CN111090281B - Method and device for estimating robot azimuth based on improved particle filter algorithm - Google Patents
Method and device for estimating robot azimuth based on improved particle filter algorithm Download PDFInfo
- Publication number
- CN111090281B CN111090281B CN201911184272.0A CN201911184272A CN111090281B CN 111090281 B CN111090281 B CN 111090281B CN 201911184272 A CN201911184272 A CN 201911184272A CN 111090281 B CN111090281 B CN 111090281B
- Authority
- CN
- China
- Prior art keywords
- mobile robot
- azimuth
- significant
- improved particle
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000002245 particle Substances 0.000 title claims abstract description 62
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000001914 filtration Methods 0.000 claims abstract description 26
- 238000012545 processing Methods 0.000 claims abstract description 14
- 238000005259 measurement Methods 0.000 claims description 40
- 230000003068 static effect Effects 0.000 claims description 27
- 238000009826 distribution Methods 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 7
- 238000013178 mathematical model Methods 0.000 claims description 6
- 238000012952 Resampling Methods 0.000 claims description 5
- 230000002159 abnormal effect Effects 0.000 claims description 5
- 230000015556 catabolic process Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 238000006731 degradation reaction Methods 0.000 claims description 3
- 230000007704 transition Effects 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 2
- 238000012546 transfer Methods 0.000 claims description 2
- 230000001052 transient effect Effects 0.000 claims description 2
- 238000001514 detection method Methods 0.000 abstract description 7
- 230000006870 function Effects 0.000 description 13
- 230000000875 corresponding effect Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 230000002411 adverse Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000002688 persistence Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000005309 stochastic process Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 230000002459 sustained effect Effects 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a method and a device for estimating the accurate azimuth of a mobile robot based on an improved particle filter algorithm, comprising the following steps: s1, establishing a motion model of a mobile robot according to system dynamics characteristics; s2, acquiring azimuth information data of the mobile robot through a sensor, and processing significant errors in a system by adopting an improved particle filtering algorithm to perform corresponding compensation operation on the significant errors to obtain azimuth state parameters of the precisely estimated mobile robot. The invention realizes the detection, identification and compensation of the significant error based on the improved particle filter algorithm, thereby realizing the accurate azimuth estimation of the mobile robot system and effectively improving the accuracy of the azimuth estimation of the mobile robot.
Description
Technical Field
The invention relates to the field of mobile robot positioning, in particular to a method and a device for realizing accurate azimuth estimation of a mobile robot by utilizing an improved particle filter algorithm to process a significant error problem.
Background
Along with the rapid development of big data and artificial intelligence, the mobile robot has obtained rapid development and wide application with the advantages of large working space, strong adaptability and the like. The mobile robot moves in a complex environment, and the environment faced by the mobile robot has the characteristics of complexity, unknown and unstructured. In order to ensure that the robot can effectively complete various tasks in various environments, the robot has the capabilities of autonomous positioning navigation and path tracking so as to accurately estimate the direction of the robot. The self-positioning algorithm is one of key technologies for realizing the mobile robot, and the mobile robot positioning function is the most basic and important function in various mobile robot systems and is also the key for realizing various functions. Estimating the precise orientation is the basic requirement for the robot to work properly and is the basis for accomplishing other tasks.
As the most important state estimation tool, filters have undergone a development history from non-recursive to recursive, frequency to time domain, non-stationary stochastic processes to state space models. Today, there are numerous filtering algorithms for state estimation, most typically: kalman Filter (KF), extended Kalman Filter (Extended Kalman Filter, EKF), unscented Kalman Filter (Unscented Kalman Filter, KF), and Particle Filter (PF). The particle filter algorithm is said to be the most important filter method in the contemporary nonlinear filtering, and has great effect in various fields, and in recent years, domestic and foreign scholars combine the particle filter algorithm into the state estimation to form the state estimation based on the particle filter.
In practical systems, it is considered that the measurement data may be subject to interference from non-random events, i.e. significant errors. Significant errors are typically caused by a single or multiple of instrument failure, measurement device correction errors, sensor damage, analog-to-digital conversion errors, process imperfections, and the like. The presence of significant errors introduces inaccurate information that creates great difficulties in solving the problem of estimating the position of the mobile robot, and therefore, improvements are necessary.
