CN115615433A - Hybrid positioning method and system based on extended Kalman and R-T-S smoothing algorithm - Google Patents
Hybrid positioning method and system based on extended Kalman and R-T-S smoothing algorithm Download PDFInfo
- Publication number
- CN115615433A CN115615433A CN202110784429.4A CN202110784429A CN115615433A CN 115615433 A CN115615433 A CN 115615433A CN 202110784429 A CN202110784429 A CN 202110784429A CN 115615433 A CN115615433 A CN 115615433A
- Authority
- CN
- China
- Prior art keywords
- robot
- extended kalman
- moment
- smoothing algorithm
- smoothing
- 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.)
- Pending
Links
- 238000009499 grossing Methods 0.000 title claims abstract description 52
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 39
- 230000008859 change Effects 0.000 claims abstract description 27
- 238000012935 Averaging Methods 0.000 claims abstract description 8
- 239000011159 matrix material Substances 0.000 claims description 16
- 230000008569 process Effects 0.000 claims description 7
- 239000013598 vector Substances 0.000 claims description 4
- 230000004927 fusion Effects 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 2
- 230000017105 transposition Effects 0.000 claims description 2
- 230000004807 localization Effects 0.000 claims 4
- 230000003068 static effect Effects 0.000 abstract description 5
- 238000005516 engineering process Methods 0.000 description 9
- 238000004891 communication Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 241000282414 Homo sapiens Species 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 239000004566 building material Substances 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Images
Classifications
-
- 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
- G01C21/20—Instruments for performing navigational calculations
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a hybrid positioning method and a system based on an extended Kalman and R-T-S smoothing algorithm, which comprises the following steps: obtaining the prediction of the position of the robot at each moment, and judging the position change conditions of the robot in the X direction and the Y direction; and for the direction of which the position change does not exceed the set threshold, smoothing the position of the robot in the direction by utilizing an R-T-S smoothing algorithm, and averaging the smoothed positions to obtain the optimal position estimation of the robot in the direction. The invention respectively judges the position change conditions of the robot in the X direction and the Y direction, smoothes the direction of which the position change is less than the set threshold value, can effectively improve the navigation estimation precision of the local direction in a static state, and further improves the whole navigation precision.
Description
Technical Field
The invention relates to the technical field of combined positioning in a complex environment, in particular to a hybrid positioning method and a hybrid positioning system based on an extended Kalman and R-T-S smoothing algorithm.
Background
The statements in this section merely provide background information related to the present disclosure and may not 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 conventional positioning methods, a 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 wide band) 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 complex and changeable, UWB signals are easily interfered, so that the positioning accuracy is reduced and even the lock is lost; 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. Therefore, UWB-based object tracking 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 needs to be pointed out that the implementation of the model requires multiple technologies participating in the combined navigation to be able to independently complete the navigation positioning. 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 at least 3 pieces of reference node information, which greatly reduces the application range of the integrated navigation model, and meanwhile, the sub-technologies involved in navigation perform positioning independently, and introduce new errors, which is not beneficial to improving the precision 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, UWB positioning algorithms that rely solely on distance are not accurate for UWB reference node accuracy.
In the prior art, the position of a UWB reference node estimated by an extended Kalman filter is smoothed by using a smoothing algorithm, and the optimal estimation of a mobile robot and the UWB reference node is finally obtained; the method can solve the problem that the position accuracy of the UWB reference node is not high, however, the method only predicts the static reference node, and in the practical application process, the target carrier can usually move, so that the navigation effect is relatively small.
Disclosure of Invention
In order to solve the problems, the invention provides a hybrid positioning method and a hybrid positioning system based on an extended Kalman and R-T-S smoothing algorithm, which can effectively improve the navigation prediction precision of a local direction in a static state, and further improve the whole navigation precision.
In some embodiments, the following technical scheme is adopted:
a hybrid positioning method based on an extended Kalman and R-T-S smoothing algorithm comprises the following steps:
obtaining the pre-estimation of the position of the robot at each moment, and judging the position change conditions of the robot in the X direction and the Y direction;
and for the direction of which the position change does not exceed the set threshold, smoothing the position of the robot in the direction by utilizing an R-T-S smoothing algorithm, and averaging the smoothed positions to obtain the optimal position estimation of the robot in the direction.
