CN117232518A - Factor graph model-based positioning method and device, electronic equipment and medium - Google Patents
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Abstract
The disclosure provides a positioning method and related equipment based on a factor graph model, and relates to the technical field of positioning. The method comprises the following steps: measuring the position information and the angular velocity information of the object to be positioned at a plurality of moments through an inertial navigation sensor, and measuring the distance information of the object to be positioned from the plurality of moments to a ranging base station through the ranging base station; constructing a factor graph model containing a base station error factor and a time error factor according to measurement data of an object to be positioned at a plurality of moments; optimizing the factor graph model based on the loss function of the limiting error, and determining the position information of the object to be positioned at each moment according to the optimized factor graph model. The method and the device are based on the factor graph model to fuse the measurement data of the inertial navigation sensor and the ranging base station, and can combine the measurement data of all the time of the history to perform positioning; and the factor graph model is optimized based on a loss function of the limiting error, so that the influence of the ranging error of the base station and the time accumulated error on the positioning result can be reduced.
Description
Technical Field
The disclosure relates to the field of positioning technologies, and in particular, to a positioning method, a device, an electronic device and a medium based on a factor graph model.
Background
Location-based services have gradually penetrated aspects of human life as a lifestyle. Currently, in an outdoor environment, positioning navigation technology is mature based on a Global Positioning System (GPS) or a cellular mobile network. However, due to the fact that more shielding and barriers exist in the environment where the object to be positioned is located, signals of satellites or cellular networks are fragile, and therefore positioning cannot be achieved through GPS or cellular mobile network technology. Since people spend about 70% -90% of their time indoors on average, it is more desirable to implement location tracking techniques in indoor environments (e.g., store, ward, prison, office, garage, etc.).
The conventional positioning technology cannot accurately position, so that the acquisition of a method capable of accurately positioning is a technical problem to be solved.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The present disclosure provides a factor graph model-based positioning method, apparatus, electronic device, and medium, which overcome, at least to some extent, the problem of positioning inaccuracy due to the related art.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to one aspect of the present disclosure, there is provided a factor graph model-based positioning method including: acquiring first measurement data and second measurement data of an object to be positioned at a plurality of moments, wherein the first measurement data comprises: the inertial navigation sensor measures the position information and the angular velocity information of the object to be positioned, and the second measurement data comprises: distance information of the object to be positioned to the ranging base station measured by the ranging base station; constructing a factor graph model according to first measurement data and second measurement data of an object to be positioned at a plurality of moments; optimizing a factor graph model based on a loss function of a limiting error, and determining the position information of an object to be positioned at each moment according to the optimized factor graph model, wherein the factor graph model comprises: the system comprises a base station error factor and a time error factor, wherein the base station error factor is used for representing the ranging error of a ranging base station, and the time error factor is used for representing the time accumulated error of an inertial navigation sensor from a first moment to a second moment.
In some embodiments, the location information in the above method includes: the north coordinate position and the east coordinate position of the object to be positioned in the inertial navigation coordinate system.
In some embodiments, the ranging base station is an indoor base station.
In some embodiments, the indoor base station is an ultra wideband UWB base station.
In some embodiments, the positioning method based on the factor graph model provided above may determine the base station error factor by the following formula: e, e Bt,i ＝r t,i -norm(B i ,X t ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein r is t,i Represents the distance from the object to be positioned measured by the ith ranging base station to the ranging base station, B i Indicating the position information of the ith ranging base station, X t Representing the position information of the object to be positioned measured by the inertial navigation sensor at time t, a function norm (B i ,X t ) Representation B i And X t Euclidean distance between them.
In some embodiments, the first measurement data further comprises: the moving step length of the object to be positioned, which is measured by the inertial navigation sensor.
In some embodiments, the positioning method based on the factor graph model provided above determines the front-to-back time error factor by the following formula: e, e x,t ＝dis([X t ,X t-1 ],[S t ,Δωt]) The method comprises the steps of carrying out a first treatment on the surface of the Wherein [ X ] t ,X t-1 ]Displacement vector representing position information of object to be positioned measured by inertial navigation sensor at time t and time t-1, [ S ] t ,Δωt]Motion vector dis ([ X ] representing movement step length and angular velocity information of an object to be positioned measured by an inertial navigation sensor at time t t ,X t-1 ],[S t ,Δωt]) Representation calculation [ X ] t ,X t-1 ]And [ S ] t ,Δωt]Euclidean distance between them.
In some embodiments, the method further comprises establishing a maximum posterior probability model of the factor graph model based on the factor graph model; and solving the maximum posterior probability model to obtain the position information of the object to be positioned at each moment.