Disclosure of Invention
The object of the present invention is to overcome the drawbacks and disadvantages of the prior art by providing a method and apparatus for estimating the orientation of a robot based on an improved particle filter algorithm. The method and the device process the problem of significant errors by utilizing an improved particle filter algorithm, and realize accurate azimuth estimation of the mobile robot.
In order to achieve the above object, the present invention provides a method for estimating a robot azimuth based on an improved particle filter algorithm, which is characterized by comprising:
s1, establishing a motion model of a mobile robot according to system dynamics characteristics;
s2, acquiring azimuth information data of the mobile robot through a sensor, and processing significant errors in a system by adopting an improved particle filtering algorithm to perform corresponding compensation operation on the significant errors to obtain azimuth state parameters of the precisely estimated mobile robot.
The further arrangement is that: the method for processing the significant errors in the system by adopting the improved particle filtering algorithm comprises the steps of firstly detecting the collected azimuth information data, judging whether significant errors exist or not, and if the significant errors do not exist, iterating to carry out next filtering estimation; if the significant error exists, the significant error identification is carried out to judge which type of the significant error belongs to, and after the significant error is judged, corresponding compensation operation is carried out on the significant error according to the type of the significant error.
The further arrangement is that: and taking the compensated azimuth information data as initial data of next filtering, and carrying out next state estimation.
The further arrangement is that: the significant error is set to be three types of abnormal value, static difference and drift, wherein the abnormal value is represented by a plurality of burst peaks in the measured data; static difference refers to residual deviation after the transient process is completed, namely, the difference between the stable value of the controlled variable and the given value, the value of the static difference can be positive or negative, and the static difference requirement is limited to a certain allowable small range near the given value, and the static difference is expressed as a continuous and relatively stable error value generated on a measuring device; drift reflects the continuous or incremental change in the measurement characteristic of a measuring instrument over a period of time under defined conditions.
The invention also provides a method and a device for estimating the azimuth of the robot based on the improved particle filtering algorithm, which comprises an azimuth signal collecting module, a signal processing module and an upper computer, wherein azimuth information of the mobile robot is collected through the azimuth signal collecting module and is input into the signal processing module for processing by the improved particle filtering algorithm, the significant errors are identified and are set into three types of abnormal values, static differences and drifting after being identified, and the significant errors are correspondingly compensated according to the type of the significant errors to obtain the azimuth state parameters of the precisely estimated mobile robot.
The invention realizes the detection, identification and compensation of the significant error based on the improved particle filter algorithm, thereby realizing the accurate azimuth estimation of the mobile robot system and effectively improving the accuracy of the azimuth estimation of the mobile robot.
The method and the device realize the detection and compensation of the significant error based on the improved particle filter algorithm, thereby realizing the accurate azimuth estimation of the mobile robot system and effectively improving the accuracy of the azimuth estimation of the mobile robot.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are required in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that it is within the scope of the invention to one skilled in the art to obtain other drawings from these drawings without inventive faculty.
FIG. 1 is a flow chart of accurate position estimation for a mobile robot;
FIG. 2 contains measurement data of outliers;
FIG. 3 contains measurement data of static differences;
FIG. 4 contains measurement data of drift;
FIG. 5 is a dynamic model of a mobile robot nonlinear system;
FIG. 6 a dynamic model in the global coordinate system of the mobile robot;
fig. 7 is a modified particle filter algorithm based on significant errors.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings, for the purpose of making the objects, technical solutions and advantages of the present invention more apparent.
As shown in fig. 1, the method in this embodiment includes: establishing a motion model of the mobile robot according to the dynamic characteristics of the system; acquiring azimuth information data of the mobile robot through a sensor; the method provided by the invention can be used for verifying the superiority of the improved particle filtering algorithm by comparing the running tracks of the mobile robot before and after compensating the significant errors.
S1: establishing a motion model of the mobile robot according to the dynamic characteristics of the system
The mobile robot azimuth estimation parameters are set, and a mathematical model of the nonlinear azimuth estimation system is established, as shown in fig. 5. When the azimuth variable of the mobile robot is selected, the linear velocity v and the steering angular velocity w are selected as measurement data, and the position and the gesture of the mobile robot are obtained: x, y and θ. Due to the noise, there is some error in both measurement and control, i.e. noise information is present for both v and w. The nonlinear description thereof can be expressed as follows:
according to the dynamic model of the mobile robot, a mathematical model of the mobile robot under a global coordinate system is built, so that the construction of a system state space is realized, and the construction is shown in fig. 6. A state space model of the particle filter algorithm is built from the mathematical model, the expression of which is shown below.