In other embodiments, the following technical solutions are adopted:
a hybrid positioning system based on an extended Kalman and R-T-S smoothing algorithm, comprising:
the position estimation module is used for acquiring estimation of the position of the robot at each moment and judging the position change conditions of the robot in the X direction and the Y direction;
and the position smoothing module is used for smoothing the position of the robot in the direction by utilizing an R-T-S smoothing algorithm for the direction of which the position change does not exceed the set threshold value, and averaging the smoothed positions to obtain the optimal position estimation of the robot in the direction.
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 a processor and to perform the hybrid positioning method based on an extended Kalman and R-T-S smoothing algorithm described above.
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 hybrid positioning method based on an extended Kalman and R-T-S smoothing algorithm described above.
Compared with the prior art, the invention has the beneficial effects that:
the invention respectively judges the position change conditions of the robot in the X direction and the Y direction, smoothes the direction of which the position change is less than the set threshold value, can effectively improve the navigation estimation precision of the local direction in a static state, and further improves the whole navigation precision.
Drawings
FIG. 1 is a schematic diagram of a robot positioning system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a hybrid positioning method based on an extended Kalman and R-T-S smoothing algorithm according to 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 exemplary 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 hybrid positioning method based on extended Kalman and R-T-S smoothing algorithms is disclosed, the positioning method based on a robot positioning system as shown in fig. 1; the method specifically comprises the following steps: UWB, electron compass and code wheel all fix on mobile robot to be connected with data processing unit through serial ports RS 232.
Wherein,
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.
Based on the robot positioning system, referring to fig. 2, the hybrid positioning method based on the extended Kalman algorithm and the R-T-S smoothing algorithm disclosed in this embodiment specifically includes the following steps:
(1) The method comprises the steps of obtaining the pre-estimation of the position of a robot at each moment through an extended Kalman (Kalman) filter, and judging the position change conditions of the robot in the X direction and the Y direction;
(2) And for the direction of which the position change does not exceed the set threshold, smoothing the position of the robot in the direction by utilizing an R-T-S smoothing algorithm, and averaging the smoothed positions to obtain the optimal position estimation of the robot in the direction.
In this embodiment, the smoothing position is determined according to the time when the robot is relatively stationary in a certain local direction, and therefore the smoothing position is not fixed.
Specifically, the estimation of the position of the robot at each moment is obtained, and the specific process is as follows:
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; and performing 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 optimal position prediction of the mobile robot.
The state equation of the extended Kalman filter is as follows:
wherein (x) k ,y k ) The position of the mobile robot k in the x and y directions at the moment; (x) i ,y i ),i∈[1,2,...,g]Position of the reference node k for UWB in x and y directions at time; v k Is the velocity at time k;
the course at the moment k is taken as the course; t is the sampling period, omega n The covariance matrix is Q, which is the system noise at time k.
The observation equation of the extended kalman filter is:
wherein, d i Distance between the robot and the ith reference node is measured by UWB at n moments; v is k To observe noise, its covariance matrix is R.
The iterative equation of the extended kalman filter is:
X k|k-1 ＝A k-1 X k-1 +ω k-1
P k|k-1 ＝A k-1 P k-1 (A k-1 ) T +Q
K k ＝P k|k-1 (H k ) T (H k P k|k-1 (H k ) T +R k ) -1
X k ＝X k|k-1 +K k [Y k -h(X k|k-1 )]
P k ＝(I-K k H k )P k|k-1
wherein,
judging the position change situation of the robot in the X direction and the Y direction specifically comprises the following steps:
according to the estimation of the position of the robot at each moment, performing difference between the position of the robot at the current moment in the X direction and the position of the robot at the previous moment in the X direction to obtain the position change condition of the robot in the X direction;
and performing difference between the estimated position of the robot in the Y direction at the current moment and the estimated position of the robot in the Y direction at the previous moment to obtain the position change condition of the robot in the Y direction.
When the position change in a certain direction does not exceed a set threshold value, the robot can be considered to be in a static state in the direction; and at the moment, smoothing the position of the robot in the direction by using an R-T-S smoothing algorithm, and averaging the smoothed positions to obtain the prediction of the optimal position of the robot in the direction at the current moment.
And regarding the direction of which the position change exceeds the set threshold, the robot is considered to be in a motion state in the direction, the motion state is not smoothed, and the result of the Kalman filter is directly used as the prediction of the position of the mobile robot at the current moment.
In this embodiment, the process of smoothing the position of the robot in the direction by using the R-T-S smoothing algorithm specifically includes:
wherein,
error gain, P, for smoothing k|k Is the error matrix at time k,
Is a transposition of the system matrix, P k+1|k Is an error matrix from k-1 to k times,
Estimation of the smooth state at time k,
At time kState estimation,
Estimation of the smooth state at time k +1,
Estimated for the smooth state from time k-1 to k,
Is a smoothed error matrix at time k,
Smoothed error matrix, P, for time k +1 k|k-1 Is the error matrix from time k-1 to time k.
