithm, and the big of particle sample set is determined by the threshold value of setting
It is small, effectively reduce calculation amount.Huang Lu et al. design artificial landmark simultaneously establishes road sign library to correct the cumulative errors of odometer.
But need mass data to cause positioning accuracy low since artificial landmark designs complicated and road sign library and establishes, it is unable to satisfy accuracy
And it is computationally intensive.
Therefore, a kind of adaptive Kano Meng Te location algorithm incorporating two-dimensional barcode information, incorporates two dimension in sampling process
The absolute location information that code carries, the class for improving odometer add up to error；Laser sensor information establishes observation model based on
It calculates and updates particle weights；And using Kullback-Leibler distance (Kullback-Leibler Distance, KLD) weight
Sampling, the statistics boundary according to sampling in the APPROXIMATE DISTRIBUTION of state space determine population come online, avoid big calculation amount.
Summary of the invention
Present invention seek to address that the above problem of the prior art.Propose a kind of amendment odometer cumulative errors, adaptive
Adjust the adaptive Kano the Meng Te localization method of particle assembly size, the involvement two-dimensional barcode information for reducing calculation amount.Of the invention
Technical solution is as follows:
A kind of adaptive Kano Meng Te localization method incorporating two-dimensional barcode information comprising following steps:
S1, the absolute location information provided according to two dimensional code and odometer control amount utMovement mould after establishing amendment error
Type；
S2, according to the motion model and the sampling set χ at t-1 moment after amendment errort-1Particle sampler is carried out, estimates machine
The initial pose of device people；
S3 establishes observation model using two-dimensional laser sensor instrument distance；
S4 calculates the weights of importance of each particle according to the likelihood-domain of given map m and observation model and with new weight；
S5, the distribution situation according to particle in state space, Kullback-Leibler distance (Kullback-Leibler
Distance, KLD) resampling adaptively adjusts population needed for next iteration；
S6 determines the position of robot in the environment according to the distribution situation of particle.
Further, the motion model that step S1 is established after correcting error specifically includes:
Time interval (t-1, t] in, give motion information utAre as follows:
WhereinRespectively indicate the pose at t and t-1 moment under odometer coordinate system, utIt is transformed into three steps
Sequence: initial rotation δrot1, translation δtransWith second of rotation δrot2.Establish the model of kinematic error:
ε indicate mean value be 0, variance b2Noise variance.Parameter alpha1-α4It is the error parameter for robot, they refer to
Surely the cumulative errors moved, therefore physical location xtFrom xt-1By initial rotation angleFollow translation distanceFollowed by
Another rotation angleIt obtains, so that
Then physical location xt=(x ', y ', θ ').
Further, the step S2 particle sampler is estimated the initial pose of robot, is specifically included:
Motion model is sampled initial attitude xt-1, odometer read utX is read with cameracAs input, robot is being transported
When arriving two dimensional code without scanning during dynamic, pose isWhen camera is got
Two-dimensional barcode information, the pose x with the sampling output of odometer at this timetIt is compared, the two error is greater than critical value τ, then samples calculation
The value of method output is the absolute value x of two dimensional code coordinatec, and enable the pose x at current timetFor the pose that two-dimensional barcode information provides, after
It is continuous to be sampled until scanning next two dimensional code.
Further, the step S3 establishes observation model using two-dimensional laser sensor instrument distance, specifically includes following step
It is rapid:
Conditional probability distribution p (zt|xt, m) be observation model, a possibility that each single observation be multiplied can be obtained it is general
Rate is as follows:
Wherein, xtIt is the pose of robot, ztIt is the observation of t moment, zt kIndicate k-th of distance measurement value of t moment.M is ring
Condition figure, it is assumed that independent between each observation beam noise.
Further, the step S4 calculates the important of each particle according to the likelihood-domain of given map m and observation model
Property weight and with new weight, specifically includes:
It is obtained in x-y space at a distance from nearest barrier with likelihood-domain calculating observation probability using map m as condition
Dist:
Since the noise of sensor different beams is independent from each other, to kValue be multiplied, by by one
A be just distributed very much is uniformly distributed the likelihood result q for being mixed to get observation model with one:
Give three parameter zhit、zrandAnd zmaxIt is weighted and averaged mixing, and zhit+zrand+zmax=1.
Further, in the step S5, Kullback-Leibler distance (Kullback-Leibler Distance,
KLD) the calculation method of sub- resampling:
KLD sampling all determines sample number to each particle filter iteration with probability 1- δ, so that true Posterior distrbutionp and base
Error between the APPROXIMATE DISTRIBUTION of sampling is less than ε, thereby determines that the size of resampling sample set, when population n meets one
When definite value, it is ensured that the K-L distance between the true value and estimated value of probability is less than threshold epsilon, at this time the value of n are as follows:
Wherein z1-δIt is the standardized normal distribution of upper quantile 1- δ, h indicates the histogram for being at least filled with a particle
Digit is meeting nxBefore counting boundary, KLD sampling will generate always particle.
It advantages of the present invention and has the beneficial effect that:
The present invention provides a kind of adaptive Kano Meng Te localization methods for incorporating two-dimensional barcode information.It is provided using two dimensional code
Absolute location information amendment odometer model cumulative errors after sampled；Effectively amendment cumulative errors improve positioning accurate
Degree；Resampling part using Kullback-Leibler distance (KLD) resampling, according to particle state space distribution situation
Population needed for adaptive adjustment next iteration, reduces calculation amount.
Detailed description of the invention
Fig. 1 is the adaptive Kano the Meng Te localization method process that the present invention provides that preferred embodiment incorporates two-dimensional barcode information
Figure.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, detailed
Carefully describe.Described embodiment is only a part of the embodiments of the present invention.
The technical solution that the present invention solves above-mentioned technical problem is:
As shown in Figure 1, the present invention provides a kind of adaptive Kano Meng Te localization method for incorporating two-dimensional barcode information, packet
Include following steps:
S1, by the sampling set χ at momentt-1, each particle corresponds to robot in the estimated motion track of this point, when t-1
Carve the control amount u appliedtThe world coordinates information x provided with two dimensional codecThe absolute location information provided as input, two dimensional code
Odometer motion model can be corrected；
Time interval (t-1, t] in, give motion information utAre as follows:
WhereinRespectively indicate the pose at t and t-1 moment under odometer coordinate system, utIt is transformed into three steps
Sequence: initial rotation δrot1, translation δtransWith second of rotation δrot2.Establish the model of kinematic error:
ε indicate mean value be 0, variance b2Noise variance.Parameter alpha1-α4It is the error parameter for robot, they refer to
Surely the cumulative errors moved, therefore physical location xtFrom xt-1By initial rotation angleFollow translation distanceFollowed by
Another rotation angleIt obtains, so that
Then physical location xt=(x ', y ', θ ')
S2 carries out the initial pose of particle sampler estimation robot according to revised motion model；
Motion model is sampled initial attitude xt-1, odometer read utX is read with cameracAs input, robot is being transported
When arriving two dimensional code without scanning during dynamic, pose isWhen camera is got
Two-dimensional barcode information, the pose x with the sampling output of odometer at this timetIt is compared, the two error is greater than critical value τ, then samples calculation
The value of method output is the absolute value x of two dimensional code coordinatec, and enable the pose x at current timetFor the pose that two-dimensional barcode information provides, after
It is continuous to be sampled until scanning next two dimensional code.
S3 establishes observation model using two-dimensional laser sensor instrument distance；With likelihood-domain calculating observation probability,
Conditional probability distribution p (zt|xt, m) be observation model, a possibility that each single observation be multiplied can be obtained it is general
Rate is as follows:
Wherein, xtIt is the pose of robot, ztIt is the observation of t moment, zt kIndicate k-th of distance measurement value of t moment.M is ring
Condition figure, it is assumed that independent between each observation beam noise.