According to another aspect of the present disclosure, there is also provided a positioning device based on a factor graph model, including: the device comprises a measurement data acquisition module, a data processing module and a data processing module, wherein the measurement data acquisition module is used for acquiring first measurement data and second measurement data of an object to be positioned at a plurality of moments, and the first measurement data comprises: the inertial navigation sensor measures the position information and the angular velocity information of the object to be positioned, and the second measurement data comprises: distance information of the object to be positioned to the ranging base station measured by the ranging base station; the factor graph model construction module is used for constructing a factor graph model according to first measurement data and second measurement data of an object to be positioned at a plurality of moments; the position information determining module is used for optimizing the factor graph model based on the loss function of the limiting error, and determining the position information of the object to be positioned at each moment according to the optimized factor graph model, wherein the factor graph model comprises: the system comprises a base station error factor and a time error factor, wherein the base station error factor is used for representing the ranging error of a ranging base station, and the time error factor is used for representing the time accumulated error of an inertial navigation sensor from a first moment to a second moment.
According to another aspect of the present disclosure, there is also provided an electronic device including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the factor graph model based positioning method of any of the above via execution of the executable instructions.
According to another aspect of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the factor graph model-based positioning method of any one of the above.
According to another aspect of the present disclosure, there is also provided a computer program product comprising a computer program which, when executed by a processor, implements the factor graph model based positioning method of any of the above.
According to the positioning method, the positioning device, the electronic equipment and the medium based on the factor graph model, which are provided by the embodiment of the disclosure, the position information and the angular velocity information of an object to be positioned, which are measured by an inertial navigation sensor at a plurality of moments, are obtained to serve as first measurement data, the distance information of the object to be positioned, which is measured by a ranging base station, to the ranging base station is taken as second measurement data, the factor graph model is constructed according to the first measurement data and the second measurement data of the object to be positioned at the plurality of moments, the factor graph model is optimized based on a loss function of limiting errors, in the optimization process, the ranging error of the ranging base station is represented by taking the base station error factor into consideration, and the time accumulation error from the first moment to the second moment is represented by the time error factor. The optimized factor graph model may be obtained by adjusting the factors in the factor graph model to minimize the loss function. Through the optimized factor graph model, the position information of the object to be positioned at each moment can be determined, so that accurate positioning is realized. According to the positioning method based on the factor graph model, a plurality of historical measurement data and error factors can be comprehensively considered, so that positioning accuracy and robustness are improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 illustrates a schematic diagram of an application system architecture in an embodiment of the present disclosure;
FIG. 2 illustrates a schematic view of a suitable scenario in an embodiment of the present disclosure;
FIG. 3 illustrates a flow chart of a method of factor graph model-based positioning in an embodiment of the disclosure;
FIG. 4 illustrates a flow chart of yet another positioning method based on a factor graph model in an embodiment of the disclosure;
FIG. 5 illustrates a schematic diagram of a positioning device based on a factor graph model in an embodiment of the disclosure;
FIG. 6 shows a block diagram of an electronic device in an embodiment of the disclosure;
fig. 7 shows a schematic diagram of a computer-readable storage medium in an embodiment of the disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
The following detailed description of embodiments of the present disclosure refers to the accompanying drawings.
FIG. 1 illustrates an exemplary application system architecture diagram to which the factor graph model-based positioning method of embodiments of the present disclosure may be applied. As shown in fig. 1, the system architecture may include a terminal device 101, a network 102, and a server 103.
The medium used by the network 102 to provide a communication link between the terminal device 101 and the server 103 may be a wired network or a wireless network.
Alternatively, the wireless network or wired network described above uses standard communication techniques and/or protocols. The network is typically the Internet, but may be any network including, but not limited to, a local area network (Local Area Network, LAN), metropolitan area network (Metropolitan Area Network, MAN), wide area network (Wide Area Network, WAN), mobile, wired or wireless network, private network, or any combination of virtual private networks. In some embodiments, data exchanged over a network is represented using techniques and/or formats including HyperText Mark-up Language (HTML), extensible markup Language (Extensible MarkupLanguage, XML), and the like. All or some of the links may also be encrypted using conventional encryption techniques such as secure sockets layer (Secure Socket Layer, SSL), transport layer security (Transport Layer Security, TLS), virtual private network (Virtual Private Network, VPN), internet security protocol (Internet Protocol Security, IPSec), etc. In other embodiments, custom and/or dedicated data communication techniques may also be used in place of or in addition to the data communication techniques described above.
The terminal device 101 may be a variety of electronic devices including, but not limited to, smart phones, tablet computers, laptop portable computers, desktop computers, smart speakers, smart watches, wearable devices, augmented reality devices, virtual reality devices, and the like.
Alternatively, the clients of the applications installed in different terminal devices 101 are the same or clients of the same type of application based on different operating systems. The specific form of the application client may also be different based on the different terminal platforms, for example, the application client may be a mobile phone client, a PC client, etc.
The server 103 may be a server providing various services, such as a background management server providing support for devices operated by the user with the terminal apparatus 101. The background management server can analyze and process the received data such as the request and the like, and feed back the processing result to the terminal equipment.
Optionally, the server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs (Content Delivery Network, content delivery networks), basic cloud computing services such as big data and artificial intelligence platforms, and the like.
Those skilled in the art will appreciate that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative, and that any number of terminal devices, networks, and servers may be provided as desired. The embodiments of the present disclosure are not limited in this regard.
Under the system architecture described above, the embodiments of the present disclosure provide a positioning method based on a factor graph model, which may be performed by any electronic device with computing processing capability.
In some embodiments, the positioning method based on the factor graph model provided in the embodiments of the present disclosure may be performed by the terminal device of the system architecture described above; in other embodiments, the factor graph model-based positioning method provided in the embodiments of the present disclosure may be performed by a server in the system architecture described above; in other embodiments, the positioning method based on the factor graph model provided in the embodiments of the present disclosure may be implemented by the terminal device and the server in the system architecture in an interactive manner.
Fig. 2 illustrates a schematic view of a scenario suitable for the factor graph model-based positioning method according to the embodiments of the present disclosure. As shown in fig. 2, a plurality of ranging base stations 201 are deployed in an indoor space 20, and an object 202 to be positioned can hold any terminal device with an inertial navigation sensor, and when the object 202 to be positioned steps into the indoor space 20, accurate positioning of the object to be positioned can be achieved according to the positioning method based on the factor graph model provided in the embodiment of the present disclosure.
Fig. 3 shows a flow chart of a positioning method based on a factor graph model in an embodiment of the disclosure, and as shown in fig. 3, the positioning method based on the factor graph model provided in the embodiment of the disclosure includes the following steps:
s302, acquiring first measurement data and second measurement data of an object to be positioned at a plurality of moments, wherein the first measurement data comprises: the inertial navigation sensor measures the position information and the angular velocity information of the object to be positioned, and the second measurement data comprises: distance information of the object to be positioned to the ranging base station measured by the ranging base station.
It should be noted that, in the embodiment of the present disclosure, the object to be positioned may be any object that needs to be positioned, such as a mobile device, a vehicle, an unmanned aerial vehicle, an animal, a human, or the like. In some embodiments, the positioning method based on the factor graph model provided in the embodiments of the present disclosure is mainly used for positioning a movable body in an indoor space.
It should be further noted that an inertial navigation sensor is a sensor system for positioning and navigation, and generally includes a gyroscope and an accelerometer. The inertial navigation sensor provides position information and angular velocity information of an object to be positioned by measuring linear acceleration and angular velocity of the object to be positioned. The position information measured by inertial navigation sensors is typically calculated based on accelerometer data. The accelerometer measures acceleration of an object to be positioned on three coordinate axes, and displacement of the object relative to an initial position can be obtained through integral operation, so that position information is obtained. Angular velocity information is obtained by measuring the angular velocity of an object in three coordinate axes by means of a gyroscope. From the output of the gyroscope, the speed at which the object to be positioned rotates about the respective axes can be known, and the angular velocity information can be used to infer the direction and attitude of the object.
A ranging base station is a base station for measuring the distance of an object to be positioned to the base station, and typically employs wireless signaling or other ranging techniques. Ranging base stations may measure range information of an object to be positioned to the base station by characteristics such as time of signal propagation or signal strength. Specifically, the ranging base station transmits a signal, such as a wireless signal or an infrared signal, and receives a signal returned from the object to be positioned. Based on the propagation time difference of the signal or the attenuation characteristic of the signal, the ranging base station can calculate the distance information of the object to be positioned from the base station.
Based on the first measurement data including the position information and the angular velocity information of the object to be positioned measured by the inertial navigation sensor and the second measurement data including the distance from the object to be positioned to the ranging base station measured by the ranging base station, the approximate position information of the object to be positioned at a plurality of moments can be obtained.
S304, constructing a factor graph model according to the first measurement data and the second measurement data of the object to be positioned at a plurality of moments.
Constructing a factor graph model from first and second measurement data of an object to be positioned at a plurality of moments is a key step of the positioning method. It should be appreciated that the factor graph model is a graph structure that represents conditional dependencies between variables. Specifically, when constructing the factor graph model, multiple factors need to be determined, and in the embodiments of the present disclosure, the factors may include a base station error factor and a time error factor, where the base station error factor is used to represent a ranging error of the ranging base station, and the time error factor is used to represent a time accumulated error of the inertial navigation sensor from the first time to the second time. And obtaining a base station error factor and a time error factor according to the first measurement data and the second measurement data, and constructing a factor graph model based on the base station error factor and the time error factor. The factor graph model can integrate the first measurement data and the second measurement data at a plurality of moments.
S306, optimizing a factor graph model based on a loss function of a limiting error, and determining the position information of an object to be positioned at each moment according to the optimized factor graph model, wherein the factor graph model comprises: the system comprises a base station error factor and a time error factor, wherein the base station error factor is used for representing the ranging error of a ranging base station, and the time error factor is used for representing the time accumulated error of an inertial navigation sensor from a first moment to a second moment.
It should be noted that, the factor graph model that has been constructed needs to be optimized, and in the embodiment of the present disclosure, the factor graph model that has been constructed is optimized based on the loss function that limits the error. It should be explained that the loss function limiting the error is a function for measuring the difference between the position estimate of the object to be positioned and the actual measured data. The loss function limiting the error may determine parameters of the medium factors of the factor graph model to make the position estimate as close as possible to the true value. In the positioning problem, the first measurement data and the second measurement data often contain a certain error or noise. The loss function, which limits the errors, takes these errors into account, optimizes the factor graph model by minimizing the difference between the object position estimate to be located and the first and second measurement data, and results in a more accurate position estimate.
Specifically, the error-limiting loss function in the disclosed embodiments may take different forms, and may include mean square error and maximum likelihood estimates, among others. By using a loss function limiting errors, the difference between the position estimation of the object to be positioned and the actual measurement data can be minimized when the factor graph model is optimized, so that an optimal position estimation result is obtained. The loss function limiting the error may improve the position accuracy at a plurality of position moments of the object to be positioned.
In addition, the factor graph model includes a base station error factor and a time error factor, which are used for representing a ranging error and a time accumulated error in the positioning process. The base station error factor is used to model the effect of the ranging error of the ranging base station on the positioning result. In the positioning process, distance information between an object to be positioned and each base station is obtained through a ranging base station. However, in the actual ranging process, there is a certain error in the ranging result due to various factors such as signal propagation, multipath effect, noise, etc.
The time error factor is used to characterize the time-integrated error of the inertial navigation sensor from the first instant to the second instant. In inertial navigation systems, the position and heading change of an object to be positioned over a period of time is inferred by measuring information such as acceleration and angular velocity. However, there is some uncertainty and drift in the time calculation in the inertial navigation sensor, resulting in a time-integrated error from the first time to the second time.
By introducing a base station error factor and a time error factor into the factor graph model, optimizing the factor graph model based on a loss function of limiting errors, and determining the position information of the object to be positioned at each moment according to the optimized factor graph model, the influence of a ranging error and a time accumulated error on a positioning result can be better considered, and the accuracy and the stability of the position of the object to be positioned at each moment are improved.
According to the embodiment of the disclosure, the measurement data of the inertial navigation sensor and the ranging base station are fused based on the factor graph model, and the measurement data of all time of history can be combined for positioning; and the factor graph model is optimized based on a loss function of the limiting error, so that the influence of the ranging error of the base station and the time accumulated error on the positioning result can be reduced.
It should be noted that, in the technical solution of the present disclosure, the acquiring, storing, using, processing, etc. of data all conform to relevant regulations of national laws and regulations, and various types of data such as personal identity data, operation data, behavior data, etc. relevant to individuals, clients, crowds, etc. acquired in the embodiments of the present disclosure have been authorized.
In some embodiments of the present disclosure, the loss function based on the constraint error is also used to perform loss correction on the base station error factor and the time error factor described above. The specific loss function can be expressed as follows:
L＝∑[w 1 ×||e 1 || 2 + w 2 ×||e2|| 2 ] (1)
Where L represents a loss function, Σ represents summing all errors, e 1 Error vector representing base station error factor, e 2 An error vector representing the time error factor, the term "two norms" (Euclidean distance), w 1 And w 2 Representing the corresponding weights.
The goal of the loss function is to minimize the error by adjusting the weight w 1 And w 2 The base station error factor and the time error factor can be adjusted to different degrees. In general, the effects of the two error factors are balanced by adjusting the weights to achieve a more accurate positioning result. The particular loss function form and weight selection will vary depending on the particular problem and application. The above formula provides only one example form, and in practical application, the definition and adjustment are required according to the requirements and the actual situation of the problem.
In other embodiments of the present disclosure, the loss function may also be according to the following formula:
wherein e is the sum of the base station error factor and the time error factor, delta is a parameter value, different values can be used according to actual conditions, the base station error factor and the time error factor are subjected to loss correction by using a formula (2) when |e| is less than or equal to delta, and the base station error factor and the time error factor are subjected to loss correction by using a formula (3) when |e| > delta.
In some embodiments of the present disclosure, the construction of the factor graph model may also specifically determine the variable nodes and the factor nodes of the factor graph model. Wherein the variable nodes represent the position information of the object to be positioned at different moments, for example, X1, X2, X3 and the like represent the positions of the object to be positioned at the first moment, the second moment, the third moment and the like. The factor nodes represent constraint or conditional relationships between the measurement data.
Specifically, the object position variable node to be positioned at each time, for example, X1, X2, X3, etc., is constructed from the first measurement data and the second measurement data. These variable nodes represent the position information of the object to be positioned at different moments. Constructing a factor node of the object to be positioned at each moment according to the first measurement data and the second measurement data, wherein the factor node specifically comprises: a base station error factor for characterizing a range error of the range base station and a time error factor for characterizing a time-integrated error of the inertial navigation sensor from a first time instant to a second time instant.
By establishing these nodes and factors and defining the relationships and conditions between them, a factor graph model can be formed. The factor graph model can comprehensively consider measurement data of a plurality of moments, so that accurate position results of the objects to be positioned at the plurality of moments are obtained.
In some embodiments of the present disclosure, the location information of the object to be located includes: the north coordinate position and the east coordinate position of the object to be positioned in the inertial navigation coordinate system.
It should be noted that the inertial navigation coordinate system is a coordinate system under the measurement result of the inertial navigation sensor, and is used for describing coordinate information of the object to be positioned in inertial navigation. In general, an inertial navigation system deduces the position and posture change of an object to be positioned by measuring information such as acceleration and angular velocity. To specifically represent the position information of the object to be positioned, the position of the object to be positioned in the inertial navigation coordinate system may be described using the north-coordinate position and the east-coordinate position.
Where the north-oriented coordinate position generally represents the position of the object to be positioned in the north direction of the inertial navigation coordinate system and the east-oriented coordinate position generally represents the position of the object to be positioned in the east direction of the inertial navigation coordinate system. The north coordinate position and the east coordinate position of the object to be positioned are used as the position information of the object to be positioned, the position information of the object to be positioned is used for constructing a factor graph model, and a more detailed space positioning result can be provided by considering the north coordinate position and the east coordinate position of the object to be positioned in the factor graph model and combining other measurement data.
In some embodiments of the present disclosure, the ranging base station appearing above is an indoor base station, and the indoor base station may be any base station installed indoors and capable of ranging. Specifically, a wireless communication base station is installed indoors to provide wireless communication coverage of an indoor area. Indoor base stations are commonly used to implement indoor positioning functions.
In the positioning method based on the factor graph model in the embodiment of the disclosure, the indoor base station is used as a ranging base station for acquiring distance information between an object to be positioned and the indoor base station as second measurement data. The distance information in these second measurement data may be used to establish a base station error factor to quantify the impact of the ranging error of the ranging base station on the positioning result.
By constructing a factor graph model from the distance information measured by the indoor base station, the position of the object to be positioned in the room can be inferred and optimized in the optimization process. The positioning method can realize more accurate position estimation in indoor environment and help to realize indoor positioning, navigation and other applications. The indoor base station can effectively solve the problem that GPS signals are weak or cannot be acquired in an indoor environment, and improves the reliability and accuracy of indoor positioning.
In some embodiments of the present disclosure, the indoor base station is an Ultra-Wideband (UWB) base station, and it should be noted that Ultra-Wideband is a Wideband communication technology that transmits data by transmitting very short pulses or pulse sequences. The UWB technology has higher bandwidth and fine time resolution, can provide accurate ranging and positioning capability, and is particularly suitable for positioning application in indoor environment.
In the positioning method based on the factor graph model, an ultra wideband UWB base station is used as a ranging base station for acquiring distance information between an object to be positioned and the UWB base station. UWB technology can provide highly accurate range measurements that work effectively even in complex indoor environments. This distance information will be used to establish a base station error factor to quantify the effect of the UWB base station's range error on the positioning results.
By constructing a factor graph model from the ranging data of the ultra wideband UWB base station, the position of the object to be positioned can be inferred and optimized in the optimization process. The positioning method based on the UWB technology can realize high-accuracy and high-accuracy indoor positioning, and is suitable for application scenes needing high-accuracy positioning, such as indoor navigation, intelligent building management and the like.
In some embodiments of the present disclosure, the base station error factor in the above may be determined by the following formula:
e Bt,i ＝ r t,i -norm(B i ,X t ) (4)
wherein r is t,i Represents the distance from the object to be positioned to the ranging base station measured by the ith ranging base station, B i Indicating the position information of the ith ranging base station, X t Representing the position information of the object to be positioned measured by the inertial navigation sensor at time t, a function norm (B i ,X t ) Representation B i And X t Euclidean distance between them.
It should be explained that the distance r measured by the ranging base station is calculated by the formula (4) t,i And ranging base station position B i Inertial navigation sensor measurement position X t Euclidean distance difference therebetween to represent base station error factor e Bt,i . Euclidean distance is a common distance measurement that measures the linear distance between two points in euclidean space.
The introduction of the base station error factor may take into account the influence of the ranging error of the ranging base station on the multiple positioning results of the object to be positioned. The factor is introduced into a factor graph model, and the accuracy and stability of positioning can be improved by correlating with position variable nodes, quantifying and modeling the ranging error, and carrying out parameter estimation or position inference according to the ranging data in the optimization process.
In some embodiments of the present disclosure, the first measurement data further comprises: and the inertial navigation sensor measures the moving step length of the object to be positioned.
The moving step length refers to the moving distance of the object to be positioned in unit time, and can be obtained by measuring through an inertial navigation sensor. Taking the movement step as one of the first measurement data may provide additional information for the accuracy and reliability of the positioning of the object to be positioned.
In constructing the factor graph model, the movement step size may be added as a factor to the factor graph for characterizing the movement characteristics of the object to be positioned. By considering the moving step factor, the motion characteristics of the object to be positioned can be better modeled, and the accuracy of the positioning algorithm is improved.
In optimizing the factor graph model, the step size factor may be shifted to minimize the error function and adjust the variable values in the model. By comprehensively considering the position information, the angular velocity information and the movement step length information, the accuracy and the stability of the positioning result can be further improved.
In some embodiments of the present disclosure, the back-and-forth time error factor is determined by the following formula:
e x,t ＝ dis([X t ,X t-1 ],[S t ,Δωt]) (5)
wherein [ X ] t ,X t-1 ]Displacement vector representing position information of object to be positioned measured by inertial navigation sensor at time t and time t-1, [ S ] t ,Δωt]Motion vector dis ([ X ] representing movement step length and angular velocity information of an object to be positioned measured by an inertial navigation sensor at time t t ,X t-1 ],[S t ,Δωt]) Representation calculation [ X ] t ,X t-1 ]And [ S ] t ,Δωt]Euclidean distance between them.
Equation (5) is calculated by calculating the sum of time tDisplacement vector [ X ] measured by inertial navigation sensor at t-1 moment t ,X t-1 ]And motion vector S t ,Δωt]Euclidean distance between them to represent the front-to-back time error factor e x,t . The influence of measurement errors of an inertial navigation sensor and the change of the movement mode of the object to be positioned on the positioning result is considered by introducing the front-back time error factors. By incorporating this factor into the factor graph model, the accuracy and stability of the positioning can be improved.
In particular, the location information may also include north and east coordinates of the object to be located in embodiments of the present disclosure. The fore-aft time error factor may specifically include a north coordinate error factor and an east coordinate error factor. The north coordinate error factor may be determined by the following equation:
e Nt ＝(N t – N t-1 ) – S t cos(ωt) (6)
the north coordinate error factor may be determined by the following equation:
e Et ＝(E t – E t-1 ) – S t sin(ωt) (7)
wherein N is t For the north coordinate value of the object to be positioned at time t, N t-1 For the north coordinate value of the object to be positioned at time t-1, S t For the moving step length of the object to be positioned, ωt is the angular velocity of the object to be positioned, E t To be the east coordinate value of the object to be positioned at time t, et-1 is the east coordinate value of the object to be positioned at time t-1.
By using equations (6) and (7), the position error of the object to be positioned in the north and east directions can be calculated. The north and east coordinate error factors may be used as factors in a factor graph model, associated with other measurement data, for optimizing and inferring the position of the object to be located at various moments. The accuracy and stability of positioning are improved, and the position estimation of the object to be positioned is further optimized.
In some embodiments of the present disclosure, as shown in fig. 4, a positioning method based on a factor graph model according to an embodiment of the present disclosure further includes the following steps:
s402, based on the factor graph model, establishing a maximum posterior probability model of the factor graph model.
It will be appreciated that the constructed factor graph model is constructed based on the first measurement data measured by the inertial navigation sensor and the second measurement data measured by the ranging base station, the factor graph model describing the various factors of the object to be positioned. Based on the factor graph model, a maximum posterior probability model is established for deducing the position state of the object to be positioned.
S404, solving a maximum posterior probability model to obtain the position information of the object to be positioned at each moment.
The maximum posterior probability model is solved, and in particular, various inference algorithms, such as a belief propagation algorithm or a variance inference algorithm, can be used to solve the maximum posterior probability model. In other embodiments of the present disclosure, the maximum a posteriori probability model may also be solved by using a batch optimizer g2 o. g2o is an open-source c++ optimization library, specially used for solving nonlinear least squares problems, including maximum a posteriori probability estimation.
And obtaining the position state estimation of the object to be positioned at each moment by solving the maximum posterior probability model. Such position information may be represented as north coordinates, east coordinates or other suitable representation providing position information of the object to be positioned at different moments in time.
Through the steps, a maximum posterior probability model is established, and the position information of the object to be positioned at each moment is obtained by solving the maximum posterior probability model. Thus, accurate estimation and tracking of the position of the object to be positioned can be realized.
Based on the same inventive concept, a positioning device based on a factor graph model is also provided in the embodiments of the disclosure, as described in the following embodiments. Since the principle of solving the problem of the embodiment of the device is similar to that of the embodiment of the method, the implementation of the embodiment of the device can be referred to the implementation of the embodiment of the method, and the repetition is omitted.
Fig. 5 shows a schematic diagram of a positioning device based on a factor graph model in an embodiment of the disclosure, as shown in fig. 5, the device 50 includes:
a measurement data acquisition module 501, configured to acquire first measurement data and second measurement data of an object to be positioned at a plurality of moments, where the first measurement data includes: the inertial navigation sensor measures the position information and the angular velocity information of the object to be positioned, and the second measurement data comprises: distance information of the object to be positioned to the ranging base station measured by the ranging base station;
the factor graph model construction module 502 constructs a factor graph model according to the first measurement data and the second measurement data of the object to be positioned at a plurality of moments;
the location information determining module 503 is configured to optimize the factor graph model based on the loss function of the limiting error, and determine location information of the object to be located at each moment according to the optimized factor graph model, where the factor graph model includes: the system comprises a base station error factor and a time error factor, wherein the base station error factor is used for representing the ranging error of a ranging base station, and the time error factor is used for representing the time accumulated error of an inertial navigation sensor from a first moment to a second moment.
It should be noted that the above modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to what is disclosed in the above method embodiments. It should be noted that the modules described above may be implemented as part of an apparatus in a computer system, such as a set of computer-executable instructions.
Those skilled in the art will appreciate that the various aspects of the present disclosure may be implemented as a system, method, or program product. Accordingly, various aspects of the disclosure may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
An electronic device 600 according to such an embodiment of the present disclosure is described below with reference to fig. 6. The electronic device 600 shown in fig. 6 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 6, the electronic device 600 is in the form of a general purpose computing device. Components of electronic device 600 may include, but are not limited to: the at least one processing unit 610, the at least one memory unit 620, and a bus 630 that connects the various system components, including the memory unit 620 and the processing unit 610.
Wherein the storage unit stores program code that is executable by the processing unit 610 such that the processing unit 610 performs steps according to various exemplary embodiments of the present disclosure described in the above section of the "exemplary method" of the present specification. For example, the processing unit 610 may perform the following steps of the method embodiment described above: acquiring first measurement data and second measurement data of an object to be positioned at a plurality of moments, wherein the first measurement data comprises: the inertial navigation sensor measures the position information and the angular velocity information of the object to be positioned, and the second measurement data comprises: distance information of the object to be positioned to the ranging base station measured by the ranging base station;
constructing a factor graph model according to first measurement data and second measurement data of an object to be positioned at a plurality of moments;
optimizing a factor graph model based on a loss function of a limiting error, and determining the position information of an object to be positioned at each moment according to the optimized factor graph model, wherein the factor graph model comprises: the system comprises a base station error factor and a time error factor, wherein the base station error factor is used for representing the ranging error of a ranging base station, and the time error factor is used for representing the time accumulated error of an inertial navigation sensor from a first moment to a second moment.
The storage unit 620 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 6201 and/or cache memory unit 6202, and may further include Read Only Memory (ROM) 6203.
The storage unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 630 may be a local bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or using any of a variety of bus architectures.
The electronic device 600 may also communicate with one or more external devices 640 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and/or any device (e.g., router, modem, etc.) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 650. Also, electronic device 600 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 660. As shown, network adapter 660 communicates with other modules of electronic device 600 over bus 630. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 600, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In particular, according to embodiments of the present disclosure, the process described above with reference to the flowcharts may be implemented as a computer program product comprising: and a computer program which, when executed by the processor, implements the factor graph model-based positioning method described above.
In an exemplary embodiment of the present disclosure, a computer-readable storage medium, which may be a readable signal medium or a readable storage medium, is also provided. Fig. 7 illustrates a schematic diagram of a computer-readable storage medium in an embodiment of the present disclosure, as shown in fig. 7, on which a program product capable of implementing the method of the present disclosure is stored 700. In some possible implementations, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the disclosure as described in the "exemplary methods" section of this specification, when the program product is run on the terminal device.
More specific examples of the computer readable storage medium in the present disclosure may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In this disclosure, a computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Alternatively, the program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
In particular implementations, the program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order or that all illustrated steps be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
From the description of the above embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (11)
1. A method of factor graph model-based positioning, comprising:
acquiring first measurement data and second measurement data of an object to be positioned at a plurality of moments, wherein the first measurement data comprises: the position information and the angular velocity information of the object to be positioned measured by the inertial navigation sensor, and the second measurement data comprise: distance information of the object to be positioned to the ranging base station measured by the ranging base station;
constructing a factor graph model according to the first measurement data and the second measurement data of the object to be positioned at a plurality of moments;
optimizing the factor graph model based on a loss function of a limiting error, and determining the position information of the object to be positioned at each moment according to the optimized factor graph model, wherein the factor graph model comprises: a base station error factor for characterizing a range error of the range base station and a time error factor for characterizing a time accumulated error of the inertial navigation sensor from a first time instant to a second time instant.
2. The factor graph model-based positioning method according to claim 1, wherein the position information of the object to be positioned includes: the north coordinate position and the east coordinate position of the object to be positioned in the inertial navigation coordinate system.
3. The factor graph model-based positioning method of claim 1, wherein the ranging base station is an indoor base station.
4. The factor graph model based positioning method of claim 3, wherein the indoor base station is an ultra wideband UWB base station.
5. The factor graph model based positioning method of claim 1, wherein the base station error factor is determined by the following formula:
e Bt,i ＝r t,i -norm(B i ,X t )
wherein r is t,i Represents the distance from the object to be positioned to the ranging base station measured by the ith ranging base station, B i Indicating the position information of the ith ranging base station, X t Representing the position information of the object to be positioned measured by the inertial navigation sensor at time t, a function norm (B i ,X t ) Representation B i And X t Euclidean distance between them.
6. The factor graph model-based positioning method of claim 1, wherein the first measurement data further comprises: and the inertial navigation sensor measures the moving step length of the object to be positioned.
7. The factor graph model based positioning method of claim 6, wherein the back-and-forth time error factor is determined by the following formula:
e x,t ＝dis([X t ,X t-1 ],[S t ,Δωt])
wherein [ X ] t ,X t-1 ]Representing displacement vector composed of position information of the object to be positioned measured by inertial navigation sensor at time t and time t-1S t ,Δωt]Motion vector dis ([ X ] representing the motion step length and angular velocity information of the object to be positioned measured by the inertial navigation sensor at time t t ,X t-1 ],[S t ,Δωt]) Representation calculation [ X ] t ,X t-1 ]And [ S ] t ,Δωt]Euclidean distance between them.
8. The factor graph model-based positioning method of claim 1, further comprising:
establishing a maximum posterior probability model of the factor graph model based on the factor graph model;
and solving the maximum posterior probability model to obtain the position information of the object to be positioned at each moment.
9. A factor graph model-based positioning device, comprising:
the device comprises a measurement data acquisition module, a data processing module and a data processing module, wherein the measurement data acquisition module is used for acquiring first measurement data and second measurement data of an object to be positioned at a plurality of moments, and the first measurement data comprises: the position information and the angular velocity information of the object to be positioned measured by the inertial navigation sensor, and the second measurement data comprise: distance information of the object to be positioned to the ranging base station measured by the ranging base station;
the factor graph model construction module is used for constructing a factor graph model according to the first measurement data and the second measurement data of the object to be positioned at a plurality of moments;
The position information determining module is used for optimizing the factor graph model based on a loss function limiting errors, and determining the position information of the object to be positioned at each moment according to the optimized factor graph model, wherein the factor graph model comprises: a base station error factor for characterizing a range error of the range base station and a time error factor for characterizing a time accumulated error of the inertial navigation sensor from a first time instant to a second time instant.
10. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the factor graph model based positioning method of any of claims 1-8 via execution of the executable instructions.
11. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the factor graph model-based positioning method of any of claims 1-8.
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| CN117872273B (en) * | 2024-03-11 | 2024-05-31 | 厦门市盛迅信息技术股份有限公司 | Multi-environment sound field sound ray identification method and system based on artificial intelligence |
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