Example two
In one or more embodiments, a hybrid positioning system based on extended Kalman and R-T-S smoothing algorithms is disclosed, comprising:
the position estimation module is used for acquiring the estimation of the position of the robot at each moment and judging the position change conditions of the robot in the X direction and the Y direction;
and the position smoothing module is used for smoothing the position of the robot in the direction in which the position change does not exceed the set threshold value by utilizing an R-T-S smoothing algorithm, and averaging the smoothed positions to obtain the optimal position estimation of the robot in the direction.
It should be noted that, the specific implementation process of the modules is already described in the first embodiment, and is not described in detail here.
EXAMPLE III
In one or more embodiments, a terminal device is disclosed, comprising a server including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements a hybrid positioning method based on extended Kalman and R-T-S smoothing algorithms in the first embodiment 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 processor, a digital signal processor DSP, an application specific integrated circuit ASIC, an off-the-shelf programmable gate array FPGA or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or the like. 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 by instructions in the form of software.
The hybrid positioning method based on the extended Kalman algorithm and the R-T-S smoothing algorithm 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, etc. as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and combines hardware thereof to complete the steps of the method. To avoid repetition, it is not described in detail here.
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 invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive changes in the technical solutions of the present invention.
Claims (10)
1. A hybrid positioning method based on an extended Kalman and R-T-S smoothing algorithm is characterized by comprising the following steps:
obtaining the pre-estimation of the position of the robot at each moment, and judging the position change conditions of the robot in the X direction and the Y direction;
and for the direction of which the position change does not exceed the set threshold, smoothing the position of the robot in the direction by utilizing an R-T-S smoothing algorithm, and averaging the smoothed positions to obtain the optimal position estimation of the robot in the direction.
2. The hybrid localization method based on extended Kalman and R-T-S smoothing algorithm as claimed in claim 1, wherein for the direction where the position change exceeds the set threshold, the obtained estimated value of the robot position at the current time is directly used as the optimal location estimate of the robot in the direction.
3. The hybrid positioning method based on the extended Kalman and R-T-S smoothing algorithm as claimed in claim 1, wherein the obtaining of the prediction of the robot position at each moment comprises the following specific processes:
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;
and performing 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 prediction of the position of the robot.
4. The hybrid localization method based on extended Kalman and R-T-S smoothing algorithm according to claim 3, wherein the state equation of the extended Kalman filter is:
wherein (x) k ,y k ) The position of the mobile robot k in the x and y directions at the moment; (x) i ,y i ),i∈[1,2,...,g]The position of a UWB reference node k in x and y directions at the moment; v k Is the velocity at time k;
the course at the moment k is taken as the course; t is the sampling period, omega n The covariance matrix is Q, which is the system noise at time k.
5. The hybrid localization method based on extended Kalman and R-T-S smoothing algorithm as claimed in claim 3, wherein the observation equation of the extended Kalman filter is:
wherein d is i,k Distance between the robot and the ith reference node is measured by UWB at the moment k; (x) k ,y k ) Is the robot position at time k; v is k To observe noise, the covariance matrix is R.
6. The hybrid positioning method based on the extended Kalman and R-T-S smoothing algorithm as claimed in claim 1, wherein the position change condition of the robot in X direction and Y direction is judged by the following specific processes:
according to the estimation of the position of the robot at each moment, the difference is made between the position of the robot at the current moment in the X direction and the position of the robot at the previous moment in the X direction, so that the position change condition of the robot in the X direction is obtained;
and performing difference between the estimated position of the robot in the Y direction at the current moment and the estimated position of the robot in the Y direction at the previous moment to obtain the position change condition of the robot in the Y direction.
7. The hybrid positioning method based on the extended Kalman and the R-T-S smoothing algorithm as claimed in claim 1, wherein the R-T-S smoothing algorithm is used to smooth the position of the robot in the direction, and the specific process is as follows:
wherein,
error gain, P, for smoothing k|k Is the error matrix at time k,
Is a transposition of the system matrix, P k+1|k Is an error matrix from k-1 to k times,
Estimation of the smooth state at time k,
Estimation of the state at time k,
Estimated for the smooth state at time k +1,
Estimated for the smooth state from time k-1 to k,
Is a smoothed error matrix at time k,
Smoothed error matrix, P, for time k +1 k|k-1 Is the error matrix from time k-1 to time k.
8. A hybrid positioning system based on an extended Kalman and R-T-S smoothing algorithm, comprising:
the position estimation module is used for acquiring estimation of the position of the robot at each moment and judging the position change conditions of the robot in the X direction and the Y direction;
and the position smoothing module is used for smoothing the position of the robot in the direction in which the position change does not exceed the set threshold value by utilizing an R-T-S smoothing algorithm, and averaging the smoothed positions to obtain the optimal position estimation of the robot in the direction.
9. A terminal device comprising a processor and a computer-readable storage medium, the processor being configured to implement instructions; a computer readable storage medium for storing a plurality of instructions adapted to be loaded by a processor and to perform the hybrid localization method based on extended Kalman and R-T-S smoothing algorithms of any one of claims 1 to 7.
10. A computer readable storage medium having stored therein a plurality of instructions, characterized in that said instructions are adapted to be loaded by a processor of a terminal device and to perform the hybrid positioning method based on an extended Kalman and R-T-S smoothing algorithm according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110784429.4A CN115615433A (en) | 2021-07-12 | 2021-07-12 | Hybrid positioning method and system based on extended Kalman and R-T-S smoothing algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110784429.4A CN115615433A (en) | 2021-07-12 | 2021-07-12 | Hybrid positioning method and system based on extended Kalman and R-T-S smoothing algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115615433A true CN115615433A (en) | 2023-01-17 |
Family
ID=84855693
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110784429.4A Pending CN115615433A (en) | 2021-07-12 | 2021-07-12 | Hybrid positioning method and system based on extended Kalman and R-T-S smoothing algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115615433A (en) |
-
2021
- 2021-07-12 CN CN202110784429.4A patent/CN115615433A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105509739B (en) | Using fixed interval CRTS smooth INS/UWB tight integrations navigation system and method | |
| CN105928518B (en) | Using the indoor pedestrian UWB/INS tight integrations navigation system and method for pseudorange and location information | |
| CN109141413B (en) | EFIR filtering algorithm and system with data missing UWB pedestrian positioning | |
| CN107255795B (en) | Indoor mobile robot positioning method and device based on EKF/EFIR hybrid filtering | |
| CN110930495A (en) | Multi-unmanned aerial vehicle cooperation-based ICP point cloud map fusion method, system, device and storage medium | |
| CN106871893A (en) | Distributed INS/UWB tight integrations navigation system and method | |
| CN113189541B (en) | Positioning method, device and equipment | |
| CN109141412B (en) | UFIR filtering algorithm and system for data-missing INS/UWB combined pedestrian navigation | |
| CN114440880B (en) | Construction site control point positioning method and system based on self-adaptive iterative EKF | |
| CN108759825B (en) | Self-adaptive pre-estimation Kalman filtering algorithm and system for INS/UWB pedestrian navigation with data missing | |
| CN109379711A (en) | A kind of localization method | |
| CN109269498B (en) | Adaptive pre-estimation EKF filtering algorithm and system for UWB pedestrian navigation with data missing | |
| CN109655060B (en) | INS/UWB integrated navigation algorithm and system based on KF/FIR and LS-SVM fusion | |
| CN111678513A (en) | Ultra-wideband/inertial navigation tight coupling indoor positioning device and system | |
| CN111578939B (en) | Robot tight combination navigation method and system considering random variation of sampling period | |
| WO2024120187A1 (en) | Method for estimating dynamic target of unmanned aerial vehicle in information rejection environment | |
| CN110879069A (en) | UWB SLAM-oriented Kalman/R-T-S hybrid positioning method and system | |
| CN109916401B (en) | Distributed seamless tight combination navigation method and system adopting LS-SVM assisted EKF filtering method | |
| CN115615433A (en) | Hybrid positioning method and system based on extended Kalman and R-T-S smoothing algorithm | |
| CN111444467A (en) | Method for local linear interpolation and prediction based on real-time positioning track data | |
| CN113218388B (en) | Mobile robot positioning method and system considering variable colored measurement noise | |
| CN111123323B (en) | Method for improving positioning precision of portable equipment | |
| CN113470342B (en) | Method and device for estimating self-movement | |
| Zhao et al. | Two-Stage Weighted Filtering Algorithm for Seamless Positioning in Indoor-Outdoor Crossover Areas | |
| CN118225091A (en) | KF/EM-KF/FIR hybrid filtering method and system for indoor pedestrian navigation |
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 |