Wherein the state space model comprises 6 state variables, respectively: x, y, θ, v x ，v y ，v θ . V herein x And v y Respectively representing the linear velocity of the x-axis and the y-axis, v θ Indicating steering angular velocity, i.e. v θ =w. The data obtained in the step form a real running track model of the mobile robot, and the real running track model is used for comparing with an estimated track obtained after filtering processing.
S2: accurate estimation of azimuth parameters of mobile robot by adopting improved particle filtering algorithm
In step 1, mathematical modeling is performed on the position estimation system-mobile robot, and a state space model of the mobile robot is obtained. This is followed by a process of processing the collected data, the specific steps of which are shown below.
1) First, according to the principle method of particle filtering, the method is performed from the prior probability p (x k |x k-1 ) A set of random samples, called particlesi represents the ith particle and the weight of the initial particle is set to +.>
2) In the prediction phase, the prior sample set at the k moment is calculated according to the state transfer equation by using the particles at the k-1 moment:
3) In the update phase, based on the measured data y k And computing a weight for each particle from the a priori samplesWherein->Is a likelihood probability. And then carrying out normalization processing on the weights so as to unify the distribution characteristics of the samples. Posterior distribution after update:
4) Due to p (x k |y 1：k ) The probability density function is not a conventional probability density function, and direct sampling cannot be performed, so importance sampling is introduced to obtain particle swarm and joint weight. By defining an importance density q (x k |y 1：k ) Then the joint weights are expressed as:
5) Normalizing the weights using the state transition probability function for the proposed distribution:
6) In the iterative process, covariance of importance weights increases due to particle degradation, which can adversely affect accuracy of state estimation. Thus introducing resampling, weighting the parent particlesCopying according to the weight size to obtain child particles, and discarding parent particles with small weight. Setting an effective particle number (Neff) to measure the degradation degree of the particle weight: />After resampling, the posterior estimate of the daughter particles is expressed as:
7) Calculating a state estimation vectorAnd corrected measured values>
8) The method for detecting the significant error of the corrected measured value is to calculate the residual size, and the residual is represented by the following steps:
the significant errors mainly comprise abnormal values, static differences and drift types. Wherein outliers are manifested as several burst peaks in the measured data, as shown in fig. 2. Static difference refers to residual deviation after the transition process is completed, namely, the difference between the stable value and the given value of the controlled variable, and the value can be positive or negative, and is an important indicator for indicating accuracy. The static difference requirement of the controlled variable in production is limited to a certain allowed small range around a given value. Which appears to produce a sustained and relatively stable error value on the measurement device, as shown in fig. 3. Drift reflects the ability of a measuring instrument to measure a continuous or incremental change in a property over a period of time under defined conditions, maintaining its constant measured property over a period of time. Drift is often caused by external factors such as pressure, temperature, humidity, etc., or by instabilities in the performance of the instrument itself. It is difficult to correct if the measurement error drifts. The measurement error with drift is much more complex than the other two types of errors, as shown in fig. 4.
Three types of detection modes of significant errors are described as follows:
(1) if an outlier appears at k 0 In the mth measurement of the step, then its observation function may represent:
wherein, the liquid crystal display device comprises a liquid crystal display device,represents k 0 Step m measures the magnitude of the outlier.
Since outliers occur primarily in the form of independent and occasional peaks, outliers at one time are often not correlated with other times. The detection of outliers is achieved with a distance scale based on the measurement residual vector r and the response time point k. For example, at k c The measurement residual difference point of the m-th measurement data at the moment contains significant errors isOther all measurement residual points ∈ ->Is the minimum distance D of (2) min Can be expressed as:
since the static and drift are formally many consecutive data points and the outlier is composed of several isolated burst peaks, D is the static and drift min The value will be significantly lower than the measurement value containing the outlier. When no outliers appear in the measurements, all D min Dots and dotsThe random distribution should be presented substantially, and in order to test this assumption, the following assumptions in the test procedure should satisfy the gaussian distribution.
Detection statistic D min ＜Z α/2 When receiving H 0 I.e. y m，k Is not considered an outlier. Conversely, satisfy alternative hypothesis H 1 When y is m，k Considered as outliers, outliers can be expressed as:
(2) in the case where the mth measurement value exhibits a static difference, the observation function can be expressed as:
wherein B is m Representing the static difference of the mth measurement.
The static difference is manifested by the creation of a continuous and relatively stable error value on the measuring device, which is herein utilized by the residual time sequence r of measured values m m，1 ，r m，2 ，…,r m，k To estimate the measurement error including the static error. Specifically, a moving window of time spanning W is used to calculate the data point r m，k-W+1 ，r m，k-W+2 ，…,r m，k The mean and variance of (2) are as follows:
average value:
variance:
due toInterference w k Obeying the white noise sequence, variance S 2 The F distribution will be obeyed and thus an appropriate threshold can be chosen to identify which measurements are currently most relevant to both significant errors, static differences or drift. S is S 2 The variance of (c) can be obtained by the following hypothesis test:
from the characteristics of the statics and drift, systematic statics will produce steady-state persistence error values, so the latest W data point variance of the statics will be much smaller than those that drift. For statistics S 2 Is calculated based on the F distribution. When variance S 2 If the data point is smaller than the predefined threshold epsilon, the data point corresponding to the mth measured value is judged to be static, otherwise, the data point is classified as drift. The size of the mth measurement containing the static difference can be estimated by:
(3) in the event of an mth measurement drift, the measurement function can be expressed as:
wherein D is m (k) Is a function describing the variation of the measurement error drift, which may be a linear, nonlinear or even periodic function. It is assumed here that the function is continuous and locally linearisable.
In describing the drift function D m (k) When linear regression based on calculated residuals is used to analyze the trend, then the slope and intercept after fitting are used to estimate the variance. The mth measurement size containing drift is calculated as follows:
C m，k ＝D m (k)≈a m，k k+b m，k 9) In the detection of significant errorsAnd after determining its size significant errors should be eliminated, i.e. compensation of the measured values is achieved. Compensated measurement y' k Can be expressed as:
y′ k ＝y k -C m，k
the update of the corresponding weights is as follows:
the updated weight value is used in the resampling stage of particle filtering, and the state variable estimated value is obtained by deductionAnd corrected measured values +.>By means of correction measured values->And updating to obtain measurement residual information. Due to C m，k Is estimated from the measurement residual time series information so that the updated measurement residual can be used to improve the measurement compensation by a subsequent amount.
After considering the significant error problem, the principle of the improved particle filter algorithm based on significant errors is summarised as shown in fig. 7.
Step 3: and comparing the real running track of the mobile robot with the estimated running track under the two conditions of compensating significant errors and uncompensated significant errors after filtering. Since significant errors can adversely affect the state estimation, to achieve accurate state estimation, significant errors should be detected in the state estimation based on particle filtering. A state estimation system of a mobile robot based on improved particle filtering. It comprises three parts: detecting the measurement, identifying significant errors, and compensating the measurement.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in implementing the methods of the above embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc.
The foregoing disclosure is illustrative of the present invention and is not to be construed as limiting the scope of the invention, which is defined by the appended claims.
It should be noted that embodiments of the present invention may be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a programmable memory or a data carrier such as an optical or electronic signal carrier.
While the invention has been described with reference to several particular embodiments, it should be understood that the invention is not limited to the particular embodiments disclosed. The invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (4)
1. A method for estimating a robot bearing based on an improved particle filter algorithm, comprising:
s1, establishing a motion model of a mobile robot according to system dynamics characteristics;
s2, acquiring azimuth information data of the mobile robot through a sensor, and processing significant errors in a system by adopting an improved particle filtering algorithm to perform corresponding compensation operation on the significant errors to obtain azimuth state parameters of the precisely estimated mobile robot;
the method for processing the significant errors in the system by adopting the improved particle filtering algorithm comprises the steps of firstly detecting the collected azimuth information data, judging whether significant errors exist or not, and if the significant errors do not exist, iterating to carry out next filtering estimation; if the significant error exists, distinguishing which type of significant error belongs to, and after distinguishing, carrying out corresponding compensation operation on the significant error according to the type of the significant error;
the step S1 specifically comprises the following steps:
setting the azimuth estimation parameters of the mobile robot, establishing a mathematical model of a nonlinear azimuth estimation system, and selecting the linear velocity v and the steering angular velocity w as measurement data when the azimuth variable of the mobile robot is selected to obtain the position and the gesture of the mobile robot: x, y and θ, the nonlinear descriptions of which are expressed as follows:
according to the dynamic model of the mobile robot, a mathematical model of the mobile robot under a global coordinate system is established, and according to the mathematical model, a state space model of a particle filtering algorithm is established, wherein the state space model is expressed as follows:
wherein the state space model comprises 6 state variables, respectively: x, y, θ, v x ，v y ，v θ V herein x And v y Respectively representing the linear velocity of the x-axis and the y-axis, v θ Indicating steering angular velocity, i.e. v θ ＝w；
The step S2 specifically comprises the following steps:
1) First, according to the principle method of particle filtering, the method is performed from the prior probability p (x k |x k-1 ) A set of random samples, called particlesi represents the ith particle and the weight of the initial particle is set to +.>
2) In the prediction phase, the prior sample set at the k moment is calculated according to the state transfer equation by using the particles at the k-1 moment:
3) In the update phase, based on the measured data y k And computing a weight for each particle from the a priori samplesWherein->Likelihood probability, then, carrying out normalization processing on the weights so as to unify the distribution characteristics of the samples, and updating posterior distribution:
4) By defining an importance density q (x k |y 1：k ) Then the joint weights are expressed as:
5) Normalizing the weights using the state transition probability function for the proposed distribution:
6) Introducing resampling to obtain parent particles with high weightCopying according to the weight as child particles, discarding parent particles with small weights, and setting an effective particle number Neff to measure the degradation degree of the particle weight: />By re-samplingAfter the sample, the posterior estimate of the daughter particles is expressed as:
7) Calculating a state estimation vectorAnd corrected measured values>
8) The method for detecting the significant error of the corrected measured value is to calculate the residual size, and the residual is represented by the following steps:
9) After the significant error has been detected and its size has been determined, it is to be eliminated, i.e. a compensation of the measured value is effected, the compensated measured value y' k Expressed as:
y′ k ＝y k -C m，k
the update of the corresponding weights is as follows:
the updated weight value is used in the resampling stage of particle filtering, and the state variable estimated value is obtained by deductionAnd corrected measured values +.>By means of correction measured values->Updating to obtain measurement residual information due to C m，k Is estimated from the measurement residual time series information, so that the updated measurement residual is used to improve the measurement compensation of the subsequent quantity.
2. The method for estimating a robot orientation based on an improved particle filter algorithm of claim 1, wherein: and taking the compensated azimuth information data as initial data of next filtering, and carrying out next state estimation.
3. The method for estimating a robot's azimuth based on an improved particle filter algorithm according to claim 1, wherein the significant error is set to three types of outliers, dead differences and drift, wherein the outliers are represented by the occurrence of several burst peaks in the measured data; static difference refers to residual deviation after the transient process is completed, namely, the difference between the stable value of the controlled variable and the given value, the value of the static difference can be positive or negative, and the static difference requirement is limited to a certain allowable small range near the given value, and the static difference is expressed as a continuous and relatively stable error value generated on a measuring device; drift reflects the continuous or incremental change in the measurement characteristic of a measuring instrument over a period of time under defined conditions.
4. The device for estimating the azimuth of the mobile robot based on the improved particle filtering algorithm is characterized in that the device estimates the accurate azimuth of the mobile robot by adopting the method as claimed in any one of claims 1-3, and comprises an azimuth signal collecting module, a signal processing module and an upper computer, wherein azimuth information of the mobile robot is collected through the azimuth signal collecting module and is input into the signal processing module to be processed by the improved particle filtering algorithm, the obvious errors existing in the mobile robot are identified and are set into three types of abnormal values, static differences and drifting after being identified, and corresponding compensation operation is carried out on the obvious errors according to the type of the obvious errors to obtain the azimuth state parameters of the accurately estimated mobile robot.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911184272.0A CN111090281B (en) | 2019-11-27 | 2019-11-27 | Method and device for estimating robot azimuth based on improved particle filter algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911184272.0A CN111090281B (en) | 2019-11-27 | 2019-11-27 | Method and device for estimating robot azimuth based on improved particle filter algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111090281A CN111090281A (en) | 2020-05-01 |
| CN111090281B true CN111090281B (en) | 2023-07-28 |
Family
ID=70393136
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911184272.0A Active CN111090281B (en) | 2019-11-27 | 2019-11-27 | Method and device for estimating robot azimuth based on improved particle filter algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111090281B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113791578B (en) * | 2021-08-23 | 2023-05-02 | 五邑大学 | Track filtering method and device based on numerical control machining system and electronic equipment |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7289906B2 (en) * | 2004-04-05 | 2007-10-30 | Oregon Health & Science University | Navigation system applications of sigma-point Kalman filters for nonlinear estimation and sensor fusion |
| KR100809352B1 (en) * | 2006-11-16 | 2008-03-05 | 삼성전자주식회사 | Method and apparatus of pose estimation in a mobile robot based on particle filter |
| CN106291645B (en) * | 2016-07-19 | 2018-08-21 | 东南大学 | The volume kalman filter method coupled deeply suitable for higher-dimension GNSS/INS |
| CN107246873A (en) * | 2017-07-03 | 2017-10-13 | 哈尔滨工程大学 | A kind of method of the mobile robot simultaneous localization and mapping based on improved particle filter |
| CN108318038A (en) * | 2018-01-26 | 2018-07-24 | 南京航空航天大学 | A kind of quaternary number Gaussian particle filtering pose of mobile robot calculation method |
| CN109459033A (en) * | 2018-12-21 | 2019-03-12 | 哈尔滨工程大学 | A kind of robot of the Multiple fading factor positions without mark Fast synchronization and builds drawing method |
-
2019
- 2019-11-27 CN CN201911184272.0A patent/CN111090281B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN111090281A (en) | 2020-05-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109829938B (en) | Adaptive fault-tolerant volume Kalman filtering method applied to target tracking | |
| CN109813342B (en) | Fault detection method and system of inertial navigation-satellite integrated navigation system | |
| CN112013836A (en) | Attitude heading reference system algorithm based on improved adaptive Kalman filtering | |
| CN114166221B (en) | Auxiliary transportation robot positioning method and system in dynamic complex mine environment | |
| US8949027B2 (en) | Multiple truth reference system and method | |
| CN110287537B (en) | Wild value resistant self-adaptive Kalman filtering method for frequency standard output jump detection | |
| CN111090281B (en) | Method and device for estimating robot azimuth based on improved particle filter algorithm | |
| KR20070036009A (en) | Methods and apparatus for real time position surveying using inertial navigation | |
| EP2869026B1 (en) | Systems and methods for off-line and on-line sensor calibration | |
| CN115902963A (en) | Single-point positioning data processing method and device, electronic equipment and storage medium | |
| CN110702093B (en) | Particle filter-based positioning method and device, storage medium and robot | |
| Yan et al. | An adaptive nonlinear filter for integrated navigation systems using deep neural networks | |
| CN111766573B (en) | Method and system for improving array grating positioning spatial resolution through Kalman filtering | |
| Zhang et al. | Adaptive cubature Kalman filter based on the variance-covariance components estimation | |
| Trehag et al. | Onboard estimation and classification of a railroad curvature | |
| CN115615456A (en) | Sensor error registration method and device based on iteration nearest integer point set | |
| CN113255851A (en) | Data fusion method and device for robot joint optomagnetic hybrid encoder | |
| CN112710306A (en) | Self-positioning method for BDS and INS combined navigation for train | |
| CN113483762A (en) | Pose optimization method and device | |
| Vazquez-Olguin et al. | Design of blind robust estimator for smart sensors | |
| Javadi et al. | Robust, Evidence-Based Data Fusion | |
| Zhang et al. | A robust particle filter for ambiguous updates of underwater terrain-aided navigation | |
| CN111007553B (en) | Navigation method and device of measured object, computer equipment and storage medium | |
| CN114714352B (en) | Robot pose information determining method, device, equipment and storage medium | |
| Hajiyev et al. | Estimation of Unmanned Aerial Vehicle Dynamics in the Presence of Sensor Faults |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |