CN110879069A - UWB SLAM-oriented Kalman/R-T-S hybrid positioning method and system - Google Patents

UWB SLAM-oriented Kalman/R-T-S hybrid positioning method and system Download PDF

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CN110879069A
CN110879069A CN201911267283.5A CN201911267283A CN110879069A CN 110879069 A CN110879069 A CN 110879069A CN 201911267283 A CN201911267283 A CN 201911267283A CN 110879069 A CN110879069 A CN 110879069A
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uwb
reference node
kalman filter
time
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徐元
申涛
毕淑慧
王自鹏
马荔瑶
张扬
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University of Jinan
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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
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    • G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation

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Abstract

The invention discloses a Kalman/R-T-S hybrid positioning method and a system facing UWB SLAM, comprising the following steps: taking the position, the speed and the course angle of the x direction and the y direction and the position of a UWB reference node as state vectors of the extended Kalman filter; taking the distance between the UWB measured robot and the UWB reference node as an observation vector of an extended Kalman filter to perform data fusion to obtain the estimated position of the UWB reference node; and smoothing the position of the UWB reference node estimated by the extended Kalman filter by using an R-T-S smoothing algorithm, averaging the positions of the UWB reference nodes smoothed at all times, and finally obtaining the optimal estimation of the mobile robot and the UWB reference node. The method utilizes the R-T-S smoothing algorithm to smooth the position of the UWB reference node estimated by the extended Kalman filter, and does not depend on the position information of the UWB reference node like the traditional UWB positioning algorithm; the introduction of the R-T-S smoothing algorithm increases the estimation precision of the position information of the reference node.

Description

UWB SLAM-oriented Kalman/R-T-S hybrid positioning method and system
Technical Field
The invention relates to the technical field of combined positioning in a complex environment, in particular to a Kalman/R-T-S mixed positioning method and system for UWB SLAM.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Mobile robot navigation and positioning are increasingly receiving attention from various scholars as the basis for providing high-quality services to human beings, and are becoming research hotspots in this field. However, as the application range of the mobile robot is expanded, the navigation environment to which the mobile robot is exposed is more complicated. Especially in indoor environment, the indoor layout, building materials and even space size of a building can influence navigation signals, and further influence positioning accuracy. Meanwhile, the relatively small platform of the mobile robot facing the indoor environment makes it impossible to install part of the high-precision navigation apparatus. Although the precision of small-sized navigation devices has improved to some extent in recent years with the progress of miniaturization of navigation devices, there is still a gap in performance compared with that of conventional large-sized high-precision navigation devices. In an indoor environment, how to utilize the acquired limited information to eliminate the influence of an indoor complex navigation environment on the accuracy and the real-time performance of the acquisition of the navigation information of the mobile robot, and ensure the continuous stability of the navigation precision of the mobile robot in the indoor environment has important scientific theoretical significance and practical application value.
Among the existing positioning methods, Global Navigation Satellite System (GNSS) is the most commonly used method. Although the GNSS can continuously and stably obtain the position information with high precision, the application range of the GNSS is limited by the defect that the GNSS is easily influenced by external environments such as electromagnetic interference and shielding, and particularly in some closed and environment-complex scenes such as indoor and underground roadways, GNSS signals are seriously shielded, and effective work cannot be performed. In recent years, uwb (ultra wideband) has shown great potential in the field of short-distance local positioning due to its high positioning accuracy in a complex environment. Researchers have proposed the use of UWB-based target tracking for pedestrian navigation in GNSS-disabled environments. Although indoor positioning can be realized by the method, because the indoor environment is complicated and changeable, UWB signals are easily interfered to cause the reduction of positioning accuracy and even the unlocking; meanwhile, because the communication technology adopted by the UWB is generally a short-distance wireless communication technology, if a large-range indoor target tracking and positioning is to be completed, a large number of network nodes are required to complete together, which inevitably introduces a series of problems such as network organization structure optimization design, multi-node multi-cluster network cooperative communication, and the like. UWB-based object tracking at the present stage therefore still faces many challenges in the field of indoor navigation.
In the aspect of navigation models, a loose integrated navigation model is widely applied in the field of indoor pedestrian integrated navigation. The model has the advantage of easy implementation, but it is noted that the implementation of the model requires multiple technologies participating in the integrated navigation to be able to perform navigation positioning independently. For example, UWB devices are required to provide navigation information of pedestrians, which requires that an environment where a target pedestrian is located must be able to acquire information of at least 3 reference nodes, which greatly reduces an application range of the integrated navigation model, and meanwhile, the sub-technologies participating in navigation perform positioning independently, introduce new errors, and are not beneficial to improving the accuracy of the integrated navigation technology. In order to overcome the problem, students propose to apply a tight combination model to the indoor pedestrian navigation field, and the tight combination model directly applies the original sensor data of the sub-technologies participating in the combined navigation to the final calculation of the navigation information, so that the risk of introducing new errors in the self-calculation of the sub-technologies is reduced, and the precision of the combined navigation is improved.
The precision of the existing UWB positioning technology depends heavily on the position precision of a UWB reference node, but the UWB positioning technology is difficult to realize in practical application; on the other hand, the UWB positioning algorithm relying only on the distance is also inaccurate in the positioning accuracy of the UWB reference node.
Disclosure of Invention
In order to solve the problems, the invention provides a Kalman/R-T-S hybrid positioning method and a system facing UWB SLAM, which take the position, the speed and the course of a mobile robot in x and y directions and the position of a UWB reference node as state vectors to construct a corresponding state equation; and taking the distance between the UWB reference node and the mobile robot obtained by UWB measurement as the observed quantity of the filter, performing data fusion through extended Kalman filter estimation, and smoothing the position of the UWB reference node estimated by the extended Kalman filter by using an R-T-S smoothing algorithm on the basis to finally obtain the optimal estimation of the mobile robot and the UWB reference node.
In some embodiments, the following technical scheme is adopted:
a Kalman/R-T-S hybrid positioning method facing UWB SLAM comprises the following steps:
taking the position, the speed and the course angle of the x direction and the y direction and the position of a UWB reference node as state vectors of the extended Kalman filter;
taking the distance between the UWB measured robot and the UWB reference node as an observation vector of an extended Kalman filter to perform data fusion to obtain the estimated position of the UWB reference node;
and smoothing the position of the UWB reference node estimated by the extended Kalman filter by using an R-T-S smoothing algorithm, averaging the positions of the UWB reference nodes smoothed at all times, and finally obtaining the optimal estimation of the mobile robot and the UWB reference node.
In other embodiments, the following technical solutions are adopted:
a Kalman/R-T-S hybrid positioning system facing UWB SLAM, comprising:
means for using the x and y direction positions, the velocity, the heading angle, and the UWB reference node position as state vectors for an extended Kalman filter;
the device is used for carrying out data fusion by taking the distance between the UWB measured robot and the UWB reference node as an observation vector of the extended Kalman filter to obtain the estimated position of the UWB reference node;
and the device is used for smoothing the position of the UWB reference node estimated by the extended Kalman filter by utilizing an R-T-S smoothing algorithm, averaging the positions of the UWB reference nodes smoothed at all times, and finally obtaining the optimal estimation of the mobile robot and the UWB reference node.
In other embodiments, the following technical solutions are adopted:
a terminal device comprising a processor and a computer-readable storage medium, the processor being configured to implement instructions; the computer readable storage medium stores a plurality of instructions adapted to be loaded by the processor and to perform the above-described uwb slam-oriented Kalman/R-T-S hybrid positioning method.
In other embodiments, the following technical solutions are adopted:
a computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to execute the above-described UWB SLAM oriented Kalman/R-T-S hybrid positioning method.
Compared with the prior art, the invention has the beneficial effects that:
the position, the speed and the course angle in the x direction and the y direction and the position of a UWB reference node are used as state vectors of an extended Kalman filter; and taking the distance between the UWB measured robot and the UWB reference node as an observation vector of the extended Kalman filter for data fusion, smoothing the position of the UWB reference node estimated by the extended Kalman filter by using an R-T-S smoothing algorithm on the basis, and solving the position of the smoothed UWB reference node to finally obtain the optimal estimation of the mobile robot and the UWB reference node.
According to the method, the position of the UWB reference node estimated by the extended Kalman filter is smoothed by using an R-T-S smoothing algorithm, so that the traditional UWB positioning algorithm does not need to rely on the position information of the UWB reference node; on the other hand, the introduction of the R-T-S smoothing algorithm increases the estimation precision of the reference node position information.
The invention can be used for positioning the mobile robot in indoor environment with high precision.
Drawings
FIG. 1 is a schematic diagram of a UWB robot positioning system according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a Kalman/R-T-S hybrid algorithm for UWB SLAM in an embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
In one or more embodiments, a UWB positioning system is disclosed, as shown in fig. 1, comprising: UWB, electron compass, data processing unit all fix on mobile robot to be connected with data processing unit through serial ports RS 232.
Wherein the content of the first and second substances,
UWB: the distance measuring device is used for measuring the distance between the mobile robot and the UWB reference node;
electronic compass: the device is used for measuring the course angle of the mobile robot;
code disc: for measuring the speed of the mobile robot;
a data processing unit: the method is used for carrying out data fusion on the acquired sensor data.
Example two
In one or more embodiments, a Kalman/R-T-S hybrid positioning method for UWB SLAM is disclosed, as shown in fig. 2, comprising:
a Kalman/R-T-S hybrid algorithm facing UWB SLAM is characterized in that the position, speed and course angle of x and y directions and the position of a UWB reference node are used as state vectors of an extended Kalman filter; taking the distance between the robot measured by the UWB and the UWB reference node as an observation vector of the extended Kalman filter for data fusion, smoothing the position of the UWB reference node estimated by the extended Kalman filter by using an R-T-S smoothing algorithm on the basis, and solving the position of the smoothed UWB reference node to finally obtain the optimal estimation of the mobile robot and the UWB reference node;
the state equation of the extended kalman filter is:
Figure BDA0002313214630000061
wherein (x)k,yk) The position of the mobile robot k in the x and y directions at the moment; (x)i,yi),i∈[1,2,...,g]The position of a UWB reference node k in x and y directions at the moment; vkIs the velocity at time k;
Figure BDA0002313214630000062
the course at the moment k is taken as the heading; t is the sampling period, omeganThe covariance matrix is Q, which is the system noise at time k.
The observation equation of the extended kalman filter is:
Figure BDA0002313214630000071
wherein d isiDistance between the robot and the ith reference node is measured by UWB at n moments; v iskTo observe noise, its covariance matrix is R.
The iterative equation of the extended kalman filter is:
Xk|k-1＝Ak-1Xk-1+ωk-1
Pk|k-1＝Ak-1Pk-1(Ak-1)T+Q
Kk＝Pk|k-1(Hk)T(HkPk|k-1(Hk)T+Rk)-1
Xk＝Xk|k-1+Kk[Yk-h(Xk|k-1)]
Pk＝(I-KkHk)Pk|k-1
wherein the content of the first and second substances,
Figure BDA0002313214630000072
the iterative equation of the R-T-S smoothing algorithm is:
Figure BDA0002313214630000073
Figure BDA0002313214630000074
Figure BDA0002313214630000075
wherein the content of the first and second substances,
Figure BDA0002313214630000076
error gain, P, for smoothingk|kIs the error matrix at time k,
Figure BDA0002313214630000077
Is a transposition of the system matrix, Pk+1|kIs an error matrix from k-1 to k times,
Figure BDA0002313214630000078
Estimation of the smooth state at time k,
Figure BDA0002313214630000079
Estimation of the state at time k,
Figure BDA00023132146300000710
Estimation of the smooth state at time k +1,
Figure BDA00023132146300000711
Is from k-1 to kEstimation of smooth state of the etching,
Figure BDA00023132146300000712
Is a smoothed error matrix at time k,
Figure BDA00023132146300000713
Smoothed error matrix, P, for time k +1k|k-1Is the error matrix from time k-1 to time k.
According to the method, the position of the UWB reference node estimated by the extended Kalman filter is smoothed by using an R-T-S smoothing algorithm, on one hand, the algorithm does not depend on the position information of the UWB reference node like the traditional UWB positioning algorithm; on the other hand, the introduction of the R-T-S smoothing algorithm increases the estimation precision of the reference node position information.
EXAMPLE III
In one or more embodiments, a UWB SLAM oriented Kalman/R-T-S hybrid positioning system is disclosed, comprising:
means for using the x and y direction positions, the velocity, the heading angle, and the UWB reference node position as state vectors for an extended Kalman filter;
the device is used for carrying out data fusion by taking the distance between the UWB measured robot and the UWB reference node as an observation vector of the extended Kalman filter to obtain the estimated position of the UWB reference node;
and the device is used for smoothing the position of the UWB reference node estimated by the extended Kalman filter by utilizing an R-T-S smoothing algorithm, averaging the positions of the UWB reference nodes smoothed at all times, and finally obtaining the optimal estimation of the mobile robot and the UWB reference node.
The specific working manner or implementation manner of each device in the system of this embodiment adopts the method in the first embodiment, and details are not described here.
Example four
In one or more implementations, a terminal device is disclosed that includes a server including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing a Kalman/R-T-S hybrid positioning method for UWB SLAM in example one when executing the program. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
The UWB SLAM-oriented Kalman/R-T-S hybrid positioning method in the first embodiment may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (7)

1. A Kalman/R-T-S hybrid positioning method facing UWB SLAM is characterized by comprising the following steps:
taking the position, the speed and the course angle of the x direction and the y direction and the position of a UWB reference node as state vectors of the extended Kalman filter;
taking the distance between the UWB measured robot and the UWB reference node as an observation vector of an extended Kalman filter to perform data fusion to obtain the estimated position of the UWB reference node;
and smoothing the position of the UWB reference node estimated by the extended Kalman filter by using an R-T-S smoothing algorithm, averaging the positions of the UWB reference nodes smoothed at all times, and finally obtaining the optimal estimation of the mobile robot and the UWB reference node.
2. The UWB SLAM-oriented Kalman/R-T-S hybrid positioning method of claim 1, wherein the extended Kalman filter has a state equation:
Figure FDA0002313214620000011
wherein (x)k,yk) The position of the mobile robot k in the x and y directions at the moment; (x)i,yi),i∈[1,2,...,g]The position of a UWB reference node k in x and y directions at the moment; vkIs the velocity at time k;
Figure FDA0002313214620000012
the course at the moment k is taken as the heading; t is the sampling period, omeganSystem noise at time kThe covariance matrix is Q.
3. The UWB SLAM-oriented Kalman/R-T-S hybrid positioning method of claim 1, wherein the observation equation of the extended Kalman filter is:
Figure FDA0002313214620000021
wherein d isi,kDistance between the robot and the ith reference node is measured by UWB at the moment k; (x)k,yk) Is the robot position at time k; v iskTo observe noise, its covariance matrix is R.
4. The hybrid positioning method of Kalman/R-T-S oriented to UWB SLAM as defined in claim 1, wherein the R-T-S smoothing algorithm is used to smooth the position of the UWB reference node estimated by the extended Kalman filter, and specifically comprises:
Figure FDA0002313214620000022
Figure FDA0002313214620000023
Figure FDA0002313214620000024
wherein the content of the first and second substances,
Figure FDA0002313214620000025
error gain, P, for smoothingk|kIs the error matrix at time k,
Figure FDA0002313214620000026
Is a transposition of the system matrix, Pk+1|kIs an error matrix from k-1 to k times,
Figure FDA0002313214620000027
Estimation of the smooth state at time k,
Figure FDA0002313214620000028
Estimation of the state at time k,
Figure FDA0002313214620000029
Estimation of the smooth state at time k +1,
Figure FDA00023132146200000210
Estimated for the smooth state from time k-1 to k,
Figure FDA00023132146200000211
Is a smoothed error matrix at time k,
Figure FDA00023132146200000212
Smoothed error matrix, P, for time k +1k|k-1Is the error matrix from time k-1 to time k.
5. A Kalman/R-T-S hybrid positioning system facing UWB SLAM, comprising:
means for using the x and y direction positions, the velocity, the heading angle, and the UWB reference node position as state vectors for an extended Kalman filter;
the device is used for carrying out data fusion by taking the distance between the UWB measured robot and the UWB reference node as an observation vector of the extended Kalman filter to obtain the estimated position of the UWB reference node;
and the device is used for smoothing the position of the UWB reference node estimated by the extended Kalman filter by utilizing an R-T-S smoothing algorithm, averaging the positions of the UWB reference nodes smoothed at all times, and finally obtaining the optimal estimation of the mobile robot and the UWB reference node.
6. A terminal device comprising a processor and a computer-readable storage medium, the processor being configured to implement instructions; a computer readable storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the UWB SLAM oriented Kalman/R-T-S hybrid positioning method of any one of claims 1 to 4.
7. A computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to execute the UWB SLAM oriented Kalman/R-T-S hybrid positioning method of any one of claims 1 to 4.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112558481A (en) * 2020-12-17 2021-03-26 广东工业大学 Course angle active disturbance rejection control method in ship control system and related device

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103471595A (en) * 2013-09-26 2013-12-25 东南大学 Inertial navigation system (INS)/wireless sensor network (WSN) indoor mobile robot tight-integration navigation-oriented iterative extended RTS average filtering method
CN105509739A (en) * 2016-02-04 2016-04-20 济南大学 Tightly coupled INS/UWB integrated navigation system and method adopting fixed-interval CRTS smoothing
US20170038214A1 (en) * 2015-08-06 2017-02-09 Gabriel Oren Benel Path Guidance System for the Visually Impaired
CN107255795A (en) * 2017-06-13 2017-10-17 山东大学 Localization Approach for Indoor Mobile and device based on EKF/EFIR mixed filterings
CN109269498A (en) * 2018-08-06 2019-01-25 济南大学 Towards auto-adaptive estimate EKF filtering algorithm and system with shortage of data UWB pedestrian navigation

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103471595A (en) * 2013-09-26 2013-12-25 东南大学 Inertial navigation system (INS)/wireless sensor network (WSN) indoor mobile robot tight-integration navigation-oriented iterative extended RTS average filtering method
US20170038214A1 (en) * 2015-08-06 2017-02-09 Gabriel Oren Benel Path Guidance System for the Visually Impaired
CN105509739A (en) * 2016-02-04 2016-04-20 济南大学 Tightly coupled INS/UWB integrated navigation system and method adopting fixed-interval CRTS smoothing
CN107255795A (en) * 2017-06-13 2017-10-17 山东大学 Localization Approach for Indoor Mobile and device based on EKF/EFIR mixed filterings
CN109269498A (en) * 2018-08-06 2019-01-25 济南大学 Towards auto-adaptive estimate EKF filtering algorithm and system with shortage of data UWB pedestrian navigation

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112558481A (en) * 2020-12-17 2021-03-26 广东工业大学 Course angle active disturbance rejection control method in ship control system and related device
CN112558481B (en) * 2020-12-17 2023-02-10 广东工业大学 Course angle active disturbance rejection control method in ship control system and related device

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