S4 calculates the weights of importance of each particle according to given map m and observation model likelihood-domain and with new weight；
It is obtained in x-y space at a distance from nearest barrier with likelihood-domain calculating observation probability using map m as condition
Dist:
Since the noise of sensor different beams is independent from each other, to kValue be multiplied, by by one
A be just distributed very much is uniformly distributed the likelihood result q for being mixed to get observation model with one:
Give three parameter zhit、zrandAnd zmaxIt is weighted and averaged mixing, and zhit+zrand+zmax=1.
S5, population needed for adaptively adjusting next iteration in the distribution situation of state space according to particle；
KLD sampling all determines sample number to each particle filter iteration with probability 1- δ, so that true Posterior distrbutionp and base
Error between the APPROXIMATE DISTRIBUTION of sampling is less than ε, thereby determines that the size of resampling sample set, when population n meets one
When definite value, it is ensured that the K-L distance between the true value and estimated value of probability is less than threshold epsilon, at this time the value of n are as follows:
Wherein z1-δIt is the standardized normal distribution of upper quantile 1- δ, h indicates the histogram for being at least filled with a particle
Digit is meeting nxBefore counting boundary, KLD sampling will generate always particle.
S6 determines the position of robot in the environment according to the distribution situation of particle.The present invention can carry out robot
It is accurately positioned.
The above embodiment is interpreted as being merely to illustrate the present invention rather than limit the scope of the invention.?
After the content for having read record of the invention, technical staff can be made various changes or modifications the present invention, these equivalent changes
Change and modification equally falls into the scope of the claims in the present invention.
Claims (6)
1. a kind of adaptive Kano Meng Te localization method for incorporating two-dimensional barcode information, which comprises the following steps:
S1, the absolute location information provided according to two dimensional code and odometer control amount utMotion model after establishing amendment error；
S2, according to the motion model and the sampling set χ at t-1 moment after amendment errort-1Particle sampler is carried out, estimates robot
Initial pose；
S3 establishes observation model using two-dimensional laser sensor instrument distance；
S4 calculates the weights of importance of each particle according to the likelihood-domain of given map m and observation model and with new weight；
S5 is adaptively adjusted using Kullback-Leibler apart from resampling according to particle in the distribution situation of state space
Population needed for next iteration；
S6 determines the position of robot in the environment according to the distribution situation of particle.
2. a kind of adaptive Kano Meng Te localization method for incorporating two-dimensional barcode information according to claim 1, feature exist
In the motion model that step S1 is established after correcting error specifically includes:
Time interval (t-1, t] in, give motion information utAre as follows:
WhereinRespectively indicate the pose at t and t-1 moment under odometer coordinate system, utIt is transformed into the sequence of three steps
Column: initial rotation δrot1, translation δtransWith second of rotation δrot2, establish the model of kinematic error:
ε indicate mean value be 0, variance b2Noise variance, parameter alpha1-α4It is the error parameter for robot, they are specified to transport
Dynamic cumulative errors, therefore physical location xtFrom xt-1By initial rotation angleFollow translation distanceFollowed by another
A rotation angleIt obtains, so that
Then physical location xt=(x ', y ', θ ').
3. a kind of adaptive Kano Meng Te localization method for incorporating two-dimensional barcode information according to claim 2, feature exist
In the step S2 particle sampler is estimated the initial pose of robot, specifically included:
Motion model is sampled initial attitude xt-1, odometer read utX is read with cameracAs input, robot is being moved through
When arriving two dimensional code without scanning in journey, pose isWhen camera gets two dimension
Code information, the pose x with the sampling output of odometer at this timetIt is compared, the two error is greater than critical value τ, then sampling algorithm is defeated
Value out is the absolute value x of two dimensional code coordinatec, and enable the pose x at current timetFor the pose that two-dimensional barcode information provides, continue into
Row sampling is until scanning next two dimensional code.
4. a kind of adaptive Kano Meng Te localization method for incorporating two-dimensional barcode information according to claim 3, feature exist
In, the step S3 establishes observation model using two-dimensional laser sensor instrument distance, specifically includes the following steps:
Conditional probability distribution p (zt|xt, m) and it is observation model, a possibility that each single observation, which is multiplied, can be obtained probability such as
Shown in lower:
Wherein, xtIt is the pose of robot, ztIt is the observation of t moment, zt kIndicate k-th of distance measurement value of t moment.M is environment
Figure, it is assumed that independent between each observation beam noise.
5. a kind of adaptive Kano Meng Te localization method for incorporating two-dimensional barcode information according to claim 4, feature exist
In the step S4 calculates the weights of importance of each particle and with newly weighing according to the likelihood-domain of given map m and observation model
Value, specifically includes:
With likelihood-domain calculating observation probability, using map m as condition, obtain in x-y space with nearest barrier distance dist:
Since the noise of sensor different beams is independent from each other, to kValue be multiplied, by by one just
Too distribution is uniformly distributed the likelihood result q for being mixed to get observation model with one:
Give three parameter zhit、zrandAnd zmaxIt is weighted and averaged mixing, and zhit+zrand+zmax=1.
6. a kind of adaptive Kano Meng Te localization method for incorporating two-dimensional barcode information according to claim 5, feature exist
In, in the step S5, calculation method of the Kullback-Leibler apart from resampling:
KLD sampling all determines sample number to each particle filter iteration with probability 1- δ so that true Posterior distrbutionp be based on adopting
Error between the APPROXIMATE DISTRIBUTION of sample is less than ε, the size of resampling sample set is thereby determined that, when population n meets certain value
When, it is ensured that the K-L distance between the true value and estimated value of probability is less than threshold epsilon, at this time the value of n are as follows:
Wherein z1-δIt is the standardized normal distribution of upper quantile 1- δ, h indicates at least to be filled with the digit of the histogram of a particle,
Meeting nxBefore counting boundary, KLD sampling will generate always particle.
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| CN113124850A (en) * | 2019-12-30 | 2021-07-16 | 北京极智嘉科技股份有限公司 | Robot, map generation method, electronic device, and storage medium |
| CN113124850B (en) * | 2019-12-30 | 2023-07-28 | 北京极智嘉科技股份有限公司 | Robot, map generation method, electronic device, and storage medium |
| CN111176296B (en) * | 2020-01-20 | 2022-06-03 | 重庆邮电大学 | Control method for formation of mobile robots based on bar code disc |
| CN111176296A (en) * | 2020-01-20 | 2020-05-19 | 重庆邮电大学 | Control method for formation of mobile robots based on bar code disc |
| CN111765883A (en) * | 2020-06-18 | 2020-10-13 | 浙江大华技术股份有限公司 | Monte Carlo positioning method and equipment for robot and storage medium |
| CN111765883B (en) * | 2020-06-18 | 2023-12-15 | 浙江华睿科技股份有限公司 | Robot Monte Carlo positioning method, equipment and storage medium |
| CN111766603A (en) * | 2020-06-27 | 2020-10-13 | 长沙理工大学 | Mobile robot laser SLAM method, system, medium and equipment based on AprilTag code vision auxiliary positioning |
| CN111766603B (en) * | 2020-06-27 | 2023-07-21 | 长沙理工大学 | Mobile robot laser SLAM method, system, medium and equipment based on april tag code vision aided positioning |
| CN112762928A (en) * | 2020-12-23 | 2021-05-07 | 重庆邮电大学 | ODOM and DM landmark combined mobile robot containing laser SLAM and navigation method |
| CN112762928B (en) * | 2020-12-23 | 2022-07-15 | 重庆邮电大学 | ODOM and DM landmark combined mobile robot containing laser SLAM and navigation method |
| CN113916232A (en) * | 2021-10-18 | 2022-01-11 | 济南大学 | Map construction method and system for improving map optimization |
| CN113916232B (en) * | 2021-10-18 | 2023-10-13 | 济南大学 | Map construction method and system for improving map optimization